From 2ffeb0b224de635571d114146d9ecce584c4f397 Mon Sep 17 00:00:00 2001 From: default Date: Tue, 14 Jul 2026 03:18:29 +0000 Subject: [PATCH 01/24] 1. Create the BAA10Y_forecasting folder based on the SP500 forecasting template. 2. Update data.py to use BAA10Y as the forecasting target. --- .env.example | 2 - .../01_sp500_multivariate_backtest.ipynb | 1609 +++++++++++++++++ .../99_starter_agent.ipynb | 239 +++ implementations/BAA10Y_forecasting /README.md | 242 +++ .../BAA10Y_forecasting /__init__.py | 58 + .../BAA10Y_forecasting /analysis.py | 161 ++ implementations/BAA10Y_forecasting /data.py | 769 ++++++++ .../BAA10Y_forecasting /leaderboard.py | 205 +++ implementations/BAA10Y_forecasting /plots.py | 238 +++ .../predictors/__init__.py | 19 + .../predictors/llmp_sampled_trajectory.py | 111 ++ .../specs/sp500_backtest_2025.yaml | 48 + .../specs/sp500_eval_2026.yaml | 51 + .../specs/sp500_smoke.yaml | 53 + .../specs/sp500_stress_2020.yaml | 50 + .../starter_agent/__init__.py | 19 + .../starter_agent/agent.py | 301 +++ .../skills/code-analysis-playbook/SKILL.md | 53 + .../starter_agent/skills/forecasting/SKILL.md | 53 + .../skills/research-playbook/SKILL.md | 47 + 20 files changed, 4326 insertions(+), 2 deletions(-) delete mode 100644 .env.example create mode 100644 implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb create mode 100644 implementations/BAA10Y_forecasting /99_starter_agent.ipynb create mode 100644 implementations/BAA10Y_forecasting /README.md create mode 100644 implementations/BAA10Y_forecasting /__init__.py create mode 100644 implementations/BAA10Y_forecasting /analysis.py create mode 100644 implementations/BAA10Y_forecasting /data.py create mode 100644 implementations/BAA10Y_forecasting /leaderboard.py create mode 100644 implementations/BAA10Y_forecasting /plots.py create mode 100644 implementations/BAA10Y_forecasting /predictors/__init__.py create mode 100644 implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py create mode 100644 implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml create mode 100644 implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml create mode 100644 implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml create mode 100644 implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml create mode 100644 implementations/BAA10Y_forecasting /starter_agent/__init__.py create mode 100644 implementations/BAA10Y_forecasting /starter_agent/agent.py create mode 100644 implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md create mode 100644 implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md create mode 100644 implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md diff --git a/.env.example b/.env.example deleted file mode 100644 index 51874a61..00000000 --- a/.env.example +++ /dev/null @@ -1,2 +0,0 @@ -# Optional keys -FRED_API_KEY=your_fred_api_key # You need to request this from FRED if you want to use it diff --git a/implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb new file mode 100644 index 00000000..40e948b0 --- /dev/null +++ b/implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb @@ -0,0 +1,1609 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "52b6fbe6", + "metadata": {}, + "source": [ + "# S&P 500 — multivariate conventional-methods comparison\n", + "\n", + "The repo's **financial-markets** reference: a head-to-head of conventional\n", + "time-series methods on a daily equity index, all reading the **same leak-safe\n", + "covariate panel** (VIX, Treasury yields, the 2s10s spread, fed funds, CPI,\n", + "unemployment, oil, gold, the dollar index, NASDAQ), plus an LLM-Process\n", + "forecaster that can read those covariates in its prompt.\n", + "\n", + "> Given the same macro/market observations, **which method forecasts the index\n", + "> best — and can an LLM-Process, handed those covariates, keep up with gradient\n", + "> boosting?**\n", + "\n", + "Unlike the energy/oil reference (univariate price trajectory forecast by\n", + "news-grounded / code-executing / adaptive **agents**), this is a clean,\n", + "reproducible **numerical-methods bake-off across a covariate panel** — no agents,\n", + "no news — scored with CRPS and direction metrics.\n", + "\n", + "**Methods**\n", + "\n", + "| Family | Predictors | Covariates? |\n", + "|---|---|---|\n", + "| Naive floor | `LastValuePredictor` | — |\n", + "| Classical | ETS, Kalman, AutoARIMA | — (univariate) |\n", + "| ML regression | Darts LinearRegression, LightGBM | ✅ optional past covariates |\n", + "| LLM-Process | `SampledTrajectoryLLMPredictor` | ✅ optional covariate prompt blocks |\n", + "\n", + "**How it's organised.** Specs (`specs/*.yaml`) carry only the **experiment\n", + "design** — the window and one single-horizon task per `sp500_logret_{N}b` target.\n", + "The **predictors and their hyperparameters live in code** (the predictors cell in\n", + "Section 4), so you tune models by editing Python, not YAML.\n", + "\n", + "**Workflow:** iterate on the **2025 backtest** (Section 5) by setting\n", + "`EXPERIMENT_CONFIG` in the config cell, then spend the **protected 2026 eval**\n", + "(Section 7) on your finalists. `\"smoke\"` is the fast default." + ] + }, + { + "cell_type": "markdown", + "id": "20748993", + "metadata": {}, + "source": [ + "## What's actually forecastable at daily resolution?\n", + "\n", + "- **Target = returns, not the index level.** We forecast **close-to-close\n", + " cumulative log returns** over a few horizons, one series per window:\n", + " `sp500_logret_1b` (next session), `sp500_logret_5b` (forward 1 week),\n", + " `sp500_logret_21b` (forward 1 month). Forecasting `sp500_logret_Nb` exactly `N`\n", + " business days ahead resolves to the **forward** cumulative return over the next\n", + " `N` sessions.\n", + "- **Per horizon:** `h=1` → direction / next-day **risk management**; `h=5`/`h=21`\n", + " → tactical rebalancing and option tenors as cumulative returns.\n", + "- **The caveat:** for an index the return *level* is close to a martingale, so\n", + " far-ahead point forecasts trend toward ~0; the forecastable, actionable objects\n", + " are **volatility, tail risk, and direction**. That's why a VIX-led panel helps\n", + " most at short horizons — watch the edge shrink as the horizon grows." + ] + }, + { + "cell_type": "markdown", + "id": "4c6b6eb8", + "metadata": {}, + "source": [ + "## ⚠️ Cutoff-aware evaluation — why the windows are what they are\n", + "\n", + "This is the methodological heart of the comparison, and it's easy to get wrong.\n", + "\n", + "- **Numerical methods are cutoff-safe by construction.** Naive, ETS, Kalman,\n", + " AutoARIMA, LinReg and LightGBM only ever see the series up to the forecast\n", + " origin (`ForecastContext` enforces it). They can be backtested on *any*\n", + " historical window — including the 2020 COVID crash.\n", + "- **An LLM is not.** Gemini's training cutoff is ~**January 2025**, so it has\n", + " effectively **memorised** outcomes before then. Scoring an LLM-Process on a\n", + " pre-2025 origin measures *recall*, not forecasting — and silently flatters it\n", + " in the head-to-head, which is exactly the comparison this notebook is about.\n", + "\n", + "So the LLM-inclusive comparison lives **after the cutoff**:\n", + "\n", + "| Window | Spec | Role | LLMP? |\n", + "|---|---|---|---|\n", + "| **2025** | `sp500_smoke.yaml` / `sp500_backtest_2025.yaml` | open iteration & comparison | ✅ post-cutoff |\n", + "| **2026** | `sp500_eval_2026.yaml` | **protected** held-out test (budgeted) | ✅ post-cutoff |\n", + "| **2020 COVID** | `sp500_stress_2020.yaml` | volatile-regime stress, **numerical only** | ❌ leaked for LLMs |\n", + "\n", + "This discipline is enforced **in code**: the predictors cell (Section 4) gates the\n", + "LLM-Process rows on a `POST_CUTOFF` flag, so selecting `EXPERIMENT_CONFIG =\n", + "\"stress_2020\"` drops them automatically — the numerical methods remain perfectly\n", + "valid on that volatile window." + ] + }, + { + "cell_type": "markdown", + "id": "823a7363", + "metadata": {}, + "source": [ + "---\n", + "## 1. Setup\n", + "\n", + "The heavy lifting lives in helper modules alongside this notebook:\n", + "\n", + "- `data.py` builds the `DataService` (return targets + the covariate panel).\n", + "- `predictors/` the S&P 500 LLM-Process recipe (prompt framing + sampling budget).\n", + "- `leaderboard.py` turns cached results into the `RESULTS_DF` leaderboard frame.\n", + "- `analysis.py` / `plots.py` direction metrics, styled tables, and figures.\n", + "\n", + "We build **one** data service that registers the three return targets plus the\n", + "full covariate panel. Target-only predictors simply ignore the registered\n", + "covariates; the covariate variants read them.\n", + "\n", + "**First run on the 2025/2026 windows?** Warm the caches to the present first:\n", + "`uv run python scripts/fetch_sp500_market.py --refresh` (Yahoo: ^GSPC/^VIX/^IXIC)\n", + "and `uv run python scripts/fetch_fred.py` (macro covariates)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "be2f6012", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "targets: ['sp500_logret_1b', 'sp500_logret_21b', 'sp500_logret_5b']\n", + "covariates registered: 10 / 11 requested\n" + ] + } + ], + "source": [ + "from __future__ import annotations\n", + "\n", + "import warnings\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import yaml\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "\n", + "# Resolve the repo root robustly (walk up for the workspace markers) so the\n", + "# proxy creds load regardless of the kernel's working directory.\n", + "def _repo_root() -> Path:\n", + " here = Path.cwd().resolve()\n", + " for cand in (here, *here.parents):\n", + " if (cand / \"pyproject.toml\").exists() and (cand / \"aieng-forecasting\").is_dir():\n", + " return cand\n", + " return here\n", + "\n", + "\n", + "ROOT = _repo_root()\n", + "load_dotenv(ROOT / \".env\", override=False) # LLMP rows call the Vector proxy — need PROXY_* set\n", + "\n", + "from aieng.forecasting.evaluation import (\n", + " MultiTargetBacktestSpec,\n", + " MultiTargetEvalSpec,\n", + " cached_multi_backtest,\n", + " describe_spec,\n", + " multi_evaluate,\n", + ")\n", + "from aieng.forecasting.methods import (\n", + " DartsAutoARIMAPredictor,\n", + " DartsExponentialSmoothingPredictor,\n", + " DartsKalmanForecasterPredictor,\n", + " DartsLightGBMPredictor,\n", + " DartsLinearRegressionPredictor,\n", + " LastValuePredictor,\n", + ")\n", + "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", + "from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame\n", + "from sp500_forecasting.plots import (\n", + " display_multivariate_backtest_leaderboard,\n", + " plot_return_forecast_vs_actual_multi,\n", + " plot_sp500_log_return_recent,\n", + ")\n", + "from sp500_forecasting.predictors import build_sp500_llmp_sampled_trajectory\n", + "\n", + "\n", + "SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", + "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n", + "\n", + "# Data environment (not part of any single experiment): how far back to load\n", + "# price/covariate history, and whether to re-fetch caches from source.\n", + "DATA_HISTORY_START = \"2016-01-01\"\n", + "REFRESH_CACHE = False\n", + "\n", + "svc = build_sp500_multivariate_service(\n", + " windows=(1, 5, 21),\n", + " include_covariates=True,\n", + " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", + " start=DATA_HISTORY_START,\n", + " refresh=REFRESH_CACHE,\n", + ")\n", + "\n", + "# The covariate panel available to the with-covariates predictors below (an\n", + "# optional FRED feed may be skipped, so filter to what actually registered).\n", + "registered = set(svc.series_ids)\n", + "COVARIATES = [c for c in DEFAULT_COVARIATE_SERIES_IDS if c in registered]\n", + "print(\"targets:\", sorted(s for s in registered if s.startswith(\"sp500_logret_\")))\n", + "print(f\"covariates registered: {len(COVARIATES)} / {len(DEFAULT_COVARIATE_SERIES_IDS)} requested\")" + ] + }, + { + "cell_type": "markdown", + "id": "20fa857f", + "metadata": {}, + "source": [ + "---\n", + "## 2. Configuration\n", + "\n", + "One selector picks the backtest window; everything downstream adapts. Specs are\n", + "the source of truth for the **window and tasks** — not the predictors." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d8d4a643", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config: 'smoke' → sp500_smoke.yaml (LLMP on)\n", + "MultiTargetBacktestSpec (spec_id=sp500_smoke)\n", + " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days.\n", + " start: 2025-10-06 00:00:00\n", + " end: 2025-11-14 00:00:00\n", + " stride: 5\n", + " warmup: 250\n", + " tasks: 3\n", + "\n", + "Task: sp500_logret_1b\n", + " description: S&P 500 close-to-close cumulative log return, 1 business day ahead (next-session return / direction).\n", + " horizons: 1\n", + " frequency: B\n", + " payload: continuous\n", + " resolution: observed_value_at_resolution_timestamp\n", + "- target_series_id: sp500_logret_1b\n", + " description: S&P 500 close-to-close cumulative log return over 1 business day(s) (next-session) (Yahoo Finance ^GSPC, derived)\n", + " source: Yahoo Finance (^GSPC), derived\n", + " units: log-return\n", + " frequency: B\n", + "\n", + "Task: sp500_logret_5b\n", + " description: S&P 500 close-to-close cumulative log return, 5 business days ahead (forward 1-week return).\n", + " horizons: 5\n", + " frequency: B\n", + " payload: continuous\n", + " resolution: observed_value_at_resolution_timestamp\n", + "- target_series_id: sp500_logret_5b\n", + " description: S&P 500 close-to-close cumulative log return over 5 business day(s) (forward 1-week (5 business days)) (Yahoo Finance ^GSPC, derived)\n", + " source: Yahoo Finance (^GSPC), derived\n", + " units: log-return\n", + " frequency: B\n", + "\n", + "Task: sp500_logret_21b\n", + " description: S&P 500 close-to-close cumulative log return, 21 business days ahead (forward 1-month return).\n", + " horizons: 21\n", + " frequency: B\n", + " payload: continuous\n", + " resolution: observed_value_at_resolution_timestamp\n", + "- target_series_id: sp500_logret_21b\n", + " description: S&P 500 close-to-close cumulative log return over 21 business day(s) (forward 1-month (21 business days)) (Yahoo Finance ^GSPC, derived)\n", + " source: Yahoo Finance (^GSPC), derived\n", + " units: log-return\n", + " frequency: B\n", + "\n" + ] + } + ], + "source": [ + "# ── Experiment configuration ──────────────────────────────────────────────────\n", + "# EXPERIMENT_CONFIG chooses which window drives the backtest (Section 5); all\n", + "# downstream cells adapt. The protected 2026 eval (Section 7) is fixed.\n", + "#\n", + "# \"smoke\" short late-2025 window (~6 weekly origins), post-cutoff.\n", + "# Fast default; LLM-Process predictors ON.\n", + "# \"backtest_2025\" weekly origins across all of 2025 (~50), post-cutoff.\n", + "# The full comparison; LLMP is slow here — trim the roster.\n", + "# \"stress_2020\" the COVID crash (daily origins Feb–Apr 2020). NUMERICAL\n", + "# ONLY — pre-cutoff is leaked for LLMs, so the predictors cell\n", + "# drops the LLMP rows for this config.\n", + "\n", + "EXPERIMENT_CONFIG = \"smoke\"\n", + "\n", + "_BACKTEST_SPEC_FILES = {\n", + " \"smoke\": \"sp500_smoke.yaml\",\n", + " \"backtest_2025\": \"sp500_backtest_2025.yaml\",\n", + " \"stress_2020\": \"sp500_stress_2020.yaml\",\n", + "}\n", + "_EVAL_SPEC_FILE = \"sp500_eval_2026.yaml\"\n", + "\n", + "# 2020 is pre-cutoff → keep the LLM-Process out of the comparison (see Section 0).\n", + "POST_CUTOFF = EXPERIMENT_CONFIG in {\"smoke\", \"backtest_2025\"}\n", + "\n", + "with (SPECS_DIR / _BACKTEST_SPEC_FILES[EXPERIMENT_CONFIG]).open() as f:\n", + " backtest_spec = MultiTargetBacktestSpec.model_validate(yaml.safe_load(f))\n", + "\n", + "print(\n", + " f\"Config: {EXPERIMENT_CONFIG!r} → {_BACKTEST_SPEC_FILES[EXPERIMENT_CONFIG]} \"\n", + " f\"(LLMP {'on' if POST_CUTOFF else 'OFF — pre-cutoff'})\"\n", + ")\n", + "print(describe_spec(backtest_spec, data_service=svc))" + ] + }, + { + "cell_type": "markdown", + "id": "22f36cca", + "metadata": {}, + "source": [ + "---\n", + "## 3. Target context — observed returns\n", + "\n", + "The most recent realised next-session returns (the `h=1` target). Blue = index up\n", + "over the window, red = down." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c2e433fb", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, _ = plot_sp500_log_return_recent(svc, n_trading_days=504)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "88f4e0aa", + "metadata": {}, + "source": [ + "---\n", + "## 4. Predictors — configured in code\n", + "\n", + "This is where you choose the roster. Each predictor implements the same\n", + "`Predictor` API against the loaded spec; the conventional methods come straight\n", + "from `aieng.forecasting.methods`, and the LLM-Process variants come from the\n", + "S&P 500 recipe in `predictors/`. The covariate variants read the registered\n", + "`COVARIATES` panel; the target-only variants pass `covariate_series_ids=None`.\n", + "\n", + "| Group | Predictor | Covariates? |\n", + "|---|---|---|\n", + "| Naive floor | `LastValuePredictor` | — |\n", + "| Classical | `DartsExponentialSmoothingPredictor` (ETS), `DartsKalmanForecasterPredictor` | — |\n", + "| ML regression | `DartsLinearRegressionPredictor`, `DartsLightGBMPredictor` | target-only **and** + panel |\n", + "| LLM-Process | `build_sp500_llmp_sampled_trajectory` | target-only **and** + panel (post-cutoff only) |\n", + "\n", + "`AutoARIMA` is left commented (accurate but slow); add it back for a classical\n", + "sweep. The `LLMP (target)` vs `LLMP + cov` pair is the centerpiece — its CRPS gap\n", + "answers whether an LLM can use the same exogenous panel the ML methods do." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2e1da155", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 predictors configured (LLMP on):\n", + " last_value_naive\n", + " darts_ets\n", + " darts_kalman\n", + " darts_autoarima\n", + " darts_linreg\n", + " darts_linreg_cov\n", + " darts_lightgbm\n", + " darts_lightgbm_cov\n", + " llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]\n", + " llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]\n" + ] + } + ], + "source": [ + "# Shared hyperparameters for the Darts regression models.\n", + "LAGS = 5 # autoregressive lags (and past-covariate lags) for the regression models\n", + "NUM_SAMPLES = 100 # empirical-quantile sample count for the probabilistic Darts models\n", + "LGBM_KWARGS = {\"num_threads\": 1, \"n_jobs\": 1, \"verbosity\": -1}\n", + "\n", + "# ── Naive floor + classical (univariate) ──────────────────────────────────────\n", + "naive = LastValuePredictor()\n", + "ets = DartsExponentialSmoothingPredictor(num_samples=NUM_SAMPLES)\n", + "kalman = DartsKalmanForecasterPredictor(num_samples=NUM_SAMPLES)\n", + "autoarima = DartsAutoARIMAPredictor(num_samples=NUM_SAMPLES) # accurate but the slowest classical method\n", + "\n", + "# ── ML regression — target-only vs the covariate panel ────────────────────────\n", + "linreg = DartsLinearRegressionPredictor(lags=LAGS, covariate_series_ids=None, num_samples=NUM_SAMPLES)\n", + "linreg_cov = DartsLinearRegressionPredictor(\n", + " lags=LAGS, lags_past_covariates=LAGS, covariate_series_ids=COVARIATES, num_samples=NUM_SAMPLES\n", + ")\n", + "lightgbm = DartsLightGBMPredictor(\n", + " lags=LAGS, covariate_series_ids=None, num_samples=NUM_SAMPLES, lgbm_kwargs=LGBM_KWARGS\n", + ")\n", + "lightgbm_cov = DartsLightGBMPredictor(\n", + " lags=LAGS,\n", + " lags_past_covariates=LAGS,\n", + " covariate_series_ids=COVARIATES,\n", + " num_samples=NUM_SAMPLES,\n", + " lgbm_kwargs=LGBM_KWARGS,\n", + ")\n", + "\n", + "all_predictors = [naive, ets, kalman, autoarima, linreg, linreg_cov, lightgbm, lightgbm_cov]\n", + "\n", + "# Which covariate panel each predictor consumes — drives the leaderboard's\n", + "# covariate columns. Predictors absent from this map are treated as target-only.\n", + "PREDICTOR_COVARIATES = {\n", + " linreg_cov.predictor_id: COVARIATES,\n", + " lightgbm_cov.predictor_id: COVARIATES,\n", + "}\n", + "# Short labels for the leaderboard \"model\" column and the CRPS charts.\n", + "PREDICTOR_LABELS = {\n", + " naive.predictor_id: \"Naive\",\n", + " ets.predictor_id: \"ETS\",\n", + " kalman.predictor_id: \"Kalman\",\n", + " autoarima.predictor_id: \"AutoARIMA\",\n", + " linreg.predictor_id: \"LinReg\",\n", + " linreg_cov.predictor_id: \"LinReg + cov\",\n", + " lightgbm.predictor_id: \"LightGBM\",\n", + " lightgbm_cov.predictor_id: \"LightGBM + cov\",\n", + "}\n", + "\n", + "# ── LLM-Process (sampled trajectories) — target-only vs with-covariates ────────\n", + "# Gated on POST_CUTOFF: the 2020 stress window is pre-cutoff and leaked for LLMs,\n", + "# so we keep the LLMP rows out of that comparison entirely. The recipe (prompt\n", + "# framing, defaults) lives in predictors/llmp_sampled_trajectory.py; tune it\n", + "# there, or override model= / n_samples= / history_window= per call here.\n", + "if POST_CUTOFF:\n", + " llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", + " llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", + " all_predictors += [llmp, llmp_cov]\n", + " PREDICTOR_COVARIATES[llmp_cov.predictor_id] = COVARIATES\n", + " PREDICTOR_LABELS[llmp.predictor_id] = \"LLMP (target)\"\n", + " PREDICTOR_LABELS[llmp_cov.predictor_id] = \"LLMP + cov\"\n", + "\n", + "print(f\"{len(all_predictors)} predictors configured (LLMP {'on' if POST_CUTOFF else 'off'}):\")\n", + "for p in all_predictors:\n", + " print(f\" {p.predictor_id}\")" + ] + }, + { + "cell_type": "markdown", + "id": "ab6c797d", + "metadata": {}, + "source": [ + "---\n", + "## 5. 2025 backtest — the comparison (post-cutoff)\n", + "\n", + "`cached_multi_backtest` runs each predictor across the three single-horizon tasks\n", + "and writes every `BacktestResult` to `data/predictions//` — so a re-run\n", + "is free from cache (pass `force_refresh=True` to recompute). The loop prints each\n", + "task's mean CRPS as it lands." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "0aa4e777", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running last_value_naive ...\n", + " sp500_logret_1b mean CRPS = 0.01196 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01721 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.02964 (6 preds)\n", + "Running darts_ets ...\n", + " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01345 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.02101 (6 preds)\n", + "Running darts_kalman ...\n", + " sp500_logret_1b mean CRPS = 0.00385 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00931 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01000 (6 preds)\n", + "Running darts_autoarima ...\n", + " sp500_logret_1b mean CRPS = 0.00415 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00962 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01406 (6 preds)\n", + "Running darts_linreg ...\n", + " sp500_logret_1b mean CRPS = 0.00352 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00994 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01427 (6 preds)\n", + "Running darts_linreg_cov ...\n", + " sp500_logret_1b mean CRPS = 0.00345 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00943 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01413 (6 preds)\n", + "Running darts_lightgbm ...\n", + " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01003 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01475 (6 preds)\n", + "Running darts_lightgbm_cov ...\n", + " sp500_logret_1b mean CRPS = 0.00356 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00919 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01121 (6 preds)\n", + "Running llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " sp500_logret_1b mean CRPS = 0.00360 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01258 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01778 (6 preds)\n", + "Running llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " sp500_logret_1b mean CRPS = 0.00383 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01182 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01969 (6 preds)\n" + ] + } + ], + "source": [ + "results_by_predictor: dict[str, dict[str, object]] = {}\n", + "\n", + "for predictor in all_predictors:\n", + " print(f\"Running {predictor.predictor_id} ...\", flush=True)\n", + " results_by_predictor[predictor.predictor_id] = cached_multi_backtest(\n", + " predictor=predictor,\n", + " spec=backtest_spec,\n", + " data_service=svc,\n", + " store_dir=PREDICTIONS_DIR,\n", + " )\n", + " for task_id, result in results_by_predictor[predictor.predictor_id].items():\n", + " print(f\" {task_id:18s} mean CRPS = {result.mean_score:.5f} ({len(result.predictions)} preds)\")\n", + "\n", + "RESULTS_DF = build_leaderboard(\n", + " results_by_predictor,\n", + " svc,\n", + " covariates_by_predictor=PREDICTOR_COVARIATES,\n", + " labels_by_predictor=PREDICTOR_LABELS,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cef968dc", + "metadata": {}, + "source": [ + "### Leaderboard — mean CRPS by method and horizon\n", + "\n", + "Read it as a story: the spread between methods (and the naive floor's\n", + "disadvantage) is widest at `h=1` and compresses by `h=21`. The `dir_*` columns\n", + "report next-direction skill (most meaningful at `h=1`)." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4f601c83", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01sp500_logret_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.003456600.5001.0000.6670.5000.2226
11sp500_logret_1bLinRegFalse0—darts_linreg0.003526600.5001.0000.6670.5000.7786
21sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.003566600.3330.3330.3330.3330.3336
31sp500_logret_1bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.003606600.0000.0000.0000.5000.5006
41sp500_logret_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.003836600.0000.0000.0000.5000.2226
51sp500_logret_1bKalmanFalse0—darts_kalman0.003856600.6670.6670.6670.6670.5566
61sp500_logret_1bLightGBMFalse0—darts_lightgbm0.003916600.5000.6670.5710.5000.5566
71sp500_logret_1bETSFalse0—darts_ets0.003916600.6670.6670.6670.6670.5566
81sp500_logret_1bAutoARIMAFalse0—darts_autoarima0.004156600.6670.6670.6670.6670.4446
91sp500_logret_1bNaiveFalse0—last_value_naive0.011966600.5001.0000.6670.5000.5006
105sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009196600.3331.0000.5000.3330.8756
115sp500_logret_5bKalmanFalse0—darts_kalman0.009316600.0000.0000.0000.3330.0006
125sp500_logret_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.009436600.3331.0000.5000.3330.3756
135sp500_logret_5bAutoARIMAFalse0—darts_autoarima0.009626600.5001.0000.6670.6670.5006
145sp500_logret_5bLinRegFalse0—darts_linreg0.009946600.3331.0000.5000.3330.7506
155sp500_logret_5bLightGBMFalse0—darts_lightgbm0.010036600.2000.5000.2860.1670.3756
165sp500_logret_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.011826600.5001.0000.6670.6670.5006
175sp500_logret_5bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.012586600.2500.5000.3330.3330.2506
185sp500_logret_5bETSFalse0—darts_ets0.013456600.3330.5000.4000.5000.2506
195sp500_logret_5bNaiveFalse0—last_value_naive0.017216600.3330.5000.4000.5000.5006
2021sp500_logret_21bKalmanFalse0—darts_kalman0.010006600.5001.0000.6670.5000.8896
2121sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.011216600.5000.6670.5710.5000.4446
2221sp500_logret_21bAutoARIMAFalse0—darts_autoarima0.014066600.7501.0000.8570.8330.8896
2321sp500_logret_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.014136600.5001.0000.6670.5000.6676
2421sp500_logret_21bLinRegFalse0—darts_linreg0.014276600.5001.0000.6670.5000.5566
2521sp500_logret_21bLightGBMFalse0—darts_lightgbm0.014756600.5001.0000.6670.5000.4446
2621sp500_logret_21bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.017786600.5001.0000.6670.5000.3336
2721sp500_logret_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.019696600.5001.0000.6670.5000.1676
2821sp500_logret_21bETSFalse0—darts_ets0.021016600.5001.0000.6670.5000.7786
2921sp500_logret_21bNaiveFalse0—last_value_naive0.029646600.5001.0000.6670.5000.5006
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_multivariate_backtest_leaderboard(RESULTS_DF)" + ] + }, + { + "cell_type": "markdown", + "id": "da3a09c0", + "metadata": {}, + "source": [ + "### Reading the LLMP ± covariates rows\n", + "\n", + "When the LLM-Process rows are present (post-cutoff windows), compare them directly:\n", + "- **LLMP (target)** — the LLM sees **only** the return history.\n", + "- **LLMP + cov** — it additionally sees **labeled covariate-history blocks**\n", + " (VIX, yields, …) in its prompt.\n", + "\n", + "Their CRPS gap answers the headline question: does the same exogenous panel the\n", + "ML methods use help an LLM — and does either LLMP variant reach the\n", + "gradient-boosting rows? Because these windows are **post-cutoff**, the LLM can't\n", + "be reciting memorised outcomes." + ] + }, + { + "cell_type": "markdown", + "id": "838475a9", + "metadata": {}, + "source": [ + "---\n", + "## 6. Forecast vs realised (next session, h=1)\n", + "\n", + "A few models' median next-session forecast against the realised return (percent)\n", + "— the near-zero, low-amplitude forecasts vs the noisy realised series are the\n", + "daily-efficiency story made visual. These reuse the cached `h=1` predictions from\n", + "Section 5 (no re-running)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "97076392", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# A few representative models, pulled straight from the cached backtest results.\n", + "H1_TASK = \"sp500_logret_1b\"\n", + "demo_ids = [naive.predictor_id, ets.predictor_id, lightgbm_cov.predictor_id]\n", + "\n", + "compare_by_run: dict[str, pd.DataFrame] = {}\n", + "for pid in demo_ids:\n", + " result = results_by_predictor.get(pid, {}).get(H1_TASK)\n", + " if result is None:\n", + " continue\n", + " label = PREDICTOR_LABELS.get(pid, pid)\n", + " compare_by_run[label] = build_return_compare_frame(result.predictions, svc, H1_TASK)\n", + "\n", + "fig, _ = plot_return_forecast_vs_actual_multi(compare_by_run, title=\"Next-session return: forecast vs realised (h=1)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "89b9939e", + "metadata": {}, + "source": [ + "---\n", + "## 7. Protected 2026 eval — the honest scoreboard\n", + "\n", + "`multi_evaluate()` against the held-out 2026 window. This is **scarce**:\n", + "`sp500_eval_2026.yaml` carries `max_runs`, and one `multi_evaluate` call across\n", + "all three horizons counts as **one** run. Pass an `EvalTracker` to enforce that\n", + "budget across sessions (commented below — it would block re-runs once exhausted,\n", + "so we leave it off here and just show `run_number`).\n", + "\n", + "Spend it on the models you committed to from the 2025 backtest — pick a small\n", + "**finalists** list in the cell below, not the whole roster. Eval results are\n", + "never cached (caching would obscure the budget), so each run recomputes.\n", + "\n", + "> **Runtime:** the finalists default to the cutoff-safe baselines plus\n", + "> `LightGBM + cov`. Add the LLMP variants when you're ready to spend the proxy\n", + "> tokens on the protected scoreboard (each LLMP eval is ~8 origins × 3 horizons ×\n", + "> `n_samples` calls)." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5570e8b0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Evaluating last_value_naive ...\n", + " sp500_logret_1b mean CRPS = 0.01043 (run #1)\n", + " sp500_logret_5b mean CRPS = 0.00984 (run #1)\n", + " sp500_logret_21b mean CRPS = 0.04483 (run #1)\n", + "Evaluating darts_lightgbm_cov ...\n", + " sp500_logret_1b mean CRPS = 0.00429 (run #1)\n", + " sp500_logret_5b mean CRPS = 0.01265 (run #1)\n", + " sp500_logret_21b mean CRPS = 0.03705 (run #1)\n" + ] + } + ], + "source": [ + "# from aieng.forecasting.evaluation import EvalTracker\n", + "# tracker = EvalTracker(ROOT / \"data\" / \"sp500_eval_runs.yaml\") # enforces max_runs across sessions\n", + "\n", + "with (SPECS_DIR / _EVAL_SPEC_FILE).open() as f:\n", + " eval_spec = MultiTargetEvalSpec.model_validate(yaml.safe_load(f))\n", + "\n", + "# Finalists only — the held-out window is scarce. Pick the handful you committed\n", + "# to from the 2025 backtest (e.g. add `llmp`, `llmp_cov` here when ready to spend\n", + "# the proxy tokens — they exist only when POST_CUTOFF).\n", + "eval_finalists = [naive, lightgbm_cov]\n", + "\n", + "eval_results: dict[str, dict[str, object]] = {}\n", + "for predictor in eval_finalists:\n", + " print(f\"Evaluating {predictor.predictor_id} ...\", flush=True)\n", + " eval_results[predictor.predictor_id] = multi_evaluate(\n", + " predictor=predictor,\n", + " spec=eval_spec,\n", + " data_service=svc,\n", + " tracker=None, # set to `tracker` above to enforce the max_runs budget\n", + " )\n", + " for task_id, result in eval_results[predictor.predictor_id].items():\n", + " print(f\" {task_id:18s} mean CRPS = {result.mean_score:.5f} (run #{result.run_number})\")\n", + "\n", + "EVAL_DF = build_leaderboard(\n", + " eval_results,\n", + " svc,\n", + " covariates_by_predictor=PREDICTOR_COVARIATES,\n", + " labels_by_predictor=PREDICTOR_LABELS,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "e6a1826b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalrun_number
01sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.004298800.2500.3330.2860.3750.46781
11sp500_logret_1bNaiveFalse0—last_value_naive0.010438800.2860.6670.4000.2500.33381
25sp500_logret_5bNaiveFalse0—last_value_naive0.009847710.3330.5000.4000.5710.55071
35sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.012657710.3331.0000.5000.4290.60071
421sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037058800.2501.0000.4000.2500.41781
521sp500_logret_21bNaiveFalse0—last_value_naive0.044838800.0000.0000.0000.5000.33381
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display_multivariate_backtest_leaderboard(EVAL_DF)" + ] + }, + { + "cell_type": "markdown", + "id": "25797985", + "metadata": {}, + "source": [ + "---\n", + "## Where to go next\n", + "\n", + "- **Scale up the backtest.** Set `EXPERIMENT_CONFIG = \"backtest_2025\"` (weekly\n", + " origins across all of 2025, full panel) and re-run from Section 2. The LLMP rows\n", + " are slow over ~50 origins — trim `all_predictors` or widen the spec's `stride`.\n", + "- **Study the COVID regime (numerical only).** Set\n", + " `EXPERIMENT_CONFIG = \"stress_2020\"`: a volatile window where a covariate edge is\n", + " most visible. The predictors cell drops the LLMP rows automatically (2020 is\n", + " pre-cutoff, so an LLM would be reciting, not forecasting).\n", + "- **Add a method.** Instantiate any `Predictor` (e.g. another Darts model — mirror\n", + " `aieng/forecasting/methods/numerical/darts_classical.py`) in the predictors\n", + " cell and append it to `all_predictors` with a `PREDICTOR_LABELS` entry. No\n", + " registry or dispatch to edit.\n", + "- **Tune the LLM-Process.** Edit `predictors/llmp_sampled_trajectory.py` (prompt\n", + " framing, history window) or pass a different `model=` / `n_samples=` in the\n", + " predictors cell. How do the results move?\n", + "- **Try an agentic forecaster** with the same cutoff caveats as the LLMP. Do\n", + " agents add lift over LLMPs or the conventional methods?\n", + "- **Spend the eval honestly.** Wire the `EvalTracker` in Section 7 so `max_runs`\n", + " is enforced, and only evaluate models you've committed to." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/implementations/BAA10Y_forecasting /99_starter_agent.ipynb b/implementations/BAA10Y_forecasting /99_starter_agent.ipynb new file mode 100644 index 00000000..150c929d --- /dev/null +++ b/implementations/BAA10Y_forecasting /99_starter_agent.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "cell-00", + "metadata": {}, + "source": [ + "# S&P 500 — Your Starter Agent\n", + "\n", + "**If you're not sure what to do next, continue from here.**\n", + "\n", + "This notebook is a fresh, hackable agent for the S&P 500 use case — deliberately *not* wired into the numbered curriculum. It gives you our common building blocks behind simple toggles, so you can start building something of your own:\n", + "\n", + "- **optional news search** — bounded, cutoff-aware Google Search (proxy-only)\n", + "- **optional code execution** — an E2B Python sandbox\n", + "- **two lightweight skills** — *tool-usage playbooks* in `starter_agent/skills/`\n", + "\n", + "It does two things: lets you **talk to the agent** (open-ended, Track 2) and **score one real forecast** (Track 1). The live cells are gated by `RUN_AGENT` so a fresh `Run All` is safe and free; flip it to `True` to actually call the model.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-01", + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "from pathlib import Path\n", + "\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "import pandas as pd\n", + "from dotenv import load_dotenv\n", + "\n", + "\n", + "# Repo root holds the .env with PROXY_* creds the agent needs.\n", + "ROOT = Path.cwd().resolve().parents[1]\n", + "load_dotenv(ROOT / \".env\", override=False)\n", + "\n", + "# ── Model selection ───────────────────────────────────\n", + "# Two project models: \"gemini-3.1-flash-lite-preview\" (lite/default) and\n", + "# \"gemini-3.5-flash\" (advanced). Lite is the default.\n", + "AGENT_MODEL = \"gemini-3.1-flash-lite-preview\"\n", + "# AGENT_MODEL = \"gemini-3.5-flash\" # advanced (higher cost/latency)\n", + "\n", + "# ── Run guard ──────────────────────────────────────\n", + "# Live agent calls cost tokens and need PROXY_* in the repo-root .env, plus warm\n", + "# data caches. Default False so `Run All` is safe; set True to call the model.\n", + "RUN_AGENT = False\n", + "\n", + "from sp500_forecasting.starter_agent import (\n", + " build_starter_agent_config,\n", + " build_starter_agent_predictor,\n", + ")\n", + "\n", + "\n", + "print(\"RUN_AGENT =\", RUN_AGENT, \"| model =\", AGENT_MODEL)" + ] + }, + { + "cell_type": "markdown", + "id": "cell-02", + "metadata": {}, + "source": [ + "---\n", + "## 1. Meet your agent\n", + "\n", + "`build_starter_agent_config` returns an `AgentConfig` with two toggles. The default turns **news search on** (proxy-only, no extra key) and **code execution off** (it needs `E2B_API_KEY` and is slower). Flip them and re-run — the loaded skills follow the enabled tools.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-03", + "metadata": {}, + "outputs": [], + "source": [ + "config = build_starter_agent_config(\n", + " model=AGENT_MODEL,\n", + " enable_search=True, # ← cutoff-aware Google Search (proxy-only)\n", + " enable_code_exec=False, # ← E2B Python sandbox (needs E2B_API_KEY); try True!\n", + ")\n", + "\n", + "print(\"Agent:\", config.name)\n", + "print(\"Search enabled: \", config.context_retrieval.enabled)\n", + "print(\"Code-exec enabled: \", config.code_execution.enabled)\n", + "print(\"Skills loaded: \", [p.name for p in config.skills_dirs])\n", + "print(\"\\n── System instruction (edit this in starter_agent/agent.py) ──\\n\")\n", + "print(config.instruction[:1200], \"...\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-04", + "metadata": {}, + "source": [ + "---\n", + "## 2. Talk to it *(Track 2 — open-ended analysis)*\n", + "\n", + "Ask the agent anything. This is the interactive mode: no scoring, no schema — just reasoning (and a web search, since search is on). Edit the question and explore.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-05", + "metadata": {}, + "outputs": [], + "source": [ + "from aieng.forecasting.methods.agentic import build_adk_agent\n", + "from aieng.forecasting.methods.agentic.adk_runner import AdkTextRunner, AdkTextRunnerConfig\n", + "\n", + "\n", + "QUESTION = (\n", + " \"What are the key macro risks for U.S. equities over the next month, and how \"\n", + " \"should they shape the spread of a 1-week S&P 500 return forecast? Be concise.\"\n", + ")\n", + "\n", + "if RUN_AGENT:\n", + " chat_agent = build_adk_agent(config) # schema-free: plain text in, text out\n", + " runner = AdkTextRunner(chat_agent, config=AdkTextRunnerConfig(app_name=\"sp500_starter_chat\"))\n", + " reply = await runner.run_text_async(QUESTION) # noqa: F704, PLE1142\n", + " print(reply)\n", + "else:\n", + " print(\"RUN_AGENT is False — set it to True in the setup cell to talk to the agent.\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-06", + "metadata": {}, + "source": [ + "---\n", + "## 3. Score one prediction against a known outcome *(Track 1)*\n", + "\n", + "Now run the agent as a `Predictor`. We pick the **most recent origin whose horizon has already resolved**, forecast the 1-week S&P 500 return, and check whether the actual return landed inside the agent's 80% band. (One origin can't tell you if the agent is *calibrated*; that's what the backtest in `01_sp500_multivariate_backtest.ipynb` is for.) Live, so gated by `RUN_AGENT`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cell-07", + "metadata": {}, + "outputs": [], + "source": [ + "from datetime import datetime, timezone\n", + "\n", + "\n", + "if RUN_AGENT:\n", + " from aieng.forecasting.evaluation.task import ForecastingTask\n", + " from sp500_forecasting import build_sp500_multivariate_service\n", + " from sp500_forecasting.data import sp500_logret_series_id\n", + "\n", + " HORIZON = 5\n", + " COVARIATES = [\"vix_level_l1b\", \"ust10y_level_l1b\", \"oil_log_ret_1b_l1b\"]\n", + " svc = build_sp500_multivariate_service(covariate_series_ids=COVARIATES)\n", + " now = datetime.now(tz=timezone.utc).replace(tzinfo=None)\n", + " tgt = sp500_logret_series_id(HORIZON)\n", + " full = svc.get_series(tgt, as_of=now)\n", + " full[\"timestamp\"] = pd.to_datetime(full[\"timestamp\"])\n", + " last_date = full[\"timestamp\"].iloc[-1]\n", + "\n", + " # Most recent origin whose horizon return has already resolved.\n", + " AS_OF = last_date - pd.offsets.BDay(HORIZON + 1)\n", + "\n", + " task = ForecastingTask(\n", + " task_id=f\"sp500_logret_{HORIZON}b\",\n", + " target_series_id=tgt,\n", + " horizons=[HORIZON],\n", + " frequency=\"B\",\n", + " description=f\"S&P 500 cumulative log return, {HORIZON} business days ahead (starter).\",\n", + " )\n", + " ctx = svc.context(as_of=AS_OF)\n", + " pred = build_starter_agent_predictor(config, covariate_series_ids=COVARIATES).predict(task, ctx)[0]\n", + "\n", + " rows = full[full[\"timestamp\"] >= AS_OF + pd.offsets.BDay(HORIZON)]\n", + " actual = float(rows[\"value\"].iloc[0]) if not rows.empty else None\n", + "\n", + " fc = pred.payload\n", + " lo, hi = fc.quantiles[0.10], fc.quantiles[0.90]\n", + " print(f\"Origin as_of={AS_OF.date()} horizon={HORIZON}b (1 week) (latest data {last_date.date()})\\n\")\n", + " print(f\" agent point : {fc.point_forecast:+.4f} ({fc.point_forecast * 100:+.2f}%)\")\n", + " print(f\" agent 80% CI : [{lo:+.4f}, {hi:+.4f}]\")\n", + " if actual is None:\n", + " print(\" actual : N/A (not yet resolved)\")\n", + " else:\n", + " inb = \"yes ✓\" if lo <= actual <= hi else \"no ✗\"\n", + " print(f\" actual : {actual:+.4f} ({actual * 100:+.2f}%) in 80% band? {inb}\")\n", + " if pred.metadata.get(\"rationale\"):\n", + " print(\"\\nRationale:\", pred.metadata[\"rationale\"][:300])\n", + "else:\n", + " print(\"RUN_AGENT is False — set it to True to score a live forecast against a known outcome.\")" + ] + }, + { + "cell_type": "markdown", + "id": "cell-08", + "metadata": {}, + "source": [ + "---\n", + "## 4. Make it yours\n", + "\n", + "This agent is a starting point. Here are concrete next steps, easiest first — each is a small edit, then re-run the cells above.\n", + "\n", + "1. **Flip code execution on.** Set `enable_code_exec=True` in §1 (needs `E2B_API_KEY`). The agent loads the `code-analysis-playbook` skill and can compute its own diagnostics before forecasting. Compare the rationale.\n", + "2. **Edit the agent's personality.** Open `starter_agent/agent.py` and change `_build_starter_instruction()` — make it more cautious, more contrarian, focused on one driver. Re-run §1 to see the new instruction.\n", + "3. **Sharpen the skills.** The two files in `starter_agent/skills/` are short on purpose. Add your best queries to `research-playbook`, or a new diagnostic to `code-analysis-playbook`. The agent picks them up automatically.\n", + "4. **Change the question and the origin.** Try a different `QUESTION` in §2 and a different origin in §3.\n", + "5. **Widen the covariate panel.** Pass `DEFAULT_COVARIATE_SERIES_IDS` (11 series) to both the service and `build_starter_agent_predictor(...)` and see if the extra context helps.\n", + "6. **Forecast other horizons.** Swap `HORIZON` to 1 (next session) or 21 (1 month) — each maps to its own `sp500_logret_{h}b` target.\n", + "\n", + "Bigger ideas — the full conventional-vs-LLM-Process comparison and direction metrics in `01_sp500_multivariate_backtest.ipynb` — are in the use-case `README.md` and `planning-docs/roadmap.md`.\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/implementations/BAA10Y_forecasting /README.md b/implementations/BAA10Y_forecasting /README.md new file mode 100644 index 00000000..53745d74 --- /dev/null +++ b/implementations/BAA10Y_forecasting /README.md @@ -0,0 +1,242 @@ +# S&P 500 multivariate forecasting (leak-safe covariates) + +> **Reference implementation 1 of 4.** Recommended order: [getting_started](../getting_started/) → **S&P 500** → [food CPI](../food_price_forecasting/) → [energy / WTI](../energy_oil_forecasting/) → [BoC rate decisions](../boc_rate_decisions/). Each stands on its own. + +The **financial-markets** reference: a head-to-head comparison of conventional +time-series methods on a daily equity index, all reading the **same leak-safe +covariate panel**, plus an LLM-Process forecaster that can read those covariates +in its prompt. It is the template for evaluated prediction (Track 1) on market +series with exogenous covariates. + +The headline question: + +> Given the same macro/market observations, **which method forecasts the index +> best — and can an LLM-Process, handed those covariates, keep up with gradient +> boosting?** + +**How this differs from the energy/oil reference.** Energy forecasts a +*univariate* price trajectory with news-grounded, code-executing, and adaptive +**agents**. This reference has no agents and no news — it is a clean, reproducible +**numerical-methods bake-off across a multivariate covariate panel**, scored with +CRPS and direction metrics. + +--- + +## Forecasting task + +The targets are **close-to-close cumulative log returns** of `^GSPC`, registered +one series per horizon (window `N` in business days): + +$$ +r^{(N)}_t = \log\frac{C^{\text{adj}}_{t}}{C^{\text{adj}}_{t-N}} +$$ + +Forecasting `sp500_logret_{N}b` exactly `N` business days ahead resolves to the +**forward** cumulative return over the next `N` sessions — a clean single-marginal +forecast at each horizon (no joint-path aggregation): + +| Target | Horizon | Actionable framing | +|--------|---------|--------------------| +| `sp500_logret_1b` | 1 (next session) | direction / next-day **risk management** | +| `sp500_logret_5b` | 5 (forward 1 week) | tactical rebalancing, weekly tenors | +| `sp500_logret_21b` | 21 (forward 1 month) | allocation, monthly tenors | + +**Frequency:** business (`B`). Returns (not the index level) keep the target +stationary, which is the right setup for a methods comparison. + +**What's forecastable at daily resolution.** The *level* of index returns is +close to a martingale, so far-ahead point forecasts trend toward ~0 and add +little; the forecastable, actionable objects are **volatility, tail risk, and +direction**. That is why a VIX-led covariate panel can help — and why the +method/covariate edge is largest at `h=1` and compresses as the horizon grows. +The notebook's opening note develops this. + +--- + +## Methods compared + +| Family | Predictors | Covariates? | +|--------|-----------|-------------| +| Naive floor | `LastValuePredictor` | — | +| Classical | `DartsExponentialSmoothingPredictor` (ETS), `DartsKalmanForecasterPredictor`, `DartsAutoARIMAPredictor` | — (univariate) | +| ML regression | `DartsLinearRegressionPredictor`, `DartsLightGBMPredictor` | ✅ optional past covariates | +| LLM-Process | `SampledTrajectoryLLMPredictor` | ✅ optional covariate prompt blocks | + +The **LLMP (target)** vs **LLMP + cov** rows are the centerpiece: the covariate +variant serializes labeled covariate-history blocks into the prompt (the +`covariate_series_ids=` passed to `build_sp500_llmp_sampled_trajectory`), so their +CRPS gap measures whether an LLM can use the same exogenous observations the ML +methods do. Both are built in the notebook's predictors cell. + +--- + +## Canonical covariates (when enabled) + +| Series ID (registered) | Economic meaning | +|------------------------|------------------| +| `vix_level_l1b` | VIX level, lagged 1 business day | +| `vix_log_ret_1b_l1b` | VIX log return, lagged | +| `ust10y_level_l1b` | 10Y Treasury yield | +| `ust2y10y_spread_l1b` | 2Y–10Y spread | +| `fed_funds_level_l1b` | Fed funds effective rate | +| `cpi_mom_logdiff_l1b` | CPI MoM log-diff | +| `unemployment_rate_l1b` | Unemployment rate | +| `oil_log_ret_1b_l1b` | Oil futures log return | +| `gold_log_ret_1b_l1b` | Gold log return (skipped if FRED series unavailable) | +| `dollar_index_log_ret_1b_l1b` | Broad dollar index log return | +| `nasdaq_log_ret_1b_l1b` | NASDAQ composite log return | + +Exact adapters and transforms live in `data.py` (`DEFAULT_COVARIATE_SERIES_IDS`). +Yahoo covariates use `YFinanceDailyAdapter` (parquet under `data/yfinance/` at the +repo root); FRED series use `FREDAdapter` (`data/fred/`). Warm both caches to the +present before running the 2025/2026 windows (see Prerequisites). + +--- + +## Cutoff-aware evaluation (read this) + +This is the methodological heart of the comparison, and easy to get wrong. + +- **Numerical methods are cutoff-safe by construction.** Naive, ETS, Kalman, + AutoARIMA, LinReg and LightGBM only ever see the series up to the forecast + origin (`ForecastContext` enforces it), so they can be backtested on *any* + historical window. +- **An LLM is not.** Gemini's training cutoff is ~**January 2025**, so it has + effectively memorised pre-2025 outcomes. Scoring an LLM-Process on a pre-cutoff + origin measures recall, not forecasting, and silently flatters it in the + head-to-head. + +So the LLM-inclusive comparison lives **after the cutoff** — a **2025 backtest** +for iteration and a **protected 2026 eval** as the honest scoreboard (mirroring +the energy reference and `getting_started`'s `backtest()` → `evaluate()` split). +The 2020 COVID window is kept as a **numerical-only** stress test. + +--- + +## No-leakage design + +- Every covariate is shifted by **one business day** before registration. +- Macro series use **conservative release proxies** before daily expansion; + rows carry `released_at` suitable for `ForecastContext` cutoffs. +- Backtests enforce **information available at `as_of`**. + +Missing optional feeds are **skipped with warnings** by default +(`strict_covariates=False`). Set `strict_covariates=True` to fail fast. + +--- + +## Specs — windows and tasks (experiment design only) + +Four co-located YAML configs. Each spec carries **only the experiment design** — +the window (`start`/`end`/`stride`/`warmup`) and one single-horizon task per +`sp500_logret_{N}b` target (`horizons: [N]`, `frequency: B`). The first three are +`MultiTargetBacktestSpec`; the eval spec is a `MultiTargetEvalSpec` that adds +`max_runs`. **Which predictors run, and all their hyperparameters (including the +covariate panel), live in the notebook — not the spec.** + +```text +specs/ +├── sp500_smoke.yaml # fast laptop run — short late-2025 window (post-cutoff) +├── sp500_backtest_2025.yaml # main comparison: weekly origins across 2025 +├── sp500_eval_2026.yaml # protected held-out 2026 eval (MultiTargetEvalSpec, max_runs) +└── sp500_stress_2020.yaml # COVID-crash stress, numerical only (notebook drops LLMP — pre-cutoff is leaked) +``` + +The notebook runs the 2025 backtest (Section 5) and the protected 2026 eval +(Section 7); set `EXPERIMENT_CONFIG = "stress_2020"` to study the volatile regime +with the cutoff-safe methods (the predictors cell drops the LLMP rows +automatically). Copy a spec and edit the window/tasks to pose a new study. + +--- + +## Module layout + +```text +implementations/sp500_forecasting/ +├── data.py # build_sp500_multivariate_service(); cumulative-return targets; covariate ids +├── predictors/ # build_sp500_llmp_sampled_trajectory() — the S&P 500 LLMP recipe +├── leaderboard.py # build_leaderboard(): cached results → RESULTS_DF; forecast-vs-actual frame +├── analysis.py # style_results_dataframe(); direction metrics +├── plots.py # target history; per-horizon CRPS; forecast vs realised return +├── starter_agent/ # fresh, hackable agent template (toggleable search/code-exec + skills) +├── specs/ # sp500_smoke / sp500_backtest_2025 / sp500_eval_2026 / sp500_stress_2020 +├── 01_sp500_multivariate_backtest.ipynb +├── 99_starter_agent.ipynb # ← start here to build your own agent +└── README.md +``` + +Unit tests for data helpers live under +`implementations/tests/sp500_forecasting/test_data.py`. + +--- + +## Adding a method + +The roster is meant to grow, and it's all just code now — no registry or dispatch +to edit. In the notebook's predictors cell: + +1. Instantiate any `Predictor` and append it to `all_predictors`. For a new Darts + model, mirror `aieng-forecasting/aieng/forecasting/methods/numerical/darts_classical.py` + (univariate, probabilistic via `num_samples`, per-horizon quantiles) and export + it from `methods/numerical/__init__.py` and `methods/__init__.py` first. +2. Add a `PREDICTOR_LABELS` entry (the leaderboard "model" column). If it reads the + covariate panel, also add a `PREDICTOR_COVARIATES` entry so the leaderboard's + covariate columns are correct. + +For a tuned LLM-Process variant, add a builder to `predictors/` (mirror +`predictors/llmp_sampled_trajectory.py`) so the prompt framing is reusable. + +Keep numerical models **fast** (sub-second per origin) and **probabilistic** (CRPS +needs a distribution — deterministic models like Theta need a conformal/residual +wrapper first). + +--- + +## Prerequisites + +From the **repository root**, run `uv sync` once so `sp500_forecasting` is on the +interpreter path (same pattern as `food_price_forecasting` / `energy_oil_forecasting`). +Use the project `.venv` as the Jupyter kernel — imports are `from sp500_forecasting import ...`. + +Warm caches at the repo root (gitignored) to the **present** — the 2025/2026 +windows need coverage through today: + +```bash +uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC (Yahoo) +uv run python scripts/fetch_fred.py # macro covariates (FRED) +``` + +`fetch_fred.py` requires a **FRED API key** in your repo-root `.env` (`FRED_API_KEY=...`). +FRED keys are free but must be requested individually — **we cannot provide one for you**. +Request yours at (approval is usually +quick, but allow some time). A description like "Requesting an API key to explore the +effectiveness of various forecasting techniques on economic data." works well. + +The `llmp_*` rows call the Vector proxy, so a populated repo-root `.env` (with +`OPENAI_BASE_URL` / `OPENAI_API_KEY`) is required when those rows are enabled. + +**How to run:** open `01_sp500_multivariate_backtest.ipynb` and **Run All**. The +`EXPERIMENT_CONFIG` cell selects the 2025 comparison spec (`"smoke"` by default; +`"backtest_2025"` for the full run, `"stress_2020"` for the numerical-only COVID +study); the protected 2026 eval (`sp500_eval_2026.yaml`) runs in Section 7. The +predictor roster is configured in the predictors cell (Section 4). + +The default smoke run keeps the LLM-Process rows on in the 2025 backtest (the +headline comparison); the 2026 eval's `eval_finalists` list defaults to the +cutoff-safe baselines plus `LightGBM + cov`, so a first Run All isn't a +long/expensive surprise. Add `llmp` / `llmp_cov` to `eval_finalists` when you're +ready to spend the proxy tokens on the protected scoreboard. + +--- + +## Build your own — `99_starter_agent.ipynb` + +Not sure what to do next? [`99_starter_agent.ipynb`](99_starter_agent.ipynb) is +this use case's first **agent** and a fresh, hackable starting point — *not* +part of the backtest above. It wires our common building blocks behind simple +toggles (cutoff-aware news search, an E2B code sandbox) plus two lightweight +tool-usage skills, and walks through talking to the agent (Track 2), scoring one +real return forecast (Track 1), and a "make it yours" guide. Live cells are +gated by `RUN_AGENT` (default `False`), so a first Run All is safe. + +--- diff --git a/implementations/BAA10Y_forecasting /__init__.py b/implementations/BAA10Y_forecasting /__init__.py new file mode 100644 index 00000000..59014149 --- /dev/null +++ b/implementations/BAA10Y_forecasting /__init__.py @@ -0,0 +1,58 @@ +"""S&P 500 multivariate log-return experiment — leak-safe covariates. + +The demo notebooks are narrative shells over the modules in this directory: + +- :mod:`data` — ``build_sp500_multivariate_service()`` and canonical covariate ids. +- :mod:`predictors` — ``build_sp500_llmp_sampled_trajectory()`` recipe (prompt framing + sampling budget). +- :mod:`leaderboard` — ``build_leaderboard()`` turns cached results into ``RESULTS_DF``. +- :mod:`analysis` — styled leaderboards and direction metrics. +- :mod:`plots` — matplotlib figures (target history, per-horizon CRPS, forecast vs realised return). +- YAML specs — experiment design only (window + one single-horizon task per + ``sp500_logret_{N}b`` target): ``sp500_smoke`` / ``sp500_backtest_2025`` / + ``sp500_eval_2026`` / ``sp500_stress_2020``. The predictor roster lives in the notebook. + +See ``README.md`` for the full experiment description. +""" + +from .data import ( + DEFAULT_COVARIATE_SERIES_IDS, + FRED_PREFETCH_REGISTRY, + FRED_SERIES_IDS_FOR_PREFETCH, + SERIES_ID_2Y10Y_SPREAD, + SERIES_ID_10Y_YIELD, + SERIES_ID_CPI_INFLATION_CHANGE, + SERIES_ID_DOLLAR_INDEX_RETURN, + SERIES_ID_FED_FUNDS, + SERIES_ID_GOLD_RETURN, + SERIES_ID_NASDAQ_RETURN, + SERIES_ID_OIL_RETURN, + SERIES_ID_UNEMPLOYMENT, + SERIES_ID_VIX_CHANGE, + SERIES_ID_VIX_LEVEL, + SP500_LOG_RETURN_SERIES_ID, + SP500_SERIES_ID, + SP500_TICKER, + build_sp500_multivariate_service, +) + + +__all__ = [ + "DEFAULT_COVARIATE_SERIES_IDS", + "FRED_PREFETCH_REGISTRY", + "FRED_SERIES_IDS_FOR_PREFETCH", + "SERIES_ID_2Y10Y_SPREAD", + "SERIES_ID_10Y_YIELD", + "SERIES_ID_CPI_INFLATION_CHANGE", + "SERIES_ID_DOLLAR_INDEX_RETURN", + "SERIES_ID_FED_FUNDS", + "SERIES_ID_GOLD_RETURN", + "SERIES_ID_NASDAQ_RETURN", + "SERIES_ID_OIL_RETURN", + "SERIES_ID_UNEMPLOYMENT", + "SERIES_ID_VIX_CHANGE", + "SERIES_ID_VIX_LEVEL", + "SP500_LOG_RETURN_SERIES_ID", + "SP500_SERIES_ID", + "SP500_TICKER", + "build_sp500_multivariate_service", +] diff --git a/implementations/BAA10Y_forecasting /analysis.py b/implementations/BAA10Y_forecasting /analysis.py new file mode 100644 index 00000000..6bfba71e --- /dev/null +++ b/implementations/BAA10Y_forecasting /analysis.py @@ -0,0 +1,161 @@ +"""Notebook-oriented formatting and direction metrics for the S&P 500 demo.""" + +from __future__ import annotations + +from datetime import datetime, timezone +from typing import TYPE_CHECKING + +import numpy as np +import pandas as pd +from aieng.forecasting.evaluation.prediction import ContinuousForecast +from pandas.io.formats.style import Styler + + +if TYPE_CHECKING: + from aieng.forecasting.data.service import DataService + from aieng.forecasting.evaluation.prediction import Prediction + + +def style_results_dataframe(df: pd.DataFrame) -> Styler: + """Return a :class:`~pandas.io.formats.style.Styler` tuned for ``RESULTS_DF``. + + Intended for ``IPython.display.display`` in Jupyter — readable numeric + precision without manual rounding in every cell of the notebook. + """ + fmt: dict[str, str] = { + "mean_crps": "{:.5f}", + "dir_precision_up": "{:.3f}", + "dir_recall_up": "{:.3f}", + "dir_f1_up": "{:.3f}", + "dir_accuracy": "{:.3f}", + "dir_roc_auc_prob_up": "{:.3f}", + } + fmt = {k: v for k, v in fmt.items() if k in df.columns} + return df.style.format(fmt, na_rep="—") + + +def prob_return_above_threshold_from_quantiles(quantiles: dict[float, float], threshold: float = 0.0) -> float: + """Approximate ``P(X > threshold)`` from a piecewise-linear CDF through quantile pairs.""" + pairs = sorted(((float(v), float(q)) for q, v in quantiles.items()), key=lambda x: x[0]) + if not pairs: + return float("nan") + vs = np.array([p[0] for p in pairs], dtype=float) + qs = np.array([p[1] for p in pairs], dtype=float) + f_at = float(np.interp(threshold, vs, qs, left=0.0, right=1.0)) + return float(np.clip(1.0 - f_at, 0.0, 1.0)) + + +def build_direction_eval_frame( + predictions: list[Prediction], + *, + target_series_id: str, + data_service: DataService, +) -> pd.DataFrame: + """Align each scored prediction with the realized log return at ``forecast_date``.""" + as_of_now = datetime.now(tz=timezone.utc).replace(tzinfo=None) + full_series = data_service.get_series(target_series_id, as_of=as_of_now) + full = full_series.copy() + full["timestamp"] = pd.to_datetime(full["timestamp"]) + lookup = full.set_index("timestamp")["value"] + + rows: list[dict[str, object]] = [] + for p in predictions: + if not isinstance(p.payload, ContinuousForecast): + continue + ts = pd.Timestamp(p.forecast_date) + if ts not in lookup.index: + continue + actual = float(lookup.loc[ts]) + qmap = p.payload.quantiles + prob_up = prob_return_above_threshold_from_quantiles(qmap, threshold=0.0) + rows.append( + { + "as_of": p.as_of, + "forecast_date": p.forecast_date, + "actual": actual, + "point_forecast": p.payload.point_forecast, + "prob_up": prob_up, + "actual_up": int(actual > 0.0), + "pred_up_point": int(p.payload.point_forecast > 0.0), + } + ) + return pd.DataFrame(rows) + + +def direction_classification_metrics( + df: pd.DataFrame, + *, + y_pred_col: str = "pred_up_point", + y_score_col: str = "prob_up", +) -> pd.Series: + """Binary metrics for predicting a positive next-session log return.""" + from sklearn.metrics import ( # noqa: PLC0415 + accuracy_score, + balanced_accuracy_score, + cohen_kappa_score, + confusion_matrix, + matthews_corrcoef, + precision_recall_fscore_support, + roc_auc_score, + ) + + if df.empty: + return pd.Series(dtype=float) + + y_true = df["actual_up"].to_numpy(dtype=int) + y_pred = df[y_pred_col].to_numpy(dtype=int) + n = int(len(y_true)) + pos_rate = float(y_true.mean()) if n else float("nan") + + acc = float(accuracy_score(y_true, y_pred)) + bal_acc = float(balanced_accuracy_score(y_true, y_pred)) + prec, rec, f1, _ = precision_recall_fscore_support(y_true, y_pred, average="binary", pos_label=1, zero_division=0) + prec_f, rec_f, f1_f, _ = precision_recall_fscore_support( + y_true, y_pred, average="binary", pos_label=0, zero_division=0 + ) + tn, fp, fn, tp = confusion_matrix(y_true, y_pred, labels=[0, 1]).ravel() + mcc = float(matthews_corrcoef(y_true, y_pred)) + kappa = float(cohen_kappa_score(y_true, y_pred)) + + baseline_acc = max(pos_rate, 1.0 - pos_rate) + maj = int(pos_rate >= 0.5) + baseline_always_up_acc = float((y_true == maj).mean()) + + roc = float("nan") + if y_score_col in df.columns and np.unique(y_true).size == 2: + try: + roc = float(roc_auc_score(y_true, df[y_score_col].to_numpy(dtype=float))) + except ValueError: + roc = float("nan") + + return pd.Series( + { + "n": n, + "prevalence_up": pos_rate, + "accuracy": acc, + "balanced_accuracy": bal_acc, + "precision_up": float(prec), + "recall_up": float(rec), + "f1_up": float(f1), + "precision_down": float(prec_f), + "recall_down": float(rec_f), + "f1_down": float(f1_f), + "matthews_corrcoef": mcc, + "cohen_kappa": kappa, + "confusion_tn": int(tn), + "confusion_fp": int(fp), + "confusion_fn": int(fn), + "confusion_tp": int(tp), + "baseline_accuracy_maj_class": baseline_acc, + "baseline_always_predict_up": baseline_always_up_acc, + "roc_auc_prob_up": roc, + } + ) + + +__all__ = [ + "build_direction_eval_frame", + "direction_classification_metrics", + "prob_return_above_threshold_from_quantiles", + "style_results_dataframe", +] diff --git a/implementations/BAA10Y_forecasting /data.py b/implementations/BAA10Y_forecasting /data.py new file mode 100644 index 00000000..224e3ff3 --- /dev/null +++ b/implementations/BAA10Y_forecasting /data.py @@ -0,0 +1,769 @@ +"""Leak-safe data-service setup for multivariate BAA10Y spread-change forecasting. + +Target: Moody's Seasoned Baa Corporate Bond Yield Relative to the +10-Year Treasury Constant Maturity Rate, downloaded from FRED using +series ID ``BAA10Y`` + +Δt(N)​=BAA10Yt​−BAA10Yt−N​ + +The target is measured in percent and is forecast at selected business-day +horizons, such as 1, 5, and 21 business days. + + +- (forecast 1 step ahead) → next-session return. +- (forecast 5 steps ahead) → forward 1-week return. +- (forecast 21 steps ahead)→ forward 1-month return. + +Using returns (rather than the index level) keeps the target stationary, which +is the appropriate setup for a conventional-methods comparison. + +Covariates supported (daily business-day frame): +- VIX level / VIX change +- 10Y Treasury yield +- 2Y-10Y yield spread +- Fed funds rate +- CPI inflation change (MoM log-diff) +- Unemployment rate +- Oil returns +- Gold returns +- Dollar index returns +- NASDAQ returns + +Anti-leakage policy: +- Every covariate is transformed and then lagged by one business day. +- ``released_at`` is set conservatively for macro series before daily expansion. +- The DataService cutoff then guarantees context views never include unavailable rows. + +Macro series use :class:`~aieng.forecasting.data.adapters.fred.FREDAdapter`, which +writes ``data/fred/{FRED_SERIES_ID}.parquet`` (see adapter docstring). Run +``uv run python scripts/fetch_fred.py`` to warm the same files the covariate +builders read. Yahoo covariates use :class:`~aieng.forecasting.data.adapters.yfinance.YFinanceDailyAdapter` +under ``data/yfinance/`` (default adapter layout). :func:`build_baa10y_multivariate_service` +loads ``FRED_API_KEY`` from the repo-root ``.env`` via ``python-dotenv``, identical +to ``fetch_fred.py``. Raw series ids and prefetch metadata live in :data:`FRED_PREFETCH_REGISTRY`. +""" + +from __future__ import annotations + +import warnings +from collections.abc import Callable +from pathlib import Path +from typing import Any + +import numpy as np +import pandas as pd +from aieng.forecasting.data import DataService, SeriesMetadata +from aieng.forecasting.data.adapters import FREDAdapter, YFinanceDailyAdapter +from aieng.forecasting.data.features import ( + StaticFrameAdapter, +) +from aieng.forecasting.data.features import ( + apply_one_business_day_feature_lag as _apply_one_business_day_feature_lag, +) +from aieng.forecasting.data.features import ( + business_daily_expand_from_releases as _business_daily_expand_from_releases, +) +from aieng.forecasting.data.features import ( + business_daily_ffill as _business_daily_ffill, +) +from aieng.forecasting.data.features import ( + canonical_three_col as _canonical_three_col, +) +from aieng.forecasting.data.features import ( + drop_weekend_timestamp_rows as _drop_weekend_timestamp_rows, +) +from aieng.forecasting.data.features import ( + to_level_feature_from_daily as _to_level_feature_from_daily, +) +from aieng.forecasting.data.features import ( + to_log_return_feature as _to_log_return_feature, +) + + +_load_dotenv: Callable[..., Any] | None +try: + from dotenv import load_dotenv as _load_dotenv +except ImportError: + _load_dotenv = None + + +def _repo_root() -> Path | None: + here = Path(__file__).resolve() + for p in (here, *here.parents): + if (p / "aieng-forecasting").is_dir(): + return p + return None + + +def _load_fred_dotenv() -> None: + """Populate os.environ from repo-root ``.env`` (same pattern as ``scripts/fetch_fred.py``).""" + if _load_dotenv is None: + return + root = _repo_root() + if root is None: + return + _load_dotenv(root / ".env", override=False) + + +def _as_absolute_cache(path: Path | None) -> Path | None: + if path is None or path.is_absolute(): + return path + root = _repo_root() + if root is not None: + return (root / path).resolve() + return path + + + + + +BAA10Y_FRED_ID = "BAA10Y" +BAA10Y_SERIES_ID = "baa10y_spread_level" + +BAA10Y_CHANGE_WINDOWS: tuple[int, ...] = (1, 5, 21) + +BAA10Y_WINDOW_LABELS: dict[int, str] = { + 1: "next business day", + 5: "5 business days ahead", + 21: "21 business days ahead", +} + + +def baa10y_change_series_id(window: int) -> str: + """Return the canonical target series id for an N-business-day spread change.""" + return f"baa10y_change_{window}b" + + +BAA10Y_CHANGE_TARGETS: dict[int, str] = { + w: baa10y_change_series_id(w) + for w in BAA10Y_CHANGE_WINDOWS +} + +BAA10Y_CHANGE_SERIES_ID = baa10y_change_series_id(1) + + +def _build_cumulative_spread_change_frame(spread_df: pd.DataFrame, window: int) -> pd.DataFrame: + """ + Build trailing BAA10Y changes: value[t] - value[t-window] + + """ + if window < 1: + raise ValueError(f"window must be >= 1, got {window}.") + if "value" not in spread_df.columns: + raise RuntimeError("BAA10Y data must include the spread level as 'value'.") + + frame = spread_df[["timestamp", "value"]].copy().sort_values("timestamp").reset_index(drop=True) + frame["value"] = pd.to_numeric(frame["value"], errors="coerce") + frame = frame.dropna(subset=["value"]).reset_index(drop=True) + + # BAA10Y is measured in percent. + # spread widening/tightening in percentage points + frame["value"] = frame["value"] - frame["value"].shift(window) + + frame = frame.dropna(subset=["value"]).reset_index(drop=True) + frame["released_at"] = pd.to_datetime(frame["timestamp"]) + return frame[["timestamp", "value", "released_at"]] + + +def build_baa10y_target_service( + *, + windows: tuple[int, ...] = BAA10Y_CHANGE_WINDOWS, + refresh: bool = False, + start: str = "2000-01-01", + end: str = "2026-07-01", + fred_cache_dir: Path | None = None, +) -> DataService: + """Register one close-to-close cumulative log-return target per window in ``windows``.""" + _load_fred_dotenv() + + fred_dir = _as_absolute_cache( + fred_cache_dir or DEFAULT_FRED_CACHE_DIR + ) + if fred_dir is None: + raise RuntimeError("Could not resolve FRED cache directory.") + + fred_dir.mkdir(parents=True, exist_ok=True) + + baa10y_df = _fred_frame( + "BAA10Y", + cache_dir=fred_dir, + refresh=refresh, + ) + + baa10y_df = _drop_weekend_timestamp_rows(baa10y_df) + baa10y_df = _business_daily_ffill(baa10y_df) + if start: + baa10y_df = baa10y_df[ + baa10y_df["timestamp"] >= pd.Timestamp(start) + ] + if end is not None: + baa10y_df = baa10y_df[ + baa10y_df["timestamp"] < pd.Timestamp(end) + ] + + baa10y_df = baa10y_df.dropna(subset=["value"]).reset_index(drop=True) + if baa10y_df.empty: + raise RuntimeError( + f"No BAA10Y rows left after applying start={start!r} and end={end!r}." + ) + + svc = DataService() + for window in windows: + series_id = baa10y_change_series_id(window) + label = BAA10Y_WINDOW_LABELS.get(window, f"{window} business days") + + target_frame = _build_cumulative_spread_change_frame( + baa10y_df, + window, + ) + svc.register( + series_id, + StaticFrameAdapter(target_frame), + SeriesMetadata( + series_id=series_id, + description=( + f"BAA10Y cumulative spread change over " + f"{window} business day(s) ({label})" + ), + source=f"FRED ({BAA10Y_FRED_ID}), derived", + units="percentage-points", + + # frequency="D" # every calendar day, including weekends + # frequency="B" # every weekday, excluding weekends + # frequency="MS" # beginning of each month + frequency="B", + table_id=( + f"fred:{BAA10Y_FRED_ID}:change-{window}b" + ), ), + ) + return svc + + +def _default_cache_dir() -> Path: + root = _repo_root() + if root is not None: + return root / "data" + return Path("data") + + +DEFAULT_CACHE_DIR = _default_cache_dir() +# Matches :attr:`~aieng.forecasting.data.adapters.yfinance.YFinanceDailyAdapter.DEFAULT_CACHE_DIR` +# resolved against repo ``data/`` (stem filenames like ``gspc_adj_close_1d.parquet`` per ticker). +DEFAULT_YAHOO_CACHE_DIR = DEFAULT_CACHE_DIR / "yfinance" +DEFAULT_FRED_CACHE_DIR = DEFAULT_CACHE_DIR / "fred" + +# Keys are FRED series ids; values are (description, units, pandas frequency hint) +# for ``scripts/fetch_fred.py`` registration — keep in sync with _fred_frame call sites below. +FRED_PREFETCH_REGISTRY: dict[str, tuple[str, str, str]] = { + + "BAA10Y": ( + "Moody's Seasoned Baa Corporate Bond Yield Relative to " + "Yield on 10-Year Treasury Constant Maturity", + "Percent", + "D", + ), + + "DGS10": ("10-Year Treasury Constant Maturity Rate", "Percent", "D"), + "DGS2": ("2-Year Treasury Constant Maturity Rate", "Percent", "D"), + "DFF": ("Effective Federal Funds Rate", "Percent", "D"), + "CPIAUCSL": ( + "Consumer Price Index for All Urban Consumers: All Items in U.S. City Average", + "Index 1982-84=100", + "MS", + ), + "UNRATE": ("Unemployment Rate", "Percent", "MS"), + "DCOILWTICO": ("Crude Oil Prices: West Texas Intermediate (WTI)", "Dollars per Barrel", "D"), + # NOTE: both London gold fixing series were discontinued by FRED and no + # longer resolve (HTTP 400 "series does not exist"); there is no equivalent + # daily USD gold price on FRED. The gold covariate therefore degrades to + # absent at runtime (see the first-available fallback below and + # ``strict_covariates=False``). ``scripts/fetch_fred.py`` skips them via + # ``KNOWN_UNAVAILABLE_FRED_IDS`` so a clean run reports no spurious failure. + "GOLDAMGBD228NLBM": ( + "Gold Fixing Price 10:30 A.M. (London time) in London Bullion Market", + "U.S. Dollars per Troy Ounce", + "D", + ), + "GOLDPMGBD228NLBM": ( + "Gold Fixing Price 3:00 P.M. (London time) in London Bullion Market", + "U.S. Dollars per Troy Ounce", + "D", + ), + "DTWEXBGS": ("Trade Weighted U.S. Dollar Index: Broad, Goods and Services", "Index Jan 2006=100", "D"), +} + +FRED_SERIES_IDS_FOR_PREFETCH: tuple[str, ...] = tuple(FRED_PREFETCH_REGISTRY.keys()) + +VIX_TICKER = "^VIX" +NASDAQ_TICKER = "^IXIC" + +SERIES_ID_VIX_LEVEL = "vix_level_l1b" +SERIES_ID_VIX_CHANGE = "vix_log_ret_1b_l1b" +SERIES_ID_10Y_YIELD = "ust10y_level_l1b" +SERIES_ID_2Y10Y_SPREAD = "ust2y10y_spread_l1b" +SERIES_ID_FED_FUNDS = "fed_funds_level_l1b" +SERIES_ID_CPI_INFLATION_CHANGE = "cpi_mom_logdiff_l1b" +SERIES_ID_UNEMPLOYMENT = "unemployment_rate_l1b" +SERIES_ID_OIL_RETURN = "oil_log_ret_1b_l1b" +SERIES_ID_GOLD_RETURN = "gold_log_ret_1b_l1b" +SERIES_ID_DOLLAR_INDEX_RETURN = "dollar_index_log_ret_1b_l1b" +SERIES_ID_NASDAQ_RETURN = "nasdaq_log_ret_1b_l1b" + + +DEFAULT_COVARIATE_SERIES_IDS: list[str] = [ + SERIES_ID_VIX_LEVEL, + SERIES_ID_VIX_CHANGE, + SERIES_ID_10Y_YIELD, + SERIES_ID_2Y10Y_SPREAD, + SERIES_ID_FED_FUNDS, + SERIES_ID_CPI_INFLATION_CHANGE, + SERIES_ID_UNEMPLOYMENT, + SERIES_ID_OIL_RETURN, + SERIES_ID_GOLD_RETURN, + SERIES_ID_DOLLAR_INDEX_RETURN, + SERIES_ID_NASDAQ_RETURN, +] + + +def _load_yahoo_close_frame( + ticker: str, + *, + start: str, + end: str | None, + cache_dir: Path, + refresh: bool, +) -> pd.DataFrame: + adapter = YFinanceDailyAdapter( + ticker, + field="Adj Close", + start=start, + end=end, + cache_dir=cache_dir, + refresh=refresh, + ) + raw = adapter.fetch() + frame = raw[["timestamp", "value"]].copy().sort_values("timestamp").reset_index(drop=True) + frame["value"] = pd.to_numeric(frame["value"], errors="coerce") + return frame.dropna(subset=["value"]).reset_index(drop=True) + + +def _fred_frame( + fred_id: str, + *, + cache_dir: Path, + refresh: bool, +) -> pd.DataFrame: + adapter = FREDAdapter(fred_id, cache_dir=cache_dir, refresh=refresh) + return _canonical_three_col(adapter.fetch()) + + +def _build_monthly_cpi_mom_feature( + *, + cache_dir: Path, + refresh: bool, + start: str, + end: str | None, +) -> pd.DataFrame: + cpi = _fred_frame("CPIAUCSL", cache_dir=cache_dir, refresh=refresh) + cpi["value"] = np.log(cpi["value"] / cpi["value"].shift(1)) + cpi = cpi.dropna(subset=["value"]).reset_index(drop=True) + # Conservative monthly publication proxy: mid-next-month business day. + cpi["released_at"] = pd.to_datetime(cpi["timestamp"]) + pd.offsets.MonthEnd(1) + pd.offsets.BDay(10) + daily = _business_daily_expand_from_releases(cpi, start=start, end=end) + return _apply_one_business_day_feature_lag(daily) + + +def _build_monthly_unemployment_feature( + *, + cache_dir: Path, + refresh: bool, + start: str, + end: str | None, +) -> pd.DataFrame: + unrate = _fred_frame("UNRATE", cache_dir=cache_dir, refresh=refresh) + # Conservative publication proxy: 10 business days after month end. + unrate["released_at"] = pd.to_datetime(unrate["timestamp"]) + pd.offsets.MonthEnd(1) + pd.offsets.BDay(10) + daily = _business_daily_expand_from_releases(unrate, start=start, end=end) + return _apply_one_business_day_feature_lag(daily) + + +def _build_daily_fred_level_feature( + fred_id: str, + *, + cache_dir: Path, + refresh: bool, +) -> pd.DataFrame: + x = _fred_frame(fred_id, cache_dir=cache_dir, refresh=refresh) + x = _drop_weekend_timestamp_rows(x) + # Forward-fill onto the full business-day calendar so bond-market holidays + # (when equities still trade) don't leave the covariate short of the origin. + x = _business_daily_ffill(x) + return _apply_one_business_day_feature_lag(x) + + +def _build_daily_fred_return_feature( + fred_id: str, + *, + cache_dir: Path, + refresh: bool, +) -> pd.DataFrame: + x = _fred_frame(fred_id, cache_dir=cache_dir, refresh=refresh) + x = _drop_weekend_timestamp_rows(x) + x = x[x["value"] > 0].reset_index(drop=True) + # Forward-fill the *level* onto the full business-day calendar first, so a + # bond-market holiday becomes a 0-return business day rather than a gap that + # ends the covariate before the target origin. + x = _business_daily_ffill(x) + x["value"] = np.log(x["value"] / x["value"].shift(1)) + x = x.dropna(subset=["value"]).reset_index(drop=True) + return _apply_one_business_day_feature_lag(x) + + +def _build_first_available_daily_fred_return_feature( + fred_ids: list[str], + *, + cache_dir: Path, + refresh: bool, +) -> tuple[pd.DataFrame, str]: + """Try multiple FRED ids and return the first one that fetches successfully.""" + last_error: Exception | None = None + for fred_id in fred_ids: + try: + frame = _build_daily_fred_return_feature(fred_id, cache_dir=cache_dir, refresh=refresh) + return frame, fred_id + except (RuntimeError, ValueError) as exc: + last_error = exc + continue + ids = ", ".join(fred_ids) + raise RuntimeError( + f"Could not fetch any configured gold FRED series ({ids}). " + "Check FRED availability/API key or override the gold covariate setup." + ) from last_error + + +def build_baa10y_multivariate_service( # noqa: PLR0912, PLR0915 + *, + windows: tuple[int, ...] = BAA10Y_CHANGE_WINDOWS, + include_covariates: bool = True, + covariate_series_ids: list[str] | None = None, + strict_covariates: bool = False, + refresh: bool = False, + start: str = "2000-01-01", + end: str | None = None, + yahoo_cache_dir: Path | None = None, + fred_cache_dir: Path | None = None, +) -> DataService: + """Build DataService with BAA10Y target plus optional leak-safe covariates. + + Parameters + ---------- + strict_covariates : bool + If ``True``, any covariate fetch/build failure raises immediately. + If ``False`` (default), unavailable covariates are skipped with a warning. + """ + _load_fred_dotenv() + # Only forward ``cache_path`` when the caller supplies one. Passing ``None`` + # would shadow the single-variable default (repo ``data/yahoo/sp500_gspc.parquet``) + # and force every notebook run to hit Yahoo Finance live. + + fred_dir = _as_absolute_cache( + fred_cache_dir or DEFAULT_FRED_CACHE_DIR + ) + if fred_dir is None: + raise RuntimeError("Could not resolve FRED cache directory.") + + fred_dir.mkdir(parents=True, exist_ok=True) + + svc = build_baa10y_target_service( + windows=windows, + refresh=refresh, + start=start, + end=end, + fred_cache_dir=fred_dir, + ) + if not include_covariates: + return svc + + desired = covariate_series_ids or DEFAULT_COVARIATE_SERIES_IDS + desired_set = set(desired) + + yahoo_dir = _as_absolute_cache(yahoo_cache_dir or DEFAULT_YAHOO_CACHE_DIR) + if yahoo_dir is None or fred_dir is None: + raise RuntimeError("Could not resolve yahoo/fred cache directories.") + yahoo_dir.mkdir(parents=True, exist_ok=True) + + def _handle_covariate_error(series_id: str, exc: Exception) -> None: + if strict_covariates: + raise RuntimeError(f"Failed to build required covariate {series_id!r}.") from exc + warnings.warn( + f"Skipping unavailable covariate {series_id!r}: {exc}", + stacklevel=2, + ) + + if SERIES_ID_VIX_LEVEL in desired_set or SERIES_ID_VIX_CHANGE in desired_set: + try: + vix_close = _load_yahoo_close_frame( + VIX_TICKER, + start=start, + end=end, + cache_dir=yahoo_dir, + refresh=refresh, + ) + if SERIES_ID_VIX_LEVEL in desired_set: + vix_level = _apply_one_business_day_feature_lag(_to_level_feature_from_daily(vix_close)) + svc.register( + SERIES_ID_VIX_LEVEL, + StaticFrameAdapter(vix_level), + SeriesMetadata( + series_id=SERIES_ID_VIX_LEVEL, + description="CBOE VIX close level, lagged 1 business day", + source=f"Yahoo Finance ({VIX_TICKER})", + units="index-level", + frequency="B", + table_id="yahoo:^VIX:close-l1b", + ), + ) + if SERIES_ID_VIX_CHANGE in desired_set: + vix_change = _apply_one_business_day_feature_lag(_to_log_return_feature(vix_close)) + svc.register( + SERIES_ID_VIX_CHANGE, + StaticFrameAdapter(vix_change), + SeriesMetadata( + series_id=SERIES_ID_VIX_CHANGE, + description="CBOE VIX close-to-close log return, lagged 1 business day", + source=f"Yahoo Finance ({VIX_TICKER}), derived", + units="log-return", + frequency="B", + table_id="yahoo:^VIX:log-return-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + if SERIES_ID_VIX_LEVEL in desired_set: + _handle_covariate_error(SERIES_ID_VIX_LEVEL, exc) + if SERIES_ID_VIX_CHANGE in desired_set: + _handle_covariate_error(SERIES_ID_VIX_CHANGE, exc) + + if SERIES_ID_NASDAQ_RETURN in desired_set: + try: + nasdaq_close = _load_yahoo_close_frame( + NASDAQ_TICKER, + start=start, + end=end, + cache_dir=yahoo_dir, + refresh=refresh, + ) + nasdaq_ret = _apply_one_business_day_feature_lag(_to_log_return_feature(nasdaq_close)) + svc.register( + SERIES_ID_NASDAQ_RETURN, + StaticFrameAdapter(nasdaq_ret), + SeriesMetadata( + series_id=SERIES_ID_NASDAQ_RETURN, + description="NASDAQ Composite close-to-close log return, lagged 1 business day", + source=f"Yahoo Finance ({NASDAQ_TICKER}), derived", + units="log-return", + frequency="B", + table_id="yahoo:^IXIC:log-return-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_NASDAQ_RETURN, exc) + + if SERIES_ID_10Y_YIELD in desired_set: + try: + dgs10 = _build_daily_fred_level_feature("DGS10", cache_dir=fred_dir, refresh=refresh) + svc.register( + SERIES_ID_10Y_YIELD, + StaticFrameAdapter(dgs10), + SeriesMetadata( + series_id=SERIES_ID_10Y_YIELD, + description="US 10-year Treasury yield level, lagged 1 business day", + source="FRED (DGS10)", + units="percent", + frequency="B", + table_id="fred:DGS10:l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_10Y_YIELD, exc) + + if SERIES_ID_2Y10Y_SPREAD in desired_set: + try: + dgs10 = _fred_frame("DGS10", cache_dir=fred_dir, refresh=refresh) + dgs2 = _fred_frame("DGS2", cache_dir=fred_dir, refresh=refresh) + spread = pd.merge( + dgs10[["timestamp", "value"]], + dgs2[["timestamp", "value"]], + on="timestamp", + how="inner", + suffixes=("_10y", "_2y"), + ) + spread["value"] = spread["value_10y"] - spread["value_2y"] + spread["released_at"] = pd.to_datetime(spread["timestamp"]) + pd.offsets.BDay(1) + # Forward-fill onto the full business-day calendar (bond-holiday safe). + spread = _business_daily_ffill(spread[["timestamp", "value", "released_at"]]) + spread = _apply_one_business_day_feature_lag(spread) + svc.register( + SERIES_ID_2Y10Y_SPREAD, + StaticFrameAdapter(spread), + SeriesMetadata( + series_id=SERIES_ID_2Y10Y_SPREAD, + description="US 10Y minus 2Y Treasury spread, lagged 1 business day", + source="FRED (DGS10, DGS2), derived", + units="percent-points", + frequency="B", + table_id="fred:DGS10-DGS2:l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_2Y10Y_SPREAD, exc) + + if SERIES_ID_FED_FUNDS in desired_set: + try: + fed = _build_daily_fred_level_feature("DFF", cache_dir=fred_dir, refresh=refresh) + svc.register( + SERIES_ID_FED_FUNDS, + StaticFrameAdapter(fed), + SeriesMetadata( + series_id=SERIES_ID_FED_FUNDS, + description="Effective federal funds rate, lagged 1 business day", + source="FRED (DFF)", + units="percent", + frequency="B", + table_id="fred:DFF:l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_FED_FUNDS, exc) + + if SERIES_ID_CPI_INFLATION_CHANGE in desired_set: + try: + cpi = _build_monthly_cpi_mom_feature( + cache_dir=fred_dir, + refresh=refresh, + start=start, + end=end, + ) + svc.register( + SERIES_ID_CPI_INFLATION_CHANGE, + StaticFrameAdapter(cpi), + SeriesMetadata( + series_id=SERIES_ID_CPI_INFLATION_CHANGE, + description="US CPI MoM log change, conservative release lag + 1B feature lag", + source="FRED (CPIAUCSL), derived", + units="log-change", + frequency="B", + table_id="fred:CPIAUCSL:mom-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_CPI_INFLATION_CHANGE, exc) + + if SERIES_ID_UNEMPLOYMENT in desired_set: + try: + unemp = _build_monthly_unemployment_feature( + cache_dir=fred_dir, + refresh=refresh, + start=start, + end=end, + ) + svc.register( + SERIES_ID_UNEMPLOYMENT, + StaticFrameAdapter(unemp), + SeriesMetadata( + series_id=SERIES_ID_UNEMPLOYMENT, + description="US unemployment rate, conservative release lag + 1B feature lag", + source="FRED (UNRATE)", + units="percent", + frequency="B", + table_id="fred:UNRATE:l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_UNEMPLOYMENT, exc) + + if SERIES_ID_OIL_RETURN in desired_set: + try: + oil = _build_daily_fred_return_feature("DCOILWTICO", cache_dir=fred_dir, refresh=refresh) + svc.register( + SERIES_ID_OIL_RETURN, + StaticFrameAdapter(oil), + SeriesMetadata( + series_id=SERIES_ID_OIL_RETURN, + description="WTI oil spot log return, lagged 1 business day", + source="FRED (DCOILWTICO), derived", + units="log-return", + frequency="B", + table_id="fred:DCOILWTICO:log-return-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_OIL_RETURN, exc) + + if SERIES_ID_GOLD_RETURN in desired_set: + try: + gold, gold_source_id = _build_first_available_daily_fred_return_feature( + ["GOLDAMGBD228NLBM", "GOLDPMGBD228NLBM"], + cache_dir=fred_dir, + refresh=refresh, + ) + svc.register( + SERIES_ID_GOLD_RETURN, + StaticFrameAdapter(gold), + SeriesMetadata( + series_id=SERIES_ID_GOLD_RETURN, + description="Gold fix log return, lagged 1 business day", + source=f"FRED ({gold_source_id}), derived", + units="log-return", + frequency="B", + table_id=f"fred:{gold_source_id}:log-return-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_GOLD_RETURN, exc) + + if SERIES_ID_DOLLAR_INDEX_RETURN in desired_set: + try: + dxy = _build_daily_fred_return_feature("DTWEXBGS", cache_dir=fred_dir, refresh=refresh) + svc.register( + SERIES_ID_DOLLAR_INDEX_RETURN, + StaticFrameAdapter(dxy), + SeriesMetadata( + series_id=SERIES_ID_DOLLAR_INDEX_RETURN, + description="Trade-weighted dollar index log return, lagged 1 business day", + source="FRED (DTWEXBGS), derived", + units="log-return", + frequency="B", + table_id="fred:DTWEXBGS:log-return-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(SERIES_ID_DOLLAR_INDEX_RETURN, exc) + + return svc + + +__all__ = [ + "DEFAULT_COVARIATE_SERIES_IDS", + "FRED_PREFETCH_REGISTRY", + "FRED_SERIES_IDS_FOR_PREFETCH", + "SERIES_ID_10Y_YIELD", + "SERIES_ID_2Y10Y_SPREAD", + "SERIES_ID_CPI_INFLATION_CHANGE", + "SERIES_ID_DOLLAR_INDEX_RETURN", + "SERIES_ID_FED_FUNDS", + "SERIES_ID_GOLD_RETURN", + "SERIES_ID_NASDAQ_RETURN", + "SERIES_ID_OIL_RETURN", + "SERIES_ID_UNEMPLOYMENT", + "SERIES_ID_VIX_CHANGE", + "SERIES_ID_VIX_LEVEL", + "BAA10Y_FRED_ID", + "BAA10Y_CHANGE_SERIES_ID", + "BAA10Y_CHANGE_TARGETS", + "BAA10Y_CHANGE_WINDOWS", + "BAA10Y_WINDOW_LABELS", + "StaticFrameAdapter", + "baa10y_change_series_id", + "build_baa10y_target_service", + "build_baa10y_multivariate_service", +] diff --git a/implementations/BAA10Y_forecasting /leaderboard.py b/implementations/BAA10Y_forecasting /leaderboard.py new file mode 100644 index 00000000..ca4e84c5 --- /dev/null +++ b/implementations/BAA10Y_forecasting /leaderboard.py @@ -0,0 +1,205 @@ +"""Leaderboard rows for the multivariate S&P 500 experiment. + +The notebook runs each predictor with the shared +:func:`~aieng.forecasting.evaluation.cached_multi_backtest` / +:func:`~aieng.forecasting.evaluation.multi_evaluate` helpers, which return a +``dict`` keyed by ``task_id`` (one task per horizon, targeting +``sp500_logret_{N}b``). :func:`build_leaderboard` turns the +``{predictor_id: {task_id: result}}`` mapping those produce into the +``RESULTS_DF`` frame consumed by +:func:`~sp500_forecasting.plots.display_multivariate_backtest_leaderboard` +(one row per predictor × horizon, with mean CRPS and next-direction metrics). + +Which predictors run, and all their hyperparameters, are configured in the +notebook — there is no model registry or dispatch here. +""" + +from __future__ import annotations + +from typing import TYPE_CHECKING + +import pandas as pd +from aieng.forecasting.data.service import DataService +from aieng.forecasting.evaluation import BacktestResult, EvalResult +from sp500_forecasting.analysis import ( + build_direction_eval_frame, + direction_classification_metrics, +) + + +if TYPE_CHECKING: + from aieng.forecasting.evaluation.prediction import Prediction + + +_NAN_DIR: dict[str, float | int] = { + "dir_precision_up": float("nan"), + "dir_recall_up": float("nan"), + "dir_f1_up": float("nan"), + "dir_accuracy": float("nan"), + "dir_roc_auc_prob_up": float("nan"), + "dir_n_eval": 0, +} + + +def build_return_compare_frame( + predictions: list[Prediction], + data_service: DataService, + target_series_id: str, +) -> pd.DataFrame: + """One row per scored prediction: realised return vs forecast median and 5–95% band. + + Returns are kept on the target (log-return) scale; the notebook renders them + as percentages. Rows whose ``forecast_date`` has no realised observation are + dropped. + """ + from datetime import datetime, timezone # noqa: PLC0415 + + from aieng.forecasting.evaluation.prediction import ContinuousForecast # noqa: PLC0415 + + as_of_now = datetime.now(tz=timezone.utc).replace(tzinfo=None) + full = data_service.get_series(target_series_id, as_of=as_of_now).copy() + full["timestamp"] = pd.to_datetime(full["timestamp"]) + lookup = full.set_index("timestamp")["value"] + + rows: list[dict[str, object]] = [] + for pred in predictions: + if not isinstance(pred.payload, ContinuousForecast): + continue + ts = pd.Timestamp(pred.forecast_date) + if ts not in lookup.index: + continue + qmap = pred.payload.quantiles + med = qmap.get(0.5, pred.payload.point_forecast) + rows.append( + { + "session": ts, + "actual_return": float(lookup.loc[ts]), + "forecast_return": float(med), + "forecast_return_p05": float(qmap.get(0.05, float("nan"))), + "forecast_return_p95": float(qmap.get(0.95, float("nan"))), + } + ) + if not rows: + return pd.DataFrame() + return pd.DataFrame(rows).sort_values("session").reset_index(drop=True) + + +def _direction_metrics_row( + *, + predictions: list[Prediction], + data_service: DataService, + target_series_id: str, +) -> dict[str, float | int]: + eval_df = build_direction_eval_frame( + predictions, + target_series_id=target_series_id, + data_service=data_service, + ) + if eval_df.empty: + return dict(_NAN_DIR) + m = direction_classification_metrics(eval_df) + return { + "dir_precision_up": float(m.get("precision_up", float("nan"))), + "dir_recall_up": float(m.get("recall_up", float("nan"))), + "dir_f1_up": float(m.get("f1_up", float("nan"))), + "dir_accuracy": float(m.get("accuracy", float("nan"))), + "dir_roc_auc_prob_up": float(m.get("roc_auc_prob_up", float("nan"))), + "dir_n_eval": int(m.get("n", 0)), + } + + +def _leaderboard_row( + *, + predictor_id: str, + result: BacktestResult | EvalResult, + data_service: DataService, + covariates: list[str], + label: str, +) -> dict[str, object]: + """Build one leaderboard row from a single (predictor, task) result.""" + # BacktestResult carries ``spec``; EvalResult carries ``eval_spec`` — both + # expose the same ``.task``. + spec = getattr(result, "spec", None) or result.eval_spec + target_series_id = spec.task.target_series_id + dir_row = _direction_metrics_row( + predictions=result.predictions, + data_service=data_service, + target_series_id=target_series_id, + ) + row: dict[str, object] = { + "horizon": int(max(spec.task.horizons)), + "target": target_series_id, + "model": label, + "uses_covariates": bool(covariates), + "n_covariates": len(covariates), + "covariates": ", ".join(covariates) if covariates else "—", + "predictor_id": predictor_id, + "mean_crps": float(result.mean_score), + "n_scores": int(len(result.scores)), + "n_predictions": int(len(result.predictions)), + "skipped_origins": int(getattr(result, "skipped_origins", 0)), + **dir_row, + } + run_number = getattr(result, "run_number", None) + if run_number is not None: + row["run_number"] = int(run_number) + return row + + +def build_leaderboard( + results_by_predictor: dict[str, dict[str, BacktestResult | EvalResult]], + data_service: DataService, + *, + covariates_by_predictor: dict[str, list[str]] | None = None, + labels_by_predictor: dict[str, str] | None = None, +) -> pd.DataFrame: + """Assemble a ``RESULTS_DF`` leaderboard from cached backtest/eval results. + + Parameters + ---------- + results_by_predictor + ``{predictor_id: {task_id: result}}`` as returned by looping + :func:`~aieng.forecasting.evaluation.cached_multi_backtest` (or + :func:`~aieng.forecasting.evaluation.multi_evaluate`) over a list of + predictors. Backtest and eval results are both accepted; eval rows get a + ``run_number`` column. + data_service + Service that registers the target series (used for the next-direction + metrics that align each forecast with its realised return). + covariates_by_predictor + Optional ``{predictor_id: [series_id, ...]}`` so the ``uses_covariates`` / + ``covariates`` columns reflect each predictor's covariate panel. A + predictor absent from the mapping is treated as target-only. + labels_by_predictor + Optional ``{predictor_id: short_label}`` driving the ``model`` column (and + the bar-chart labels). Falls back to ``predictor_id``. + + Returns + ------- + pandas.DataFrame + One row per (predictor, horizon), sorted by ``(horizon, mean_crps)``. + """ + covariates_by_predictor = covariates_by_predictor or {} + labels_by_predictor = labels_by_predictor or {} + + rows: list[dict[str, object]] = [] + for predictor_id, task_results in results_by_predictor.items(): + for result in task_results.values(): + rows.append( + _leaderboard_row( + predictor_id=predictor_id, + result=result, + data_service=data_service, + covariates=list(covariates_by_predictor.get(predictor_id, [])), + label=labels_by_predictor.get(predictor_id, predictor_id), + ) + ) + if not rows: + return pd.DataFrame() + return pd.DataFrame(rows).sort_values(["horizon", "mean_crps"], na_position="last").reset_index(drop=True) + + +__all__ = [ + "build_leaderboard", + "build_return_compare_frame", +] diff --git a/implementations/BAA10Y_forecasting /plots.py b/implementations/BAA10Y_forecasting /plots.py new file mode 100644 index 00000000..05da068b --- /dev/null +++ b/implementations/BAA10Y_forecasting /plots.py @@ -0,0 +1,238 @@ +"""Matplotlib helpers for the multivariate S&P 500 demo notebook. + +Keeps the notebook narrative-focused; style matches +``food_price_forecasting/plots.py`` (matplotlib only, ``(fig, ax)`` returns). +""" + +from __future__ import annotations + +from collections.abc import Mapping +from datetime import datetime, timezone +from typing import TYPE_CHECKING + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +from matplotlib.axes import Axes +from matplotlib.figure import Figure +from matplotlib.ticker import FuncFormatter +from sp500_forecasting.analysis import style_results_dataframe +from sp500_forecasting.data import SP500_LOG_RETURN_SERIES_ID + + +if TYPE_CHECKING: + from aieng.forecasting.data.service import DataService + + +def plot_sp500_log_return_recent( + data_service: DataService, + *, + series_id: str = SP500_LOG_RETURN_SERIES_ID, + n_trading_days: int = 756, + title: str | None = None, +) -> tuple[Figure, Axes]: + """Plot the last *n_trading_days* observed close-to-close log returns. + + Parameters + ---------- + data_service + Any service that registers ``series_id`` (typically ``svc_no_cov``). + series_id + Canonical log-return series id (defaults to the 1-business-day return). + n_trading_days + How many most recent rows to show (default ~3y of sessions). + title + Figure title; a default is used when ``None``. + """ + as_of = datetime.now(tz=timezone.utc).replace(tzinfo=None) + df = data_service.get_series(series_id, as_of=as_of) + plot_df = df.sort_values("timestamp").tail(int(n_trading_days)).copy() + plot_df["timestamp"] = pd.to_datetime(plot_df["timestamp"]) + + fig, ax = plt.subplots(figsize=(10, 3.5), layout="constrained") + ax.axhline(0.0, color="0.45", linewidth=0.8, linestyle="--", zorder=1) + ax.fill_between( + plot_df["timestamp"], + 0.0, + plot_df["value"], + where=plot_df["value"] >= 0, + interpolate=True, + alpha=0.35, + color="#1f77b4", + linewidth=0, + ) + ax.fill_between( + plot_df["timestamp"], + 0.0, + plot_df["value"], + where=plot_df["value"] < 0, + interpolate=True, + alpha=0.35, + color="#d62728", + linewidth=0, + ) + ax.plot(plot_df["timestamp"], plot_df["value"], color="0.15", linewidth=0.6, zorder=2) + ax.set_xlabel("Session date (target timestamp)") + ax.set_ylabel("Log return") + ttl = title or ( + f"Observed {series_id} (last {len(plot_df)} sessions)\nPositive: index up over the window; negative: down." + ) + ax.set_title(ttl, fontsize=11) + ax.grid(True, alpha=0.25) + fig.autofmt_xdate() + return fig, ax + + +def plot_mean_crps_leaderboard( + results_df: pd.DataFrame, + *, + value_col: str = "mean_crps", + label_col: str = "predictor_id", + title: str = "Mean CRPS by run (lower is better)", +) -> tuple[Figure, Axes]: + """Horizontal bar chart from a ``RESULTS_DF``-style frame (single horizon).""" + d = results_df.dropna(subset=[value_col]).copy() + fig, ax = plt.subplots(figsize=(8.5, max(2.5, 0.45 * len(d) + 1)), layout="constrained") + + if d.empty: + ax.text(0.5, 0.5, "No rows with finite mean CRPS to plot.", ha="center", va="center") + ax.set_axis_off() + return fig, ax + + d = d.sort_values(value_col, ascending=True) + y = np.arange(len(d)) + viridis = plt.get_cmap("viridis") + colors = viridis(np.linspace(0.25, 0.85, len(d))) + ax.barh(y, d[value_col].to_numpy(dtype=float), color=colors, height=0.65) + ax.set_yticks(y, d[label_col].astype(str).to_list()) + ax.invert_yaxis() + ax.set_xlabel("Mean CRPS") + ax.set_title(title, fontsize=11) + ax.grid(True, axis="x", alpha=0.3) + for yi, val in zip(y, d[value_col].to_numpy(dtype=float), strict=True): + ax.text(float(val), float(yi), f" {val:.5f}", va="center", fontsize=9, color="0.2") + return fig, ax + + +def plot_mean_crps_by_horizon( + results_df: pd.DataFrame, + *, + label_col: str = "model", + title: str = "Mean CRPS by method and horizon (lower is better)", +) -> tuple[Figure, list[Axes]]: + """Small-multiples: one CRPS bar panel per horizon, methods sorted within each. + + Expects a combined frame from + :func:`~sp500_forecasting.leaderboard.build_leaderboard` (with a ``horizon`` + column). Makes the "predictability decays with horizon" story visible. + """ + d = results_df.dropna(subset=["mean_crps"]).copy() + horizons = sorted(d["horizon"].unique()) if "horizon" in d.columns and not d.empty else [] + n = len(horizons) + fig, axes = plt.subplots(1, max(n, 1), figsize=(5.0 * max(n, 1), 4.0), layout="constrained", squeeze=False) + ax_row = list(axes[0]) + + if not horizons: + ax_row[0].text(0.5, 0.5, "No rows with finite mean CRPS to plot.", ha="center", va="center") + ax_row[0].set_axis_off() + return fig, ax_row + + cmap = plt.get_cmap("viridis") + for ax, h in zip(ax_row, horizons): + dh = d[d["horizon"] == h].sort_values("mean_crps", ascending=True) + y = np.arange(len(dh)) + ax.barh(y, dh["mean_crps"].to_numpy(dtype=float), color=cmap(np.linspace(0.25, 0.85, len(dh))), height=0.65) + ax.set_yticks(y, dh[label_col].astype(str).to_list(), fontsize=8) + ax.invert_yaxis() + ax.set_xlabel("Mean CRPS") + ax.set_title(f"h = {h} business day(s)", fontsize=10) + ax.grid(True, axis="x", alpha=0.3) + fig.suptitle(title, fontsize=12) + return fig, ax_row + + +def plot_return_forecast_vs_actual_multi( + compare_by_run: Mapping[str, pd.DataFrame], + *, + title: str | None = None, +) -> tuple[Figure, Axes]: + """Realised return (once) vs each run's median forecast, rendered as percent. + + Each value frame is from + :func:`~sp500_forecasting.leaderboard.build_return_compare_frame` for a + single horizon. Insertion order controls legend order. + """ + fig, ax = plt.subplots(figsize=(12, 5.0), layout="constrained", facecolor="0.98") + ax.set_facecolor("#fafafa") + + items = [(k, df) for k, df in compare_by_run.items() if df is not None and not df.empty] + if not items: + ax.text(0.5, 0.5, "No rows to plot (check price cache and backtest window).", ha="center", va="center") + ax.set_axis_off() + return fig, ax + + base = items[0][1].copy() + base["session"] = pd.to_datetime(base["session"]) + base = base.sort_values("session") + ax.axhline(0.0, color="0.5", linewidth=0.8, linestyle="--", zorder=1) + ax.plot( + base["session"].to_numpy(), + 100.0 * base["actual_return"].to_numpy(dtype=float), + color="#0d47a1", + linewidth=2.2, + marker="o", + markersize=4, + label="Actual", + zorder=5, + ) + + cmap = plt.get_cmap("tab10") + for i, (run_key, d0) in enumerate(items): + d = d0.copy() + d["session"] = pd.to_datetime(d["session"]) + d = d.sort_values("session") + ax.plot( + d["session"].to_numpy(), + 100.0 * d["forecast_return"].to_numpy(dtype=float), + color=cmap(i % 10), + linewidth=1.6, + linestyle="--", + marker="s", + markersize=3, + label=run_key.replace("_", " "), + zorder=4 + i * 0.01, + ) + + ttl = title or "S&P 500 — forecast vs realised return" + ax.set_title(ttl, fontsize=12, fontweight="600", color="0.15") + ax.set_xlabel("Session date (forecast resolution)", fontsize=10, color="0.25") + ax.set_ylabel("Return (%)", fontsize=10, color="0.25") + ax.legend(loc="upper left", framealpha=0.92, fontsize=8, ncol=2) + ax.grid(True, alpha=0.28, linestyle="-", linewidth=0.6) + ax.tick_params(axis="both", labelsize=9, colors="0.35") + ax.yaxis.set_major_formatter(FuncFormatter(lambda v, _: f"{v:,.1f}%")) + fig.autofmt_xdate() + for spine in ("top", "right"): + ax.spines[spine].set_visible(False) + return fig, ax + + +_RESULTS_EMPTY_HINT = ( + "RESULTS_DF is empty — add at least one predictor to ``all_predictors`` in the " + "notebook's predictors cell, then re-run the backtest loop." +) + + +def display_multivariate_backtest_leaderboard(results_df: pd.DataFrame) -> None: + """Styled ``RESULTS_DF`` plus mean-CRPS bar charts (faceted by horizon when present).""" + from IPython.display import display # noqa: PLC0415 — optional notebook dependency + + if results_df.empty: + print(_RESULTS_EMPTY_HINT) + return + display(style_results_dataframe(results_df)) # type: ignore[no-untyped-call] + if "horizon" in results_df.columns and results_df["horizon"].nunique() > 1: + plot_mean_crps_by_horizon(results_df) + else: + plot_mean_crps_leaderboard(results_df, title="Mean CRPS — log return (target scale)") + plt.show() diff --git a/implementations/BAA10Y_forecasting /predictors/__init__.py b/implementations/BAA10Y_forecasting /predictors/__init__.py new file mode 100644 index 00000000..e735f74b --- /dev/null +++ b/implementations/BAA10Y_forecasting /predictors/__init__.py @@ -0,0 +1,19 @@ +"""Tuned predictor recipes for the multivariate S&P 500 experiment. + +Each module here builds a fully-configured predictor instance for the S&P 500 +use case. Recipes pair a task-agnostic predictor from +:mod:`aieng.forecasting.methods` with use-case-specific configuration: prompt +overrides (what the series is and how returns behave), history windows, sampling +budgets, the optional covariate panel, and a +:attr:`~aieng.forecasting.methods.llm_processes.base.LLMPredictorConfig.variant_tag` +that keeps cached artifacts distinct from ad-hoc bare-config runs. + +The conventional numerical methods (naive floor, ETS/Kalman/AutoARIMA, Darts +linear regression / LightGBM) need no recipe — the notebook instantiates them +directly from :mod:`aieng.forecasting.methods`. +""" + +from .llmp_sampled_trajectory import build_sp500_llmp_sampled_trajectory + + +__all__ = ["build_sp500_llmp_sampled_trajectory"] diff --git a/implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py b/implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py new file mode 100644 index 00000000..a8cd8f18 --- /dev/null +++ b/implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py @@ -0,0 +1,111 @@ +"""S&P 500 recipe: sampled-trajectory LLMP (target-only and with-covariates). + +This file is intentionally small and explicit so notebook readers can open it as +a reference recipe. The reusable method lives in ``aieng.forecasting``; this +module captures the S&P 500 prompt framing (what the series *is* and how returns +behave), the default sampling budget, the history window, and the cache tag used +by the experiment. + +Two variants share this builder: + +- **target-only** — ``covariate_series_ids=None``; the LLM sees only the return + history. +- **with-covariates** — pass the covariate panel; the predictor serializes + labeled covariate-history blocks (VIX, yields, …) into the prompt, so its CRPS + gap vs the target-only variant answers "can an LLM use the same exogenous + observations the ML methods do?". +""" + +from __future__ import annotations + +from aieng.forecasting.methods.llm_processes import ( + SampledTrajectoryLLMPredictor, + SampledTrajectoryLLMPredictorConfig, +) +from aieng.forecasting.models import LITE_MODEL + + +_DEFAULT_MODEL = LITE_MODEL +_DEFAULT_N_SAMPLES = 10 +_DEFAULT_HISTORY_WINDOW = 64 +_RECIPE_FAMILY = "sp500_v1" + +_SERIES_DESCRIPTION = ( + "Series: S&P 500 (^GSPC) close-to-close cumulative log return over a fixed " + "number of business days.\n" + "Units: log-return (a value of 0.01 is roughly a +1% move).\n" + "Frequency: business days (Mon-Fri)." +) + +_USER_PROMPT_SUFFIX = ( + "Notes for this series:\n" + "- Daily index returns are close to a martingale: the *level* of the return " + "is barely predictable, so point forecasts should sit near 0 and the value " + "is in the *spread* (volatility and tail risk), not a confident direction.\n" + "- Returns cluster in volatility — calm and turbulent stretches persist — so " + "recent realised dispersion is the best guide to the width of your interval.\n" + "- Keep the distribution roughly symmetric about ~0 unless the recent history " + "or the covariate blocks give a clear reason to skew it; avoid extrapolating " + "a short run of up or down days into a trend." +) + + +def build_sp500_llmp_sampled_trajectory( + *, + model: str = _DEFAULT_MODEL, + n_samples: int = _DEFAULT_N_SAMPLES, + history_window: int | None = _DEFAULT_HISTORY_WINDOW, + covariate_series_ids: list[str] | None = None, + reasoning_effort: str | None = None, + max_tokens: int = 16384, + variant_tag: str | None = None, +) -> SampledTrajectoryLLMPredictor: + """Return the S&P 500 sampled-trajectory LLMP recipe. + + The model is a normal parameter because the base LLMP ``predictor_id`` + already includes it. The recipe tag records the S&P 500 prompt/config family, + whether the covariate panel is in context, and the cache-relevant knobs that + are not otherwise visible in the ID. + + Parameters + ---------- + model : str + Model identifier. Defaults to the lite model (``gemini-3.1-flash-lite-preview``). + n_samples : int + Number of trajectory samples to draw per prediction call. + history_window : int or None + Number of most-recent business days to include in context. + covariate_series_ids : list[str] or None + When provided, the covariate panel is serialized into the prompt + (the "with-covariates" variant). ``None`` is the target-only variant. + reasoning_effort : str or None + Provider reasoning budget. ``None`` (default) uses the provider default; + the Vector proxy rejects ``'disable'``/``'low'``. + max_tokens : int, default=16384 + Per-call output token budget. The generous default prevents truncation + on thinking models where thinking tokens consume the same budget via the + OpenAI-compatible proxy. The model only generates tokens it needs, so + non-thinking models are unaffected in cost. + variant_tag : str or None + Override the cache tag suffix. + """ + history_tag = "hfull" if history_window is None else f"h{history_window}" + sample_count_tag = f"n{n_samples}" + covariate_tag = "cov" if covariate_series_ids else "target" + resolved_variant_tag = variant_tag or f"{_RECIPE_FAMILY}_{covariate_tag}_{history_tag}_{sample_count_tag}" + + config = SampledTrajectoryLLMPredictorConfig( + model=model, + n_samples=n_samples, + history_window=history_window, + covariate_series_ids=covariate_series_ids, + reasoning_effort=reasoning_effort, + max_tokens=max_tokens, + series_description=_SERIES_DESCRIPTION, + user_prompt_suffix=_USER_PROMPT_SUFFIX, + variant_tag=resolved_variant_tag, + ) + return SampledTrajectoryLLMPredictor(config) + + +__all__ = ["build_sp500_llmp_sampled_trajectory"] diff --git a/implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml b/implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml new file mode 100644 index 00000000..e1ab30ed --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml @@ -0,0 +1,48 @@ +# Main backtest spec — weekly origins across 2025 (post-cutoff). +# +# 2025 is after the Gemini training cutoff (~Jan 2025), so this is the window +# where the conventional methods AND the LLM-Process can be compared *fairly* — +# the LLM has not memorised these outcomes. Mirrors the energy reference's 2025 +# backtest window. Use it for open iteration; spend the protected 2026 eval +# (`sp500_eval_2026.yaml`) sparingly on your finalists. +# +# This spec carries experiment design only (window + one single-horizon task per +# target). The predictor roster and hyperparameters live in the notebook. Note +# the LLMP predictors are token-heavy over ~50 weekly origins — the notebook lets +# you trim the predictor list (or widen the stride here) before enabling them. + +spec_id: sp500_backtest_2025 + +description: >- + Main multivariate comparison: weekly origins across 2025 (post-cutoff), + forecasting close-to-close cumulative returns at 1/5/21 business days. + +tasks: + - task_id: sp500_logret_1b + target_series_id: sp500_logret_1b + horizons: [1] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 1 business day ahead + (next-session return / direction). + + - task_id: sp500_logret_5b + target_series_id: sp500_logret_5b + horizons: [5] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 5 business days ahead + (forward 1-week return). + + - task_id: sp500_logret_21b + target_series_id: sp500_logret_21b + horizons: [21] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 21 business days ahead + (forward 1-month return). + +start: "2025-01-06" +end: "2025-12-22" +stride: 5 # weekly origins (~50) +warmup: 250 diff --git a/implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml b/implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml new file mode 100644 index 00000000..564368d0 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml @@ -0,0 +1,51 @@ +# Protected eval — held-out 2026 window, scored through multi_evaluate() with a budget. +# +# This is the honest scoreboard. 2026 is unambiguously after the Gemini training +# cutoff, so neither the numerical methods nor the LLM-Process can have seen the +# outcomes. Treat it as scarce: `max_runs` caps how many times the spec may be +# scored (via EvalTracker), so iterate on `sp500_backtest_2025.yaml` and spend +# this only on a curated set of finalists (chosen in the notebook's eval cell). +# +# Loaded as a MultiTargetEvalSpec: one single-horizon task per target, all under +# a single shared run budget. One multi_evaluate() call across all three +# horizons counts as ONE run against `max_runs` — the budget is keyed by +# `spec_id`, not per-horizon. The predictor roster lives in the notebook. + +spec_id: sp500_eval_2026 + +description: >- + Protected eval: weekly origins across early/mid 2026 (post-cutoff), + forecasting close-to-close cumulative returns at 1/5/21 business days. + +tasks: + - task_id: sp500_logret_1b + target_series_id: sp500_logret_1b + horizons: [1] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 1 business day ahead + (next-session return / direction). + + - task_id: sp500_logret_5b + target_series_id: sp500_logret_5b + horizons: [5] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 5 business days ahead + (forward 1-week return). + + - task_id: sp500_logret_21b + target_series_id: sp500_logret_21b + horizons: [21] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 21 business days ahead + (forward 1-month return). + +start: "2026-02-02" +end: "2026-03-23" # 8 weekly origins; all resolve at h=21 +stride: 5 # weekly origins +warmup: 250 + +# Budget cap: each multi_evaluate() call (all 3 horizons) counts as one run. +max_runs: 5 diff --git a/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml b/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml new file mode 100644 index 00000000..bc873e54 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml @@ -0,0 +1,53 @@ +# Smoke spec — fast laptop run over a short, post-cutoff (2025) window. +# +# Weekly origins in late 2025: after the Gemini training cutoff (~Jan 2025), so +# the LLM-Process rows in the notebook can be compared *fairly* against the +# conventional methods here. The notebook keeps its LLMP predictors ON for this +# window (the predictors cell gates them on a post-cutoff flag). +# +# This spec carries experiment design only — the window, stride/warmup, and one +# single-horizon task per target. WHICH predictors run (and all their +# hyperparameters, including the covariate panel) is configured in the notebook, +# not here. Each task targets `sp500_logret_{N}b` (the close-to-close cumulative +# log return over N business days), so forecasting it N steps ahead resolves to +# the forward N-session return. +# +# Prerequisites (warm caches to the present first): +# uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC +# uv run python scripts/fetch_fred.py # macro covariates + +spec_id: sp500_smoke + +description: >- + Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), + forecasting close-to-close cumulative returns at 1/5/21 business days. + +tasks: + - task_id: sp500_logret_1b + target_series_id: sp500_logret_1b + horizons: [1] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 1 business day ahead + (next-session return / direction). + + - task_id: sp500_logret_5b + target_series_id: sp500_logret_5b + horizons: [5] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 5 business days ahead + (forward 1-week return). + + - task_id: sp500_logret_21b + target_series_id: sp500_logret_21b + horizons: [21] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 21 business days ahead + (forward 1-month return). + +start: "2025-10-06" +end: "2025-11-14" +stride: 5 # weekly origins (~6) +warmup: 250 diff --git a/implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml b/implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml new file mode 100644 index 00000000..2e1f3c14 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml @@ -0,0 +1,50 @@ +# Regime-stress spec — the 2020 COVID crash, NUMERICAL METHODS ONLY. +# +# ⚠️ Keep the notebook's LLM-Process predictors OFF for this window. 2020 is +# BEFORE the Gemini training cutoff (~Jan 2025), so an LLM has effectively +# memorised these outcomes — scoring an LLMP here measures recall, not +# forecasting, and would silently flatter it in the comparison. The numerical +# methods are cutoff-safe by construction (they only see the series up to the +# origin), so this volatile window is a perfectly valid stress test *for them* — +# it's where a covariate edge is most visible. +# +# The notebook enforces "numerical only" in code: its predictors cell gates the +# LLMP variants on a post-cutoff flag that is False for this config. Use this to +# study "when do covariates help?" among the conventional methods; use the 2025 +# backtest / 2026 eval for anything involving the LLMP. + +spec_id: sp500_stress_2020 + +description: >- + COVID-crash regime stress (numerical methods only): daily origins Feb–Apr + 2020, forecasting close-to-close cumulative returns at 1/5/21 business days. + +tasks: + - task_id: sp500_logret_1b + target_series_id: sp500_logret_1b + horizons: [1] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 1 business day ahead + (next-session return / direction). + + - task_id: sp500_logret_5b + target_series_id: sp500_logret_5b + horizons: [5] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 5 business days ahead + (forward 1-week return). + + - task_id: sp500_logret_21b + target_series_id: sp500_logret_21b + horizons: [21] + frequency: B + description: >- + S&P 500 close-to-close cumulative log return, 21 business days ahead + (forward 1-month return). + +start: "2020-02-03" +end: "2020-04-30" +stride: 1 # daily origins across the crash +warmup: 250 diff --git a/implementations/BAA10Y_forecasting /starter_agent/__init__.py b/implementations/BAA10Y_forecasting /starter_agent/__init__.py new file mode 100644 index 00000000..3c4d9a72 --- /dev/null +++ b/implementations/BAA10Y_forecasting /starter_agent/__init__.py @@ -0,0 +1,19 @@ +"""S&P 500 starter agent — a fresh, hackable template for your own exploration. + +Exports the toggle-driven :class:`AgentConfig` factory, the predictor +convenience factory, and the self-contained prompt builder. See +``99_starter_agent.ipynb`` and ``agent.py``. +""" + +from sp500_forecasting.starter_agent.agent import ( + Sp500StarterPromptBuilder, + build_starter_agent_config, + build_starter_agent_predictor, +) + + +__all__ = [ + "Sp500StarterPromptBuilder", + "build_starter_agent_config", + "build_starter_agent_predictor", +] diff --git a/implementations/BAA10Y_forecasting /starter_agent/agent.py b/implementations/BAA10Y_forecasting /starter_agent/agent.py new file mode 100644 index 00000000..b02e5b3d --- /dev/null +++ b/implementations/BAA10Y_forecasting /starter_agent/agent.py @@ -0,0 +1,301 @@ +"""S&P 500 starter agent — a fresh, hackable template for your own exploration. + +This is the S&P 500 use case's **first** agent, and it is deliberately minimal: +a clean starting point with our common building blocks behind simple toggles — + +- **optional news search** (``enable_search``, on by default) — bounded, + cutoff-aware Google Search through the Vector proxy; +- **optional code execution** (``enable_code_exec``, off by default) — an E2B + Python sandbox; +- **two lightweight skills** (:mod:`skills/`) that are *tool-usage playbooks*: + how to get good results out of search and code execution. + +Everything routes through the Vector proxy — no direct provider keys. See +``planning-docs/vector-llm-proxy.md``. + +This use case had no agent to borrow a prompt builder from, so +:class:`Sp500StarterPromptBuilder` below is a small, self-contained serialiser — +read it, then extend it (more covariates, richer panels, report context). The +target is a single-horizon cumulative log return; the output is a probabilistic +forecast of that return. Pair this with ``99_starter_agent.ipynb``. + +Module-level ``__getattr__`` exposes ``root_agent`` lazily so ``adk web`` can +load this module for interactive (schema-free) use. +""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any, Callable + +import pandas as pd +from aieng.forecasting.data.context import ForecastContext +from aieng.forecasting.evaluation.prediction import STANDARD_QUANTILES +from aieng.forecasting.evaluation.task import ForecastingTask +from aieng.forecasting.methods.agentic import ( + AgentPredictor, + ContinuousAgentForecastOutput, + build_adk_agent, +) +from aieng.forecasting.methods.agentic.agent_factory import ( + AgentConfig, + CodeExecutionConfig, + ContextRetrievalConfig, +) +from aieng.forecasting.models import LITE_MODEL +from pydantic import BaseModel + + +# Skills live next to this module. +_SKILLS_ROOT = Path(__file__).parent / "skills" +_FORECASTING_SKILL = _SKILLS_ROOT / "forecasting" +_RESEARCH_SKILL = _SKILLS_ROOT / "research-playbook" +_CODE_ANALYSIS_SKILL = _SKILLS_ROOT / "code-analysis-playbook" + + +# --------------------------------------------------------------------------- +# Prompt builder (self-contained — this use case has no analyst_agent to reuse) +# --------------------------------------------------------------------------- + + +class Sp500StarterPromptBuilder(BaseModel): + """Serialise the target log-return series (+ optional covariate snapshot). + + Minimal on purpose: the recent history of the cumulative-log-return target, + the task spec, the exact quantile grid, and — when ``covariate_series_ids`` + are supplied and present in the context — the latest value of each covariate + as a leak-safe macro snapshot. Implements the + :class:`~aieng.forecasting.methods.agentic.predictor.ForecastPromptBuilder` + protocol structurally — extend it with richer covariate panels. + """ + + model_config = {"extra": "forbid"} + + history: int = 64 + covariate_series_ids: list[str] = [] + + def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str: + df = context.get_series(task.target_series_id).tail(self.history) + rows = ["date,log_return"] + [ + f"{pd.Timestamp(ts).date()},{float(v):.6f}" for ts, v in zip(df["timestamp"], df["value"]) + ] + + covariate_snapshot: dict[str, float] = {} + for cov_id in self.covariate_series_ids: + try: + cov_df = context.get_series(cov_id) + except Exception: # noqa: BLE001 — a missing covariate just drops out of the snapshot + continue + if not cov_df.empty: + covariate_snapshot[cov_id] = round(float(cov_df["value"].iloc[-1]), 6) + + payload: dict[str, Any] = { + "task": task.task_id, + "as_of": str(context.as_of)[:10], + "horizons": list(task.horizons), + "standard_quantiles": list(STANDARD_QUANTILES), + "target_summary": { + "last_log_return": float(df["value"].iloc[-1]), + "last_date": str(pd.Timestamp(df["timestamp"].iloc[-1]).date()), + "n_obs": int(len(df)), + }, + "target_history_csv": "\n".join(rows), + } + if covariate_snapshot: + payload["covariate_snapshot"] = covariate_snapshot + return json.dumps(payload, indent=2) + + +# --------------------------------------------------------------------------- +# System prompt +# --------------------------------------------------------------------------- + + +def _build_starter_instruction() -> str: + """Build the task-agnostic, skill-agnostic starter persona. + + Just the analyst's identity and how to behave — no output schema, no payload + contract, no skill or tool mechanics. ADK injects the name + description of + every attached skill (and every tool) into the system prompt, so the agent + already knows what it can load and call; repeating that here would only + duplicate dynamically-injected information. The forecasting *contract* lives + in the loadable ``forecasting`` skill. Edit the persona freely. + """ + return ( + "## Role\n\n" + "You are an equity-market analyst — fluent in the rate path and Fed " + "guidance, inflation and jobs data, volatility and credit conditions, " + "and how macro catalysts move the S&P 500. This is a starter agent: keep " + "your reasoning transparent and your claims honest, and remember returns " + "are close to a random walk.\n\n" + "## How to respond\n\n" + "- For open-ended questions, scenario analysis, or anything " + "conversational, answer directly and concisely — do NOT ask for a JSON " + "payload.\n" + "- When you are handed a task that asks for a structured probabilistic " + "forecast, produce a calibrated one." + ) + + +_STARTER_INSTRUCTION = _build_starter_instruction() + + +_CONTEXT_RETRIEVAL_INSTRUCTION = """\ +You are an equity-market intelligence specialist with web search. + +Return a concise structured markdown summary (3-5 paragraphs) covering, as the +query warrants: the rate path and Fed guidance; recent inflation and jobs data; +the VIX and credit spreads; earnings-season tone; and major geopolitical or +policy shocks. + +Ground every claim in the search results you actually retrieve. When a cutoff +date is specified, never report or speculate about events after it. + +Before finalizing your summary, reason step by step: (1) for each candidate \ +fact, judge its actual recency from the substance of the result itself, \ +never from a source's claimed publish date or byline timestamp — those are \ +frequently stale or updated after original publication; (2) discard \ +anything you cannot confidently place before the cutoff date; (3) only then \ +write your summary. Do not supplement the search results with your own \ +background/training knowledge — if the results are insufficient, say so \ +explicitly rather than filling gaps from memory.\ +""" + + +# --------------------------------------------------------------------------- +# Config factory +# --------------------------------------------------------------------------- + + +def build_starter_agent_config( + model: str = LITE_MODEL, + search_model: str = LITE_MODEL, + *, + enable_search: bool = True, + enable_code_exec: bool = False, +) -> AgentConfig: + """Build the S&P 500 starter :class:`AgentConfig`. + + Parameters + ---------- + model : str + Model for the analyst agent (default: lite). Pass the advanced model + (``"gemini-3.5-flash"``) for higher-quality runs. + search_model : str + Model for the bounded web-search sub-tool. + enable_search : bool, default=True + Wire a cutoff-aware ``search_web`` tool and load the + ``research-playbook`` skill. Proxy-only — no extra API key. + enable_code_exec : bool, default=False + Wire an E2B Python sandbox and load the ``code-analysis-playbook`` + skill. Needs ``E2B_API_KEY`` and is slower, so it is off by default. + + Returns + ------- + AgentConfig + """ + # Every attached skill is loaded on demand: ADK injects each skill's name + + # description into the system prompt, and the agent reads the full SKILL.md + # only when relevant — so toggling a tool just adds its skill, no persona edits. + skills_dirs: list[Path] = [_FORECASTING_SKILL] + if enable_search: + skills_dirs.append(_RESEARCH_SKILL) + if enable_code_exec: + skills_dirs.append(_CODE_ANALYSIS_SKILL) + + context_retrieval = ( + ContextRetrievalConfig( + enabled=True, + instruction=_CONTEXT_RETRIEVAL_INSTRUCTION, + search_model=search_model, + ) + if enable_search + else ContextRetrievalConfig() + ) + + return AgentConfig( + name="sp500_starter_agent", + model=model, + instruction=_STARTER_INSTRUCTION, + # 16k headroom: enough for a complete run_code script + structured output. + max_output_tokens=16_384 if enable_code_exec else None, + context_retrieval=context_retrieval, + code_execution=CodeExecutionConfig(enabled=enable_code_exec), + skills_dirs=skills_dirs, + ) + + +# --------------------------------------------------------------------------- +# Predictor convenience factory +# --------------------------------------------------------------------------- + + +class _StarterForecastPromptBuilder: + """Add the output schema + a forecast directive to a base builder's payload. + + The exact JSON schema is generated at call time from the output class + (drift-free) and injected into the user payload — never into the system + prompt — so the agent stays conversational until it is actually asked to + forecast. Implements the + :class:`~aieng.forecasting.methods.agentic.predictor.ForecastPromptBuilder` + protocol structurally. + """ + + def __init__(self, inner: Callable[..., str], output_schema_json: str) -> None: + self._inner = inner + self._schema_json = output_schema_json + + def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str: + payload = json.loads(self._inner(task=task, context=context)) + payload["instructions"] = ( + "Produce a calibrated probabilistic forecast for this task and return it by " + "calling `set_model_response` with a `json_response` string matching " + "`output_schema` exactly." + ) + payload["output_schema"] = self._schema_json + return json.dumps(payload, indent=2) + + +def build_starter_agent_predictor( + config: AgentConfig, + *, + covariate_series_ids: list[str] | None = None, +) -> AgentPredictor: + """Wrap a starter :class:`AgentConfig` in an :class:`AgentPredictor`. + + Wraps :class:`Sp500StarterPromptBuilder` so the (drift-free) continuous + output schema and a forecast directive ride in the payload — keeping the + schema out of the persona. ``predict(task, context)`` returns one + :class:`~aieng.forecasting.evaluation.prediction.Prediction` for the task's + single horizon. + + Parameters + ---------- + config : AgentConfig + A config from :func:`build_starter_agent_config`. + covariate_series_ids : list[str] or None + Covariates to include as a leak-safe snapshot in the prompt. They must be + registered on the data service used to build the context. ``None`` keeps + the starter target-only. + """ + return AgentPredictor( + agent_config=config, + prompt_builder=_StarterForecastPromptBuilder( + Sp500StarterPromptBuilder(covariate_series_ids=covariate_series_ids or []), + ContinuousAgentForecastOutput.prompt_schema_json(), + ), + output_schema=ContinuousAgentForecastOutput, + ) + + +# --------------------------------------------------------------------------- +# Lazy root_agent for `adk web` interactive use +# --------------------------------------------------------------------------- + + +def __getattr__(name: str) -> Any: + """Expose ``root_agent`` lazily for schema-free interactive use via ``adk web``.""" + if name == "root_agent": + return build_adk_agent(build_starter_agent_config()) + raise AttributeError(f"module {__name__!r} has no attribute {name!r}") diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md b/implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md new file mode 100644 index 00000000..230fe87a --- /dev/null +++ b/implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md @@ -0,0 +1,53 @@ +--- +name: code-analysis-playbook +description: >- + How to use the code execution sandbox well — parse the JSON payload (not + disk files), compute a couple of useful diagnostics before forecasting, and + keep the session stateful within a turn. Load this before writing code. No + scripts. +--- + +# Code-analysis playbook + +A short guide to using the `run_code` sandbox productively. This is a starter +skill — extend it with the diagnostics that matter for your problem. + +## Where your data lives + +All data comes from the **JSON payload in your context** — there are no disk +files and no network. The history arrives as a CSV *string* (e.g. +`target_history_csv`). Parse it with `io.StringIO`, never as a file path: + +```python +import io, pandas as pd +df = pd.read_csv(io.StringIO(payload["target_history_csv"])) +``` + +The sandbox is **stateful within a turn**: parse once in your first code block, +then reuse the DataFrame in later blocks instead of re-parsing. + +## Compute before you forecast + +Run a couple of cheap diagnostics so your forecast is grounded in arithmetic, +not vibes: + +1. **Recent trend** — slope/return over the last N observations. +2. **Volatility** — recent standard deviation of changes; it sets how wide your + quantile bands should be. +3. **Sanity check** — does your point forecast sit within a plausible multiple + of recent moves? If not, revisit it. + +Use the printed numbers to set the point forecast and to *calibrate the spread* +between your low and high quantiles — wider when recent volatility is high. + +## Domain focus (edit this for your use case) + +For S&P 500 log-returns, the series is near-unforecastable in the mean: recent +realised volatility is far more predictable than direction. Let recent vol, not a +directional hunch, set the *width* of your quantile bands, and keep the point +forecast close to zero unless you have real signal. + +## Room to grow + +- Add your own diagnostic patterns (regime detection, seasonality, covariates). +- Drop reusable reference values into a `references/` file and `load_skill_resource` them. diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md b/implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md new file mode 100644 index 00000000..2986b32d --- /dev/null +++ b/implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md @@ -0,0 +1,53 @@ +--- +name: forecasting +description: >- + The output contract for producing a structured probabilistic forecast — the + JSON shape, the calibration and quantile rules, and how to submit it. Load + this ONLY when your task payload asks for a forecast; ignore it for + open-ended questions. No scripts. +--- + +# Forecasting skill + +Load this when your task payload asks for a structured forecast. For open-ended +questions, ignore it and just answer. + +## What you'll receive + +A JSON payload describing the task: a `task` id, the `as_of` cutoff date, +`horizons` (steps ahead), the `standard_quantiles` grid, a `target_summary`, the +recent `target_history_csv`, and an `output_schema` showing the exact JSON to +return. + +## The output contract + +1. Produce **one forecast per horizon** in `horizons`. +2. Use **exactly** the levels in `standard_quantiles` — no additions or omissions. +3. `point_forecast` must equal the **0.50 quantile** value. +4. Quantile values must be **non-decreasing** as the quantile level rises. +5. Use ONLY information available on or before `as_of`. +6. Put your reasoning in the `rationale` fields. + +Submit by calling `set_model_response` with a `json_response` string that +matches the payload's `output_schema` **exactly** — use `"horizon"` (an +integer), and make `"quantiles"` a **list** of `{"quantile": , "value": +}` objects. Omit any field not shown in the schema. + +## Calibration + +Report calibrated intervals, not false precision: across many forecasts where +your 80% band is stated, the truth should land inside it about 80% of the time. +Anchor the point on the recent level and trend; let recent **volatility** set +how wide the bands are, and widen them as the horizon grows. + +## Domain focus (edit this for your use case) + +For S&P 500 log-returns, keep the point forecast near zero unless you have real +signal — returns are close to a random walk. Let recent realised volatility set +how WIDE the quantile bands are; it is far more predictable than direction. Note +any macro catalyst you lean on in the rationale. + +## Room to grow + +- Tighten the calibration guidance with your own backtest findings. +- Add worked examples of good vs. over-confident forecasts. diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md b/implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md new file mode 100644 index 00000000..50215fa7 --- /dev/null +++ b/implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md @@ -0,0 +1,47 @@ +--- +name: research-playbook +description: >- + How to use the search_web tool well when grounding a forecast in recent + news — phrase cutoff-aware queries, decide what is worth searching for, and + weigh sources. Load this before your first search_web call. No scripts. +--- + +# Research playbook + +A short guide to getting real signal out of `search_web`. This is a starter +skill — extend it with the queries and sources that work for your problem. + +## The one rule that matters + +Always pass `cutoff_date` equal to the `as_of` date in your payload. It is the +temporal fence that keeps post-origin information out of a historical forecast. +A forecast that "knew" what happened after `as_of` is not a forecast. + +`search_web` runs an independent verifier on every result and returns +`[SEARCH_VERIFICATION_FAILED]` instead of content it couldn't confirm as +pre-cutoff. Treat that as no verified news for the query — proceed on your +other signals and say so, never filling the gap from your own background +knowledge. + +## How to search + +- **Search before you forecast, not after.** Gather context first, then reason. +- **One topic per query.** Several focused queries beat one broad one. Stop when + new queries stop returning new facts. +- **Ask for the present state, not a prediction.** "current OPEC+ production + policy" returns facts; "will oil go up" returns noise. +- **Weigh sources.** Prefer primary releases and major outlets; treat a single + blog or forum post as a lead to confirm, not a fact. + +## Domain focus (edit this for your use case) + +For S&P 500 returns, the signals: the rate path and Fed guidance, recent +inflation and jobs prints, the VIX and credit spreads, earnings-season tone, and +major geopolitical or policy shocks. Search for the *current state* of these, +then remember returns are close to a random walk — be humble about direction. + +## Room to grow + +- Add a curated list of go-to sources for your domain. +- Track which queries paid off and prune the ones that didn't. +- Add a `references/` file with example high-signal searches. From 5ad8c5c77e131c654fd38d96611d5b53d98174f3 Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 01:54:30 +0000 Subject: [PATCH 02/24] created baa10y_smoke.yaml from sp500_smoke.yaml --- .../specs/baa10y_smoke.yaml} | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) rename implementations/{BAA10Y_forecasting /specs/sp500_smoke.yaml => baa10y_forecasting/specs/baa10y_smoke.yaml} (87%) diff --git a/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml b/implementations/baa10y_forecasting/specs/baa10y_smoke.yaml similarity index 87% rename from implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml rename to implementations/baa10y_forecasting/specs/baa10y_smoke.yaml index bc873e54..a5764c24 100644 --- a/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml +++ b/implementations/baa10y_forecasting/specs/baa10y_smoke.yaml @@ -8,23 +8,22 @@ # This spec carries experiment design only — the window, stride/warmup, and one # single-horizon task per target. WHICH predictors run (and all their # hyperparameters, including the covariate panel) is configured in the notebook, -# not here. Each task targets `sp500_logret_{N}b` (the close-to-close cumulative -# log return over N business days), so forecasting it N steps ahead resolves to +# not here. Each task targets `baa10y_{N}b` (the close-to-close spread change over N business days), so forecasting it N steps ahead resolves to # the forward N-session return. # # Prerequisites (warm caches to the present first): # uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC # uv run python scripts/fetch_fred.py # macro covariates -spec_id: sp500_smoke +spec_id: baa10y_smoke description: >- Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days. tasks: - - task_id: sp500_logret_1b - target_series_id: sp500_logret_1b + - task_id: baa10y_1b + target_series_id: baa10y_1b horizons: [1] frequency: B description: >- From 14c43a4e5152b919a62ed1078842258e376161e9 Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 02:12:56 +0000 Subject: [PATCH 03/24] added baa10y_smoke.yaml --- .../specs/baa10y_smoke.yaml | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml b/implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml new file mode 100644 index 00000000..efbb5e21 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml @@ -0,0 +1,53 @@ +# Smoke spec — fast laptop run over a short, post-cutoff (2025) window. +# +# Weekly origins in late 2025: after the Gemini training cutoff (~Jan 2025), so +# the LLM-Process rows in the notebook can be compared *fairly* against the +# conventional methods here. The notebook keeps its LLMP predictors ON for this +# window (the predictors cell gates them on a post-cutoff flag). +# +# This spec carries experiment design only — the window, stride/warmup, and one +# single-horizon task per target. WHICH predictors run (and all their +# hyperparameters, including the covariate panel) is configured in the notebook, +# not here. Each task targets `baa10y_change_{N}b` (the close-to-close cumulative +# change over N business days), so forecasting it N steps ahead resolves to +# the forward N-session return. +# +# Prerequisites (warm caches to the present first): +# uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC +# uv run python scripts/fetch_fred.py # macro covariates + +spec_id: baa10y_smoke + +description: >- + Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), + forecasting close-to-close cumulative changes at 1/5/21 business days. + +tasks: + - task_id: baa10y_change_1b + target_series_id: baa10y_change_1b + horizons: [1] + frequency: B + description: >- + BAA10Y close-to-close cumulative changes, 1 business day ahead + (next-session change / direction). + + - task_id: baa10y_change_5b + target_series_id: baa10y_change_5b + horizons: [5] + frequency: B + description: >- + BAA10Y close-to-close cumulative changes, 5 business days ahead + (forward 1-week change). + + - task_id: baa10y_change_21b + target_series_id: baa10y_change_21b + horizons: [21] + frequency: B + description: >- + BAA10Y close-to-close cumulative changes, 21 business days ahead + (forward 1-month change). + +start: "2025-10-06" +end: "2025-11-14" +stride: 5 # weekly origins (~6) +warmup: 250 From 3a93e893bb5a006ea295a1d976bd91b3508c58de Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 03:00:08 +0000 Subject: [PATCH 04/24] created baa10y backtest/eval/stress specs --- .../specs/baa10y_backtest_2025.yaml | 48 +++++++++++++++++ .../specs/baa10y_eval_2026.yaml | 51 +++++++++++++++++++ .../specs/baa10y_stress_2020.yaml | 50 ++++++++++++++++++ 3 files changed, 149 insertions(+) create mode 100644 implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml create mode 100644 implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml create mode 100644 implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml b/implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml new file mode 100644 index 00000000..1b56014d --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml @@ -0,0 +1,48 @@ +# Main backtest spec — weekly origins across 2025 (post-cutoff). +# +# 2025 is after the Gemini training cutoff (~Jan 2025), so this is the window +# where the conventional methods AND the LLM-Process can be compared *fairly* — +# the LLM has not memorised these outcomes. Mirrors the energy reference's 2025 +# backtest window. Use it for open iteration; spend the protected 2026 eval +# (`baa10y_eval_2026.yaml`) sparingly on your finalists. +# +# This spec carries experiment design only (window + one single-horizon task per +# target). The predictor roster and hyperparameters live in the notebook. Note +# the LLMP predictors are token-heavy over ~50 weekly origins — the notebook lets +# you trim the predictor list (or widen the stride here) before enabling them. + +spec_id: baa10y_backtest_2025 + +description: >- + Main multivariate comparison: weekly origins across 2025 (post-cutoff), + forecasting close-to-close cumulative changes at 1/5/21 business days. + +tasks: + - task_id: baa10y_change_1b + target_series_id: baa10y_change_1b + horizons: [1] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 1 business day ahead + (next-session change / direction). + + - task_id: baa10y_change_5b + target_series_id: baa10y_change_5b + horizons: [5] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 5 business days ahead + (forward 1-week change). + + - task_id: baa10y_change_21b + target_series_id: baa10y_change_21b + horizons: [21] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 21 business days ahead + (forward 1-month change). + +start: "2025-01-06" +end: "2025-12-22" +stride: 5 # weekly origins (~50) +warmup: 250 diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml b/implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml new file mode 100644 index 00000000..efa83549 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml @@ -0,0 +1,51 @@ +# Protected eval — held-out 2026 window, scored through multi_evaluate() with a budget. +# +# This is the honest scoreboard. 2026 is unambiguously after the Gemini training +# cutoff, so neither the numerical methods nor the LLM-Process can have seen the +# outcomes. Treat it as scarce: `max_runs` caps how many times the spec may be +# scored (via EvalTracker), so iterate on `baa10y_backtest_2025.yaml` and spend +# this only on a curated set of finalists (chosen in the notebook's eval cell). +# +# Loaded as a MultiTargetEvalSpec: one single-horizon task per target, all under +# a single shared run budget. One multi_evaluate() call across all three +# horizons counts as ONE run against `max_runs` — the budget is keyed by +# `spec_id`, not per-horizon. The predictor roster lives in the notebook. + +spec_id: baa10y_eval_2026 + +description: >- + Protected eval: weekly origins across early/mid 2026 (post-cutoff), + forecasting close-to-close cumulative changes at 1/5/21 business days. + +tasks: + - task_id: baa10y_change_1b + target_series_id: baa10y_change_1b + horizons: [1] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 1 business day ahead + (next-session change / direction). + + - task_id: baa10y_change_5b + target_series_id: baa10y_change_5b + horizons: [5] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 5 business days ahead + (forward 1-week change). + + - task_id: baa10y_change_21b + target_series_id: baa10y_change_21b + horizons: [21] + frequency: B + description: >- + BAA10Y close-to-close cumulative change, 21 business days ahead + (forward 1-month change). + +start: "2026-02-02" +end: "2026-03-23" # 8 weekly origins; all resolve at h=21 +stride: 5 # weekly origins +warmup: 250 + +# Budget cap: each multi_evaluate() call (all 3 horizons) counts as one run. +max_runs: 5 diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml b/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml new file mode 100644 index 00000000..2c7bf776 --- /dev/null +++ b/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml @@ -0,0 +1,50 @@ +# Regime-stress spec — the 2020 COVID crash, NUMERICAL METHODS ONLY. +# +# ⚠️ Keep the notebook's LLM-Process predictors OFF for this window. 2020 is +# BEFORE the Gemini training cutoff (~Jan 2025), so an LLM has effectively +# memorised these outcomes — scoring an LLMP here measures recall, not +# forecasting, and would silently flatter it in the comparison. The numerical +# methods are cutoff-safe by construction (they only see the series up to the +# origin), so this volatile window is a perfectly valid stress test *for them* — +# it's where a covariate edge is most visible. +# +# The notebook enforces "numerical only" in code: its predictors cell gates the +# LLMP variants on a post-cutoff flag that is False for this config. Use this to +# study "when do covariates help?" among the conventional methods; use the 2025 +# backtest / 2026 eval for anything involving the LLMP. + +spec_id: baa10y_stress_2020 + +description: >- + COVID-crash regime stress (numerical methods only): daily origins Feb–Apr + 2020, forecasting close-to-close cumulative changes at 1/5/21 business days. + +tasks: + - task_id: baa10y_change_1b + target_series_id: baa10y_change_1b + horizons: [1] + frequency: B + description: >- + S&P 500 close-to-close cumulative change, 1 business day ahead + (next-session change / direction). + + - task_id: baa10y_change_5b + target_series_id: baa10y_change_5b + horizons: [5] + frequency: B + description: >- + S&P 500 close-to-close cumulative change, 5 business days ahead + (forward 1-week change). + + - task_id: baa10y_change_21b + target_series_id: baa10y_change_21b + horizons: [21] + frequency: B + description: >- + S&P 500 close-to-close cumulative change, 21 business days ahead + (forward 1-month change). + +start: "2020-02-03" +end: "2020-04-30" +stride: 1 # daily origins across the crash +warmup: 250 From 42eee9e5088156aafc3f5e52aaa3e1466d8fce75 Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 03:11:47 +0000 Subject: [PATCH 05/24] Update BAA10Y forecasting components Remove the extraneous space from the folder name. Update all predictor files and skill documentation. Update leaderboard.py, plots.py, and __init__.py. --- .../BAA10Y_forecasting /__init__.py | 58 ----------------- .../01_BAA10Y_multivariate_backtest.ipynb} | 2 +- .../99_starter_agent.ipynb | 0 .../README.md | 0 .../BAA10Y_forecasting/__init__.py | 63 +++++++++++++++++++ .../analysis.py | 0 .../data.py | 12 ++-- .../leaderboard.py | 26 ++++---- .../plots.py | 57 +++++++++-------- .../predictors/__init__.py | 8 +-- .../predictors/llmp_sampled_trajectory.py | 39 ++++++------ .../specs/sp500_backtest_2025.yaml | 0 .../specs/sp500_eval_2026.yaml | 0 .../specs/sp500_smoke.yaml | 0 .../specs/sp500_stress_2020.yaml | 0 .../starter_agent/__init__.py | 8 +-- .../starter_agent/agent.py | 62 +++++++++--------- .../skills/code-analysis-playbook/SKILL.md | 14 +++-- .../starter_agent/skills/forecasting/SKILL.md | 15 +++-- .../skills/research-playbook/SKILL.md | 22 +++++-- .../02_food_cpi_experiment.ipynb | 2 +- 21 files changed, 216 insertions(+), 172 deletions(-) delete mode 100644 implementations/BAA10Y_forecasting /__init__.py rename implementations/{BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb => BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb} (99%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/99_starter_agent.ipynb (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/README.md (100%) create mode 100644 implementations/BAA10Y_forecasting/__init__.py rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/analysis.py (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/data.py (99%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/leaderboard.py (89%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/plots.py (81%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/predictors/__init__.py (77%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/predictors/llmp_sampled_trajectory.py (71%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/sp500_backtest_2025.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/sp500_eval_2026.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/sp500_smoke.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/sp500_stress_2020.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/starter_agent/__init__.py (60%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/starter_agent/agent.py (82%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/starter_agent/skills/code-analysis-playbook/SKILL.md (72%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/starter_agent/skills/forecasting/SKILL.md (70%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/starter_agent/skills/research-playbook/SKILL.md (65%) diff --git a/implementations/BAA10Y_forecasting /__init__.py b/implementations/BAA10Y_forecasting /__init__.py deleted file mode 100644 index 59014149..00000000 --- a/implementations/BAA10Y_forecasting /__init__.py +++ /dev/null @@ -1,58 +0,0 @@ -"""S&P 500 multivariate log-return experiment — leak-safe covariates. - -The demo notebooks are narrative shells over the modules in this directory: - -- :mod:`data` — ``build_sp500_multivariate_service()`` and canonical covariate ids. -- :mod:`predictors` — ``build_sp500_llmp_sampled_trajectory()`` recipe (prompt framing + sampling budget). -- :mod:`leaderboard` — ``build_leaderboard()`` turns cached results into ``RESULTS_DF``. -- :mod:`analysis` — styled leaderboards and direction metrics. -- :mod:`plots` — matplotlib figures (target history, per-horizon CRPS, forecast vs realised return). -- YAML specs — experiment design only (window + one single-horizon task per - ``sp500_logret_{N}b`` target): ``sp500_smoke`` / ``sp500_backtest_2025`` / - ``sp500_eval_2026`` / ``sp500_stress_2020``. The predictor roster lives in the notebook. - -See ``README.md`` for the full experiment description. -""" - -from .data import ( - DEFAULT_COVARIATE_SERIES_IDS, - FRED_PREFETCH_REGISTRY, - FRED_SERIES_IDS_FOR_PREFETCH, - SERIES_ID_2Y10Y_SPREAD, - SERIES_ID_10Y_YIELD, - SERIES_ID_CPI_INFLATION_CHANGE, - SERIES_ID_DOLLAR_INDEX_RETURN, - SERIES_ID_FED_FUNDS, - SERIES_ID_GOLD_RETURN, - SERIES_ID_NASDAQ_RETURN, - SERIES_ID_OIL_RETURN, - SERIES_ID_UNEMPLOYMENT, - SERIES_ID_VIX_CHANGE, - SERIES_ID_VIX_LEVEL, - SP500_LOG_RETURN_SERIES_ID, - SP500_SERIES_ID, - SP500_TICKER, - build_sp500_multivariate_service, -) - - -__all__ = [ - "DEFAULT_COVARIATE_SERIES_IDS", - "FRED_PREFETCH_REGISTRY", - "FRED_SERIES_IDS_FOR_PREFETCH", - "SERIES_ID_2Y10Y_SPREAD", - "SERIES_ID_10Y_YIELD", - "SERIES_ID_CPI_INFLATION_CHANGE", - "SERIES_ID_DOLLAR_INDEX_RETURN", - "SERIES_ID_FED_FUNDS", - "SERIES_ID_GOLD_RETURN", - "SERIES_ID_NASDAQ_RETURN", - "SERIES_ID_OIL_RETURN", - "SERIES_ID_UNEMPLOYMENT", - "SERIES_ID_VIX_CHANGE", - "SERIES_ID_VIX_LEVEL", - "SP500_LOG_RETURN_SERIES_ID", - "SP500_SERIES_ID", - "SP500_TICKER", - "build_sp500_multivariate_service", -] diff --git a/implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb similarity index 99% rename from implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb rename to implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb index 40e948b0..35744e6f 100644 --- a/implementations/BAA10Y_forecasting /01_sp500_multivariate_backtest.ipynb +++ b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb @@ -5,7 +5,7 @@ "id": "52b6fbe6", "metadata": {}, "source": [ - "# S&P 500 — multivariate conventional-methods comparison\n", + "# BAA10Y — multivariate conventional-methods comparison\n", "\n", "The repo's **financial-markets** reference: a head-to-head of conventional\n", "time-series methods on a daily equity index, all reading the **same leak-safe\n", diff --git a/implementations/BAA10Y_forecasting /99_starter_agent.ipynb b/implementations/BAA10Y_forecasting/99_starter_agent.ipynb similarity index 100% rename from implementations/BAA10Y_forecasting /99_starter_agent.ipynb rename to implementations/BAA10Y_forecasting/99_starter_agent.ipynb diff --git a/implementations/BAA10Y_forecasting /README.md b/implementations/BAA10Y_forecasting/README.md similarity index 100% rename from implementations/BAA10Y_forecasting /README.md rename to implementations/BAA10Y_forecasting/README.md diff --git a/implementations/BAA10Y_forecasting/__init__.py b/implementations/BAA10Y_forecasting/__init__.py new file mode 100644 index 00000000..26490bef --- /dev/null +++ b/implementations/BAA10Y_forecasting/__init__.py @@ -0,0 +1,63 @@ +"""BAA10Y multivariate spread-change forecasting with leak-safe covariates. + +The demo notebooks are narrative shells over the modules in this directory: + +- :mod:`data` — BAA10Y target construction, multivariate data service, and + canonical covariate IDs. +- :mod:`predictors` — ``build_baa10y_llmp_sampled_trajectory()`` recipe (prompt framing + sampling budget). +- :mod:`leaderboard` — ``build_leaderboard()`` turns cached results into ``RESULTS_DF``. +- :mod:`analysis` — styled leaderboards and direction metrics. +- :mod:`plots` — matplotlib figures (target history, per-horizon CRPS, forecast vs realised return). +- YAML specs — experiment design only (window + one single-horizon task xxx need update later + +See ``README.md`` for the full experiment description. +""" + +from .data import ( + DEFAULT_COVARIATE_SERIES_IDS, + FRED_PREFETCH_REGISTRY, + FRED_SERIES_IDS_FOR_PREFETCH, + SERIES_ID_2Y10Y_SPREAD, + SERIES_ID_10Y_YIELD, + SERIES_ID_CPI_INFLATION_CHANGE, + SERIES_ID_DOLLAR_INDEX_RETURN, + SERIES_ID_FED_FUNDS, + SERIES_ID_GOLD_RETURN, + SERIES_ID_NASDAQ_RETURN, + SERIES_ID_OIL_RETURN, + SERIES_ID_UNEMPLOYMENT, + SERIES_ID_VIX_CHANGE, + SERIES_ID_VIX_LEVEL, + BAA10Y_CHANGE_SERIES_ID, + BAA10Y_CHANGE_TARGETS, + BAA10Y_CHANGE_WINDOWS, + BAA10Y_FRED_ID, + baa10y_change_series_id, + build_baa10y_multivariate_service, + +) + + +__all__ = [ + DEFAULT_COVARIATE_SERIES_IDS, + FRED_PREFETCH_REGISTRY, + FRED_SERIES_IDS_FOR_PREFETCH, + SERIES_ID_2Y10Y_SPREAD, + SERIES_ID_10Y_YIELD, + SERIES_ID_CPI_INFLATION_CHANGE, + SERIES_ID_DOLLAR_INDEX_RETURN, + SERIES_ID_FED_FUNDS, + SERIES_ID_GOLD_RETURN, + SERIES_ID_NASDAQ_RETURN, + SERIES_ID_OIL_RETURN, + SERIES_ID_UNEMPLOYMENT, + SERIES_ID_VIX_CHANGE, + SERIES_ID_VIX_LEVEL, + BAA10Y_CHANGE_SERIES_ID, + BAA10Y_CHANGE_TARGETS, + BAA10Y_CHANGE_WINDOWS, + BAA10Y_FRED_ID, + baa10y_change_series_id, + build_baa10y_multivariate_service, + +] diff --git a/implementations/BAA10Y_forecasting /analysis.py b/implementations/BAA10Y_forecasting/analysis.py similarity index 100% rename from implementations/BAA10Y_forecasting /analysis.py rename to implementations/BAA10Y_forecasting/analysis.py diff --git a/implementations/BAA10Y_forecasting /data.py b/implementations/BAA10Y_forecasting/data.py similarity index 99% rename from implementations/BAA10Y_forecasting /data.py rename to implementations/BAA10Y_forecasting/data.py index 224e3ff3..b80a7b0e 100644 --- a/implementations/BAA10Y_forecasting /data.py +++ b/implementations/BAA10Y_forecasting/data.py @@ -3,10 +3,8 @@ Target: Moody's Seasoned Baa Corporate Bond Yield Relative to the 10-Year Treasury Constant Maturity Rate, downloaded from FRED using series ID ``BAA10Y`` - -Δt(N)​=BAA10Yt​−BAA10Yt−N​ - -The target is measured in percent and is forecast at selected business-day +delta_t(N) = BAA10Y(t) - BAA1OY(t-N) +The target is measured in basis point and is forecast at selected business-day horizons, such as 1, 5, and 21 business days. @@ -156,8 +154,8 @@ def _build_cumulative_spread_change_frame(spread_df: pd.DataFrame, window: int) frame["value"] = pd.to_numeric(frame["value"], errors="coerce") frame = frame.dropna(subset=["value"]).reset_index(drop=True) - # BAA10Y is measured in percent. - # spread widening/tightening in percentage points + # BAA10Y is measured in basis. + # spread widening/tightening in basis points frame["value"] = frame["value"] - frame["value"].shift(window) frame = frame.dropna(subset=["value"]).reset_index(drop=True) @@ -226,7 +224,7 @@ def build_baa10y_target_service( f"{window} business day(s) ({label})" ), source=f"FRED ({BAA10Y_FRED_ID}), derived", - units="percentage-points", + units="basis-points", # frequency="D" # every calendar day, including weekends # frequency="B" # every weekday, excluding weekends diff --git a/implementations/BAA10Y_forecasting /leaderboard.py b/implementations/BAA10Y_forecasting/leaderboard.py similarity index 89% rename from implementations/BAA10Y_forecasting /leaderboard.py rename to implementations/BAA10Y_forecasting/leaderboard.py index ca4e84c5..f707b187 100644 --- a/implementations/BAA10Y_forecasting /leaderboard.py +++ b/implementations/BAA10Y_forecasting/leaderboard.py @@ -1,13 +1,13 @@ -"""Leaderboard rows for the multivariate S&P 500 experiment. +"""Leaderboard rows for the multivariate BAA10Y spread-change experiment. The notebook runs each predictor with the shared :func:`~aieng.forecasting.evaluation.cached_multi_backtest` / :func:`~aieng.forecasting.evaluation.multi_evaluate` helpers, which return a ``dict`` keyed by ``task_id`` (one task per horizon, targeting -``sp500_logret_{N}b``). :func:`build_leaderboard` turns the +``baa10y_change_{N}b``). :func:`build_leaderboard` turns the ``{predictor_id: {task_id: result}}`` mapping those produce into the ``RESULTS_DF`` frame consumed by -:func:`~sp500_forecasting.plots.display_multivariate_backtest_leaderboard` +:func:`BAA10Y_forecasting.plots.display_multivariate_backtest_leaderboard` (one row per predictor × horizon, with mean CRPS and next-direction metrics). Which predictors run, and all their hyperparameters, are configured in the @@ -21,7 +21,7 @@ import pandas as pd from aieng.forecasting.data.service import DataService from aieng.forecasting.evaluation import BacktestResult, EvalResult -from sp500_forecasting.analysis import ( +from BAA10Y_forecasting.analysis import ( build_direction_eval_frame, direction_classification_metrics, ) @@ -41,15 +41,15 @@ } -def build_return_compare_frame( +def build_spread_change_compare_frame( predictions: list[Prediction], data_service: DataService, target_series_id: str, ) -> pd.DataFrame: """One row per scored prediction: realised return vs forecast median and 5–95% band. - Returns are kept on the target (log-return) scale; the notebook renders them - as percentages. Rows whose ``forecast_date`` has no realised observation are + The realized values, forecasts, and quantiles are already measured in basis + points. Rows whose ``forecast_date`` has no realised observation are dropped. """ from datetime import datetime, timezone # noqa: PLC0415 @@ -73,10 +73,10 @@ def build_return_compare_frame( rows.append( { "session": ts, - "actual_return": float(lookup.loc[ts]), - "forecast_return": float(med), - "forecast_return_p05": float(qmap.get(0.05, float("nan"))), - "forecast_return_p95": float(qmap.get(0.95, float("nan"))), + "actual_spread_change": float(lookup.loc[ts]), + "forecast_spread_change": float(med), + "forecast_spread_change_p05": float(qmap.get(0.05, float("nan"))), + "forecast_spread_change_p95": float(qmap.get(0.95, float("nan"))), } ) if not rows: @@ -165,7 +165,7 @@ def build_leaderboard( ``run_number`` column. data_service Service that registers the target series (used for the next-direction - metrics that align each forecast with its realised return). + metrics that align each forecast with its realised spread change). covariates_by_predictor Optional ``{predictor_id: [series_id, ...]}`` so the ``uses_covariates`` / ``covariates`` columns reflect each predictor's covariate panel. A @@ -201,5 +201,5 @@ def build_leaderboard( __all__ = [ "build_leaderboard", - "build_return_compare_frame", + "build_spread_change_compare_frame", ] diff --git a/implementations/BAA10Y_forecasting /plots.py b/implementations/BAA10Y_forecasting/plots.py similarity index 81% rename from implementations/BAA10Y_forecasting /plots.py rename to implementations/BAA10Y_forecasting/plots.py index 05da068b..dcccd3fa 100644 --- a/implementations/BAA10Y_forecasting /plots.py +++ b/implementations/BAA10Y_forecasting/plots.py @@ -1,7 +1,7 @@ -"""Matplotlib helpers for the multivariate S&P 500 demo notebook. - -Keeps the notebook narrative-focused; style matches -``food_price_forecasting/plots.py`` (matplotlib only, ``(fig, ax)`` returns). +"""Matplotlib helpers for the multivariate BAA10Y demo notebook. + BAA10Y spread changes and their forecasts are assumed to be stored in basis + points. No additional percentage-point-to-basis-point conversion is performed + in this module. """ from __future__ import annotations @@ -16,29 +16,29 @@ from matplotlib.axes import Axes from matplotlib.figure import Figure from matplotlib.ticker import FuncFormatter -from sp500_forecasting.analysis import style_results_dataframe -from sp500_forecasting.data import SP500_LOG_RETURN_SERIES_ID +from BAA10Y_forecasting.analysis import style_results_dataframe +from BAA10Y_forecasting.data import BAA10Y_CHANGE_SERIES_ID if TYPE_CHECKING: from aieng.forecasting.data.service import DataService -def plot_sp500_log_return_recent( +def plot_baa10y_spread_change_recent( data_service: DataService, *, - series_id: str = SP500_LOG_RETURN_SERIES_ID, + series_id: str = BAA10Y_CHANGE_SERIES_ID, n_trading_days: int = 756, title: str | None = None, ) -> tuple[Figure, Axes]: - """Plot the last *n_trading_days* observed close-to-close log returns. + """Plot recent BAA10Y spread changes in basis points. Parameters ---------- data_service Any service that registers ``series_id`` (typically ``svc_no_cov``). series_id - Canonical log-return series id (defaults to the 1-business-day return). + BAA10Y spread-change series ID (defaults to the 1-business-day return). n_trading_days How many most recent rows to show (default ~3y of sessions). title @@ -49,13 +49,18 @@ def plot_sp500_log_return_recent( plot_df = df.sort_values("timestamp").tail(int(n_trading_days)).copy() plot_df["timestamp"] = pd.to_datetime(plot_df["timestamp"]) + # The target is in basis points. + plot_df["value_bp"] = pd.to_numeric( + plot_df["value"], + errors="coerce", + ) fig, ax = plt.subplots(figsize=(10, 3.5), layout="constrained") ax.axhline(0.0, color="0.45", linewidth=0.8, linestyle="--", zorder=1) ax.fill_between( plot_df["timestamp"], 0.0, - plot_df["value"], - where=plot_df["value"] >= 0, + plot_df["value_bp"], + where=plot_df["value_bp"] >= 0, interpolate=True, alpha=0.35, color="#1f77b4", @@ -64,16 +69,16 @@ def plot_sp500_log_return_recent( ax.fill_between( plot_df["timestamp"], 0.0, - plot_df["value"], - where=plot_df["value"] < 0, + plot_df["value_bp"], + where=plot_df["value_bp"] < 0, interpolate=True, alpha=0.35, color="#d62728", linewidth=0, ) - ax.plot(plot_df["timestamp"], plot_df["value"], color="0.15", linewidth=0.6, zorder=2) - ax.set_xlabel("Session date (target timestamp)") - ax.set_ylabel("Log return") + ax.plot(plot_df["timestamp"], plot_df["value_bp"], color="0.15", linewidth=0.6, zorder=2) + ax.set_xlabel("Business date (target timestamp)") + ax.set_ylabel("Spread change (basis points)") ttl = title or ( f"Observed {series_id} (last {len(plot_df)} sessions)\nPositive: index up over the window; negative: down." ) @@ -123,7 +128,7 @@ def plot_mean_crps_by_horizon( """Small-multiples: one CRPS bar panel per horizon, methods sorted within each. Expects a combined frame from - :func:`~sp500_forecasting.leaderboard.build_leaderboard` (with a ``horizon`` + :func:`~BAA10Y_forecasting.leaderboard.build_leaderboard` (with a ``horizon`` column). Makes the "predictability decays with horizon" story visible. """ d = results_df.dropna(subset=["mean_crps"]).copy() @@ -151,7 +156,7 @@ def plot_mean_crps_by_horizon( return fig, ax_row -def plot_return_forecast_vs_actual_multi( +def plot_spread_change_forecast_vs_actual_multi( compare_by_run: Mapping[str, pd.DataFrame], *, title: str | None = None, @@ -159,7 +164,7 @@ def plot_return_forecast_vs_actual_multi( """Realised return (once) vs each run's median forecast, rendered as percent. Each value frame is from - :func:`~sp500_forecasting.leaderboard.build_return_compare_frame` for a + :func:`~BAA10Y_forecasting.leaderboard.build_spread_change_compare_frame` for a single horizon. Insertion order controls legend order. """ fig, ax = plt.subplots(figsize=(12, 5.0), layout="constrained", facecolor="0.98") @@ -167,7 +172,7 @@ def plot_return_forecast_vs_actual_multi( items = [(k, df) for k, df in compare_by_run.items() if df is not None and not df.empty] if not items: - ax.text(0.5, 0.5, "No rows to plot (check price cache and backtest window).", ha="center", va="center") + ax.text(0.5, 0.5, "No rows to plot (check data cache and backtest window).", ha="center", va="center") ax.set_axis_off() return fig, ax @@ -177,7 +182,7 @@ def plot_return_forecast_vs_actual_multi( ax.axhline(0.0, color="0.5", linewidth=0.8, linestyle="--", zorder=1) ax.plot( base["session"].to_numpy(), - 100.0 * base["actual_return"].to_numpy(dtype=float), + 100.0 * base["actual_spread_change"].to_numpy(dtype=float), color="#0d47a1", linewidth=2.2, marker="o", @@ -193,7 +198,7 @@ def plot_return_forecast_vs_actual_multi( d = d.sort_values("session") ax.plot( d["session"].to_numpy(), - 100.0 * d["forecast_return"].to_numpy(dtype=float), + 100.0 * d["forecast_spread_change"].to_numpy(dtype=float), color=cmap(i % 10), linewidth=1.6, linestyle="--", @@ -203,10 +208,10 @@ def plot_return_forecast_vs_actual_multi( zorder=4 + i * 0.01, ) - ttl = title or "S&P 500 — forecast vs realised return" + ttl = title or "BAA10Y spread change — forecast vs realised" ax.set_title(ttl, fontsize=12, fontweight="600", color="0.15") ax.set_xlabel("Session date (forecast resolution)", fontsize=10, color="0.25") - ax.set_ylabel("Return (%)", fontsize=10, color="0.25") + ax.set_ylabel("Spread change (basis points)", fontsize=10, color="0.25") ax.legend(loc="upper left", framealpha=0.92, fontsize=8, ncol=2) ax.grid(True, alpha=0.28, linestyle="-", linewidth=0.6) ax.tick_params(axis="both", labelsize=9, colors="0.35") @@ -234,5 +239,5 @@ def display_multivariate_backtest_leaderboard(results_df: pd.DataFrame) -> None: if "horizon" in results_df.columns and results_df["horizon"].nunique() > 1: plot_mean_crps_by_horizon(results_df) else: - plot_mean_crps_leaderboard(results_df, title="Mean CRPS — log return (target scale)") + plot_mean_crps_leaderboard(results_df, title="Mean CRPS — BAA10Y spread change " "(basis-point scale)") plt.show() diff --git a/implementations/BAA10Y_forecasting /predictors/__init__.py b/implementations/BAA10Y_forecasting/predictors/__init__.py similarity index 77% rename from implementations/BAA10Y_forecasting /predictors/__init__.py rename to implementations/BAA10Y_forecasting/predictors/__init__.py index e735f74b..aefc9f44 100644 --- a/implementations/BAA10Y_forecasting /predictors/__init__.py +++ b/implementations/BAA10Y_forecasting/predictors/__init__.py @@ -1,6 +1,6 @@ -"""Tuned predictor recipes for the multivariate S&P 500 experiment. +"""Tuned predictor recipes for the multivariate BAA10Y experiment. -Each module here builds a fully-configured predictor instance for the S&P 500 +Each module here builds a fully-configured predictor instance for the BAA10Y use case. Recipes pair a task-agnostic predictor from :mod:`aieng.forecasting.methods` with use-case-specific configuration: prompt overrides (what the series is and how returns behave), history windows, sampling @@ -13,7 +13,7 @@ directly from :mod:`aieng.forecasting.methods`. """ -from .llmp_sampled_trajectory import build_sp500_llmp_sampled_trajectory +from .llmp_sampled_trajectory import build_baa10y_llmp_sampled_trajectory -__all__ = ["build_sp500_llmp_sampled_trajectory"] +__all__ = ["build_baa10y_llmp_sampled_trajectory"] diff --git a/implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py b/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py similarity index 71% rename from implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py rename to implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py index a8cd8f18..5022e38a 100644 --- a/implementations/BAA10Y_forecasting /predictors/llmp_sampled_trajectory.py +++ b/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py @@ -1,8 +1,8 @@ -"""S&P 500 recipe: sampled-trajectory LLMP (target-only and with-covariates). +"""BAA1OY recipe: sampled-trajectory LLMP (target-only and with-covariates). This file is intentionally small and explicit so notebook readers can open it as a reference recipe. The reusable method lives in ``aieng.forecasting``; this -module captures the S&P 500 prompt framing (what the series *is* and how returns +module captures the BAA1OY prompt framing (what the series *is* and how returns behave), the default sampling budget, the history window, and the cache tag used by the experiment. @@ -28,29 +28,32 @@ _DEFAULT_MODEL = LITE_MODEL _DEFAULT_N_SAMPLES = 10 _DEFAULT_HISTORY_WINDOW = 64 -_RECIPE_FAMILY = "sp500_v1" +_RECIPE_FAMILY = "baa10y_v1" _SERIES_DESCRIPTION = ( - "Series: S&P 500 (^GSPC) close-to-close cumulative log return over a fixed " - "number of business days.\n" - "Units: log-return (a value of 0.01 is roughly a +1% move).\n" + "Series: change in the FRED BAA10Y corporate credit spread over " + "a fixed number of business days. BAA10Y spread between Moody;s Seasoned Baa Corporate Bond and 10-Year Treasury Constant Maturity.\n" + "Units: percentage points. A positive value means spread widening;" + "a negative value means spread tightening.\n" "Frequency: business days (Mon-Fri)." ) +## provides BAA10Y-specific instructions to the LLM each time it generates a forecast. + _USER_PROMPT_SUFFIX = ( "Notes for this series:\n" - "- Daily index returns are close to a martingale: the *level* of the return " - "is barely predictable, so point forecasts should sit near 0 and the value " - "is in the *spread* (volatility and tail risk), not a confident direction.\n" - "- Returns cluster in volatility — calm and turbulent stretches persist — so " - "recent realised dispersion is the best guide to the width of your interval.\n" - "- Keep the distribution roughly symmetric about ~0 unless the recent history " - "or the covariate blocks give a clear reason to skew it; avoid extrapolating " - "a short run of up or down days into a trend." + "- Spread changes are generally centered near zero, but their volatility " + "varies over time and can rise sharply during periods of market stress.\n" + "- Credit-spread changes can be asymmetric. Sudden widening may be larger " + "than routine tightening, so allow a wider positive tail when VIX, Treasury " + "yields, or other covariates indicate financial stress.\n" + "- The forecast horizon is already encoded in the target series. Predict " + "the cumulative spread change directly rather than summing a simulated " + "daily path.\n" ) -def build_sp500_llmp_sampled_trajectory( +def build_baa10y_llmp_sampled_trajectory( *, model: str = _DEFAULT_MODEL, n_samples: int = _DEFAULT_N_SAMPLES, @@ -60,10 +63,10 @@ def build_sp500_llmp_sampled_trajectory( max_tokens: int = 16384, variant_tag: str | None = None, ) -> SampledTrajectoryLLMPredictor: - """Return the S&P 500 sampled-trajectory LLMP recipe. + """Build the BAA10Y sampled-trajectory LLMP predictor. The model is a normal parameter because the base LLMP ``predictor_id`` - already includes it. The recipe tag records the S&P 500 prompt/config family, + already includes it. The recipe tag records the BAA10Y prompt/config family, whether the covariate panel is in context, and the cache-relevant knobs that are not otherwise visible in the ID. @@ -108,4 +111,4 @@ def build_sp500_llmp_sampled_trajectory( return SampledTrajectoryLLMPredictor(config) -__all__ = ["build_sp500_llmp_sampled_trajectory"] +__all__ = ["build_baa10y_llmp_sampled_trajectory"] diff --git a/implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml b/implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/sp500_backtest_2025.yaml rename to implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml diff --git a/implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml b/implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/sp500_eval_2026.yaml rename to implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml diff --git a/implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml b/implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/sp500_smoke.yaml rename to implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml diff --git a/implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml b/implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/sp500_stress_2020.yaml rename to implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml diff --git a/implementations/BAA10Y_forecasting /starter_agent/__init__.py b/implementations/BAA10Y_forecasting/starter_agent/__init__.py similarity index 60% rename from implementations/BAA10Y_forecasting /starter_agent/__init__.py rename to implementations/BAA10Y_forecasting/starter_agent/__init__.py index 3c4d9a72..9208bf8f 100644 --- a/implementations/BAA10Y_forecasting /starter_agent/__init__.py +++ b/implementations/BAA10Y_forecasting/starter_agent/__init__.py @@ -1,19 +1,19 @@ -"""S&P 500 starter agent — a fresh, hackable template for your own exploration. +"""BAA10Y starter agent — a configurable template for forecasting corporate credit-spread changes Exports the toggle-driven :class:`AgentConfig` factory, the predictor convenience factory, and the self-contained prompt builder. See ``99_starter_agent.ipynb`` and ``agent.py``. """ -from sp500_forecasting.starter_agent.agent import ( - Sp500StarterPromptBuilder, +from BAA10Y_forecasting .starter_agent.agent import ( + BAA10YStarterPromptBuilder, build_starter_agent_config, build_starter_agent_predictor, ) __all__ = [ - "Sp500StarterPromptBuilder", + "BAA10YStarterPromptBuilder", "build_starter_agent_config", "build_starter_agent_predictor", ] diff --git a/implementations/BAA10Y_forecasting /starter_agent/agent.py b/implementations/BAA10Y_forecasting/starter_agent/agent.py similarity index 82% rename from implementations/BAA10Y_forecasting /starter_agent/agent.py rename to implementations/BAA10Y_forecasting/starter_agent/agent.py index b02e5b3d..5297c9ce 100644 --- a/implementations/BAA10Y_forecasting /starter_agent/agent.py +++ b/implementations/BAA10Y_forecasting/starter_agent/agent.py @@ -1,6 +1,6 @@ -"""S&P 500 starter agent — a fresh, hackable template for your own exploration. +"""BAA10Y starter agent — a fresh, hackable template for your own exploration. -This is the S&P 500 use case's **first** agent, and it is deliberately minimal: +This is the BAA10Y use case's **first** agent, and it is deliberately minimal: a clean starting point with our common building blocks behind simple toggles — - **optional news search** (``enable_search``, on by default) — bounded, @@ -14,10 +14,10 @@ ``planning-docs/vector-llm-proxy.md``. This use case had no agent to borrow a prompt builder from, so -:class:`Sp500StarterPromptBuilder` below is a small, self-contained serialiser — +:class:`BAA10YStarterPromptBuilder` below is a small, self-contained serialiser — read it, then extend it (more covariates, richer panels, report context). The -target is a single-horizon cumulative log return; the output is a probabilistic -forecast of that return. Pair this with ``99_starter_agent.ipynb``. +target is a single-horizon cumulative spread changes; the output is a probabilistic +forecast of that spread change. Pair this with ``99_starter_agent.ipynb``. Module-level ``__getattr__`` exposes ``root_agent`` lazily so ``adk web`` can load this module for interactive (schema-free) use. @@ -55,14 +55,14 @@ # --------------------------------------------------------------------------- -# Prompt builder (self-contained — this use case has no analyst_agent to reuse) +# Prompt builder # --------------------------------------------------------------------------- -class Sp500StarterPromptBuilder(BaseModel): - """Serialise the target log-return series (+ optional covariate snapshot). +class BAA10YStarterPromptBuilder(BaseModel): + """Serialize BAA10Y spread-change history and optional covariates. - Minimal on purpose: the recent history of the cumulative-log-return target, + Minimal on purpose: the recent history of the cumulative-spread change target, the task spec, the exact quantile grid, and — when ``covariate_series_ids`` are supplied and present in the context — the latest value of each covariate as a leak-safe macro snapshot. Implements the @@ -77,7 +77,7 @@ class Sp500StarterPromptBuilder(BaseModel): def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str: df = context.get_series(task.target_series_id).tail(self.history) - rows = ["date,log_return"] + [ + rows = ["date,spread_change"] + [ f"{pd.Timestamp(ts).date()},{float(v):.6f}" for ts, v in zip(df["timestamp"], df["value"]) ] @@ -96,7 +96,7 @@ def __call__(self, *, task: ForecastingTask, context: ForecastContext) -> str: "horizons": list(task.horizons), "standard_quantiles": list(STANDARD_QUANTILES), "target_summary": { - "last_log_return": float(df["value"].iloc[-1]), + "last_spread_change": float(df["value"].iloc[-1]), "last_date": str(pd.Timestamp(df["timestamp"].iloc[-1]).date()), "n_obs": int(len(df)), }, @@ -124,11 +124,15 @@ def _build_starter_instruction() -> str: """ return ( "## Role\n\n" - "You are an equity-market analyst — fluent in the rate path and Fed " - "guidance, inflation and jobs data, volatility and credit conditions, " - "and how macro catalysts move the S&P 500. This is a starter agent: keep " - "your reasoning transparent and your claims honest, and remember returns " - "are close to a random walk.\n\n" + "You are a corporate-credit-market analyst specializing in the BAA10Y " + "spread: Moody's Seasoned Baa Corporate Bond Yield Relative to the " + "10-Year Treasury yield. You understand Federal Reserve policy, " + "Treasury rates, inflation and employment data, economic growth, " + "corporate defaults and downgrades, refinancing conditions, market " + "liquidity, equity volatility, and investor risk sentiment.\n\n" + "keep your reasoning transparent and your claims honest, BAA10Y spread " + "changes are often centered near zero, while volatility and tail risk vary " + "over time. Remain conservative about directional predictability.\n\n" "## How to respond\n\n" "- For open-ended questions, scenario analysis, or anything " "conversational, answer directly and concisely — do NOT ask for a JSON " @@ -142,12 +146,19 @@ def _build_starter_instruction() -> str: _CONTEXT_RETRIEVAL_INSTRUCTION = """\ -You are an equity-market intelligence specialist with web search. +You are a corporate-credit-market intelligence specialist with web search. Return a concise structured markdown summary (3-5 paragraphs) covering, as the -query warrants: the rate path and Fed guidance; recent inflation and jobs data; -the VIX and credit spreads; earnings-season tone; and major geopolitical or -policy shocks. +query warrants: +- Federal Reserve policy, interest-rate guidance, and Treasury-market changes; +- recent inflation, employment, and economic-growth data; +- corporate defaults, downgrades, and rating-agency developments; +- corporate refinancing pressure, funding conditions, and market liquidity; +- VIX, equity-market stress, and broader investor risk sentiment; +- financial-sector stress; + +Focus on the current credit-risk regime and information that could affect +short-horizon BAA10Y spread widening, tightening, volatility, or tail risk. Ground every claim in the search results you actually retrieve. When a cutoff date is specified, never report or speculate about events after it. @@ -175,7 +186,7 @@ def build_starter_agent_config( enable_search: bool = True, enable_code_exec: bool = False, ) -> AgentConfig: - """Build the S&P 500 starter :class:`AgentConfig`. + """Build the BAA10Y starter :class:`AgentConfig`. Parameters ---------- @@ -215,7 +226,7 @@ def build_starter_agent_config( ) return AgentConfig( - name="sp500_starter_agent", + name="baa10y_starter_agent", model=model, instruction=_STARTER_INSTRUCTION, # 16k headroom: enough for a complete run_code script + structured output. @@ -264,11 +275,6 @@ def build_starter_agent_predictor( ) -> AgentPredictor: """Wrap a starter :class:`AgentConfig` in an :class:`AgentPredictor`. - Wraps :class:`Sp500StarterPromptBuilder` so the (drift-free) continuous - output schema and a forecast directive ride in the payload — keeping the - schema out of the persona. ``predict(task, context)`` returns one - :class:`~aieng.forecasting.evaluation.prediction.Prediction` for the task's - single horizon. Parameters ---------- @@ -282,7 +288,7 @@ def build_starter_agent_predictor( return AgentPredictor( agent_config=config, prompt_builder=_StarterForecastPromptBuilder( - Sp500StarterPromptBuilder(covariate_series_ids=covariate_series_ids or []), + BAA10YStarterPromptBuilder(covariate_series_ids=covariate_series_ids or []), ContinuousAgentForecastOutput.prompt_schema_json(), ), output_schema=ContinuousAgentForecastOutput, diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md b/implementations/BAA10Y_forecasting/starter_agent/skills/code-analysis-playbook/SKILL.md similarity index 72% rename from implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md rename to implementations/BAA10Y_forecasting/starter_agent/skills/code-analysis-playbook/SKILL.md index 230fe87a..b506125f 100644 --- a/implementations/BAA10Y_forecasting /starter_agent/skills/code-analysis-playbook/SKILL.md +++ b/implementations/BAA10Y_forecasting/starter_agent/skills/code-analysis-playbook/SKILL.md @@ -12,6 +12,8 @@ description: >- A short guide to using the `run_code` sandbox productively. This is a starter skill — extend it with the diagnostics that matter for your problem. +# BAA10Y code-analysis playbook + ## Where your data lives All data comes from the **JSON payload in your context** — there are no disk @@ -42,10 +44,14 @@ between your low and high quantiles — wider when recent volatility is high. ## Domain focus (edit this for your use case) -For S&P 500 log-returns, the series is near-unforecastable in the mean: recent -realised volatility is far more predictable than direction. Let recent vol, not a -directional hunch, set the *width* of your quantile bands, and keep the point -forecast close to zero unless you have real signal. +For BAA10Y spread changes, the conditional mean may be difficult to forecast, +so keep the point forecast near zero unless the target history or covariates +provide a meaningful directional signal.Recent volatility is generally more informative for setting the width of the +forecast distribution. Positive values represent spread widening and negative +values represent spread tightening. During stressed periods, widening moves may +produce a larger positive tail. The 1-, 5-, and 21-business-day target series already represent cumulative +spread changes for their respective horizons. Forecast the selected target +directly rather than aggregating it again. ## Room to grow diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md b/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md similarity index 70% rename from implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md rename to implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md index 2986b32d..c5363ca5 100644 --- a/implementations/BAA10Y_forecasting /starter_agent/skills/forecasting/SKILL.md +++ b/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md @@ -42,10 +42,17 @@ how wide the bands are, and widen them as the horizon grows. ## Domain focus (edit this for your use case) -For S&P 500 log-returns, keep the point forecast near zero unless you have real -signal — returns are close to a random walk. Let recent realised volatility set -how WIDE the quantile bands are; it is far more predictable than direction. Note -any macro catalyst you lean on in the rationale. +For BAA10Y spread changes, keep the point forecast near zero unless the target +history or covariates provide a meaningful directional signal. Recent +volatility is generally more informative for determining the width of the +forecast distribution than for predicting direction. +Positive values represent spread widening and negative values represent spread +tightening. During stressed periods, widening moves may create a larger positive +tail than the magnitude of routine tightening. Allow this asymmetry only when +supported by recent history or available market covariates. +The target series already represents the cumulative BAA10Y spread change for +its configured horizon. Forecast it directly rather than aggregating it again. +Mention any important market or macro signal used in the rationale. ## Room to grow diff --git a/implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md b/implementations/BAA10Y_forecasting/starter_agent/skills/research-playbook/SKILL.md similarity index 65% rename from implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md rename to implementations/BAA10Y_forecasting/starter_agent/skills/research-playbook/SKILL.md index 50215fa7..52c018dd 100644 --- a/implementations/BAA10Y_forecasting /starter_agent/skills/research-playbook/SKILL.md +++ b/implementations/BAA10Y_forecasting/starter_agent/skills/research-playbook/SKILL.md @@ -35,10 +35,24 @@ knowledge. ## Domain focus (edit this for your use case) -For S&P 500 returns, the signals: the rate path and Fed guidance, recent -inflation and jobs prints, the VIX and credit spreads, earnings-season tone, and -major geopolitical or policy shocks. Search for the *current state* of these, -then remember returns are close to a random walk — be humble about direction. +For BAA10Y spread changes, focus searches on the current state of: + +- Federal Reserve policy and interest-rate guidance; +- inflation and employment releases; +- recession and economic-growth indicators; +- corporate defaults, downgrades, and credit-rating developments; +- corporate earnings, refinancing pressure, and funding conditions; +- financial-sector stress and market liquidity; +- VIX, equity-market stress, and investor risk sentiment; +- major geopolitical or policy shocks affecting credit markets. + +Search for information available on or before the forecast cutoff. Give greater +weight to Federal Reserve releases, official economic data, rating agencies, +company disclosures, and established financial-news sources. + +Use news primarily to identify the current credit-risk regime and possible tail +risk. Keep the directional forecast conservative unless multiple verified +signals consistently support spread widening or tightening. ## Room to grow diff --git a/implementations/food_price_forecasting/02_food_cpi_experiment.ipynb b/implementations/food_price_forecasting/02_food_cpi_experiment.ipynb index e5576a0e..20346585 100644 --- a/implementations/food_price_forecasting/02_food_cpi_experiment.ipynb +++ b/implementations/food_price_forecasting/02_food_cpi_experiment.ipynb @@ -2028,7 +2028,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, From 699161997acff3658d74c913c312404255cf1404 Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 03:45:54 +0000 Subject: [PATCH 06/24] replaced S&P 500 by BAA10Y in stress spec --- .../BAA10Y_forecasting /specs/baa10y_stress_2020.yaml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml b/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml index 2c7bf776..705b3bb9 100644 --- a/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml +++ b/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml @@ -25,7 +25,7 @@ tasks: horizons: [1] frequency: B description: >- - S&P 500 close-to-close cumulative change, 1 business day ahead + BAA10Y close-to-close cumulative change, 1 business day ahead (next-session change / direction). - task_id: baa10y_change_5b @@ -33,7 +33,7 @@ tasks: horizons: [5] frequency: B description: >- - S&P 500 close-to-close cumulative change, 5 business days ahead + BAA10Y close-to-close cumulative change, 5 business days ahead (forward 1-week change). - task_id: baa10y_change_21b @@ -41,7 +41,7 @@ tasks: horizons: [21] frequency: B description: >- - S&P 500 close-to-close cumulative change, 21 business days ahead + BAA10Y close-to-close cumulative change, 21 business days ahead (forward 1-month change). start: "2020-02-03" From 64db9a238301651b11da26151508b48a7b9bd3ac Mon Sep 17 00:00:00 2001 From: default Date: Sat, 18 Jul 2026 19:09:57 +0000 Subject: [PATCH 07/24] created 00_baa10y_data_exploration notebook --- .../00_baa10y_data_exploration.ipynb | 22101 ++++++++++++++++ 1 file changed, 22101 insertions(+) create mode 100644 implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb diff --git a/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb new file mode 100644 index 00000000..9bf68a77 --- /dev/null +++ b/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb @@ -0,0 +1,22101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fdbc501", + "metadata": {}, + "source": [ + "# BAA10Y Data Exploration\n", + "\n", + "| Series | Why it's here |\n", + "|---|---|\n", + "| `BAA10Y` | Moody's Seasoned Baa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity |\n", + "\n", + "**Before running this notebook**, populate the local data cache:\n", + "\n", + "```bash\n", + "uv run python scripts/fetch_fred.py\n", + "```\n", + "\n", + "After that, no network calls are made — the notebook reads entirely from\n", + "the local cache, which is what the `CutoffEnforcer` discipline requires." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4504704b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:29.656595Z", + "iopub.status.busy": "2026-07-18T18:48:29.656335Z", + "iopub.status.idle": "2026-07-18T18:48:31.682737Z", + "shell.execute_reply": "2026-07-18T18:48:31.681879Z" + } + }, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from pathlib import Path\n", + "\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "from aieng.forecasting.data import DataService, SeriesMetadata\n", + "from aieng.forecasting.data.adapters import StatCanAdapter\n", + "from aieng.forecasting.evaluation import ForecastingTask\n", + "\n", + "from data import build_baa10y_target_service" + ] + }, + { + "cell_type": "markdown", + "id": "f392ac73", + "metadata": {}, + "source": [ + "## 1. Build the DataService" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "884638ec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:31.692776Z", + "iopub.status.busy": "2026-07-18T18:48:31.692609Z", + "iopub.status.idle": "2026-07-18T18:48:31.900662Z", + "shell.execute_reply": "2026-07-18T18:48:31.899754Z" + } + }, + "outputs": [], + "source": [ + "svc = build_baa10y_target_service()" + ] + }, + { + "cell_type": "markdown", + "id": "6567148e", + "metadata": {}, + "source": [ + "## 2. Inspect the registered BAA10Y time series" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3d4d98ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_iddescriptionsourceunitsfrequencyn_obsstartend
0baa10y_change_1bBAA10Y cumulative spread change over 1 busines...FRED (BAA10Y), derivedpercentage-pointsB69112000-01-042026-06-30
1baa10y_change_21bBAA10Y cumulative spread change over 21 busine...FRED (BAA10Y), derivedpercentage-pointsB68912000-02-012026-06-30
2baa10y_change_5bBAA10Y cumulative spread change over 5 busines...FRED (BAA10Y), derivedpercentage-pointsB69072000-01-102026-06-30
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" + ], + "text/plain": [ + " series_id description \\\n", + "0 baa10y_change_1b BAA10Y cumulative spread change over 1 busines... \n", + "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", + "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", + "\n", + " source units frequency n_obs start \\\n", + "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", + "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", + "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", + "\n", + " end \n", + "0 2026-06-30 \n", + "1 2026-06-30 \n", + "2 2026-06-30 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "da1cd72e", + "metadata": {}, + "source": [ + "## 3. Cutoff filtering\n", + "\n", + "TODO" + ] + }, + { + "cell_type": "markdown", + "id": "0449690a", + "metadata": {}, + "source": [ + "## 4. Plot the three BAA10Y time series\n", + "\n", + "Code generated by Claude Sonnet 5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1bd6aed", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:31.913662Z", + "iopub.status.busy": "2026-07-18T18:48:31.913456Z", + "iopub.status.idle": "2026-07-18T18:48:33.482648Z", + "shell.execute_reply": "2026-07-18T18:48:33.481450Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#3987e5", + "width": 1.6 + }, + "mode": "lines", + "name": "1 business day", + "showlegend": false, + "type": "scatter", + "x": [ + "2000-01-04T00:00:00.000000", + "2000-01-05T00:00:00.000000", + "2000-01-06T00:00:00.000000", + "2000-01-07T00:00:00.000000", + "2000-01-10T00:00:00.000000", + "2000-01-11T00:00:00.000000", + "2000-01-12T00:00:00.000000", + "2000-01-13T00:00:00.000000", + 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"#283442", + "linecolor": "#506784", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#283442", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "matches": "x3", + "showticklabels": false + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 1 + ], + "matches": "x3", + "showticklabels": false + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Timestamp" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.7133333333333334, + 1 + ], + "title": { + "text": "Change (pp)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.3566666666666667, + 0.6433333333333333 + ], + "title": { + "text": "Change (pp)" + } + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 0.2866666666666667 + ], + "title": { + "text": "Change (pp)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "as_of = \"2027-1-1\"\n", + "series_ids = [\"baa10y_change_1b\", \"baa10y_change_5b\", \"baa10y_change_21b\"]\n", + "series_colors = {\n", + " \"baa10y_change_1b\": \"#3987e5\", # blue (dark-mode step)\n", + " \"baa10y_change_5b\": \"#008300\", # green\n", + " \"baa10y_change_21b\": \"#d55181\", # magenta (dark-mode step)\n", + "}\n", + "series_labels = {\n", + " \"baa10y_change_1b\": \"1 business day\",\n", + " \"baa10y_change_5b\": \"5 business days\",\n", + " \"baa10y_change_21b\": \"21 business days\",\n", + "}\n", + "recession_periods = [\n", + " (\"2001-03-01\", \"2001-11-01\"),\n", + " (\"2007-12-01\", \"2009-06-01\"),\n", + " (\"2020-02-01\", \"2020-04-01\"),\n", + "]\n", + "\n", + "# Each horizon's cumulative change lives on a very different scale (1b is tight\n", + "# around zero, 21b swings much wider), so they get their own y-axis per row\n", + "# rather than being forced onto one shared scale.\n", + "fig = make_subplots(\n", + " rows=len(series_ids),\n", + " cols=1,\n", + " shared_xaxes=True,\n", + " vertical_spacing=0.07,\n", + " subplot_titles=[series_labels[sid] for sid in series_ids],\n", + ")\n", + "\n", + "for row, series_id in enumerate(series_ids, start=1):\n", + " s = svc.get_series(series_id, as_of=as_of).sort_values(\"timestamp\")\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=s[\"timestamp\"],\n", + " y=s[\"value\"],\n", + " mode=\"lines\",\n", + " name=series_labels[series_id],\n", + " line=dict(color=series_colors[series_id], width=1.6),\n", + " showlegend=False,\n", + " ),\n", + " row=row,\n", + " col=1,\n", + " )\n", + " fig.update_yaxes(title_text=\"Change (pp)\", row=row, col=1)\n", + "\n", + "for start, end in recession_periods:\n", + " fig.add_vrect(\n", + " x0=start,\n", + " x1=end,\n", + " fillcolor=\"#ffffff\",\n", + " opacity=0.14,\n", + " line_width=0,\n", + " row=\"all\",\n", + " col=1,\n", + " )\n", + "\n", + "fig.update_xaxes(title_text=\"Timestamp\", row=len(series_ids), col=1)\n", + "fig.update_layout(\n", + " title=\"BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)\",\n", + " template=\"plotly_dark\",\n", + " height=700,\n", + ")\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From d567ce5623b4fe4637eabf067750edb52efe9d20 Mon Sep 17 00:00:00 2001 From: default Date: Sun, 19 Jul 2026 01:54:53 +0000 Subject: [PATCH 08/24] Consolidate duplicate BAA10Y forecasting folders --- .../00_baa10y_data_exploration.ipynb | 22101 ---------------- .../specs/baa10y_backtest_2025.yaml | 0 .../specs/baa10y_eval_2026.yaml | 0 .../specs/baa10y_smoke.yaml | 0 .../specs/baa10y_stress_2020.yaml | 0 .../specs/sp500_backtest_2025.yaml | 48 - .../specs/sp500_eval_2026.yaml | 51 - .../BAA10Y_forecasting/specs/sp500_smoke.yaml | 53 - .../specs/sp500_stress_2020.yaml | 50 - .../specs/baa10y_smoke.yaml | 52 - 10 files changed, 22355 deletions(-) delete mode 100644 implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/baa10y_backtest_2025.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/baa10y_eval_2026.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/baa10y_smoke.yaml (100%) rename implementations/{BAA10Y_forecasting => BAA10Y_forecasting}/specs/baa10y_stress_2020.yaml (100%) delete mode 100644 implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml delete mode 100644 implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml delete mode 100644 implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml delete mode 100644 implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml delete mode 100644 implementations/baa10y_forecasting/specs/baa10y_smoke.yaml diff --git a/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb deleted file mode 100644 index 9bf68a77..00000000 --- a/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb +++ /dev/null @@ -1,22101 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7fdbc501", - "metadata": {}, - "source": [ - "# BAA10Y Data Exploration\n", - "\n", - "| Series | Why it's here |\n", - "|---|---|\n", - "| `BAA10Y` | Moody's Seasoned Baa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity |\n", - "\n", - "**Before running this notebook**, populate the local data cache:\n", - "\n", - "```bash\n", - "uv run python scripts/fetch_fred.py\n", - "```\n", - "\n", - "After that, no network calls are made — the notebook reads entirely from\n", - "the local cache, which is what the `CutoffEnforcer` discipline requires." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4504704b", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-18T18:48:29.656595Z", - "iopub.status.busy": "2026-07-18T18:48:29.656335Z", - "iopub.status.idle": "2026-07-18T18:48:31.682737Z", - "shell.execute_reply": "2026-07-18T18:48:31.681879Z" - } - }, - "outputs": [], - "source": [ - "from datetime import datetime\n", - "from pathlib import Path\n", - "\n", - "import plotly.graph_objects as go\n", - "from plotly.subplots import make_subplots\n", - "\n", - "from aieng.forecasting.data import DataService, SeriesMetadata\n", - "from aieng.forecasting.data.adapters import StatCanAdapter\n", - "from aieng.forecasting.evaluation import ForecastingTask\n", - "\n", - "from data import build_baa10y_target_service" - ] - }, - { - "cell_type": "markdown", - "id": "f392ac73", - "metadata": {}, - "source": [ - "## 1. Build the DataService" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "884638ec", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-18T18:48:31.692776Z", - "iopub.status.busy": "2026-07-18T18:48:31.692609Z", - "iopub.status.idle": "2026-07-18T18:48:31.900662Z", - "shell.execute_reply": "2026-07-18T18:48:31.899754Z" - } - }, - "outputs": [], - "source": [ - "svc = build_baa10y_target_service()" - ] - }, - { - "cell_type": "markdown", - "id": "6567148e", - "metadata": {}, - "source": [ - "## 2. Inspect the registered BAA10Y time series" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "3d4d98ad", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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0baa10y_change_1bBAA10Y cumulative spread change over 1 busines...FRED (BAA10Y), derivedpercentage-pointsB69112000-01-042026-06-30
1baa10y_change_21bBAA10Y cumulative spread change over 21 busine...FRED (BAA10Y), derivedpercentage-pointsB68912000-02-012026-06-30
2baa10y_change_5bBAA10Y cumulative spread change over 5 busines...FRED (BAA10Y), derivedpercentage-pointsB69072000-01-102026-06-30
\n", - "
" - ], - "text/plain": [ - " series_id description \\\n", - "0 baa10y_change_1b BAA10Y cumulative spread change over 1 busines... \n", - "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", - "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", - "\n", - " source units frequency n_obs start \\\n", - "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", - "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", - "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", - "\n", - " end \n", - "0 2026-06-30 \n", - "1 2026-06-30 \n", - "2 2026-06-30 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "svc.summary()" - ] - }, - { - "cell_type": "markdown", - "id": "da1cd72e", - "metadata": {}, - "source": [ - "## 3. Cutoff filtering\n", - "\n", - "TODO" - ] - }, - { - "cell_type": "markdown", - "id": "0449690a", - "metadata": {}, - "source": [ - "## 4. Plot the three BAA10Y time series\n", - "\n", - "Code generated by Claude Sonnet 5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d1bd6aed", - "metadata": { - "execution": { - "iopub.execute_input": "2026-07-18T18:48:31.913662Z", - "iopub.status.busy": "2026-07-18T18:48:31.913456Z", - "iopub.status.idle": "2026-07-18T18:48:33.482648Z", - "shell.execute_reply": "2026-07-18T18:48:33.481450Z" - } - }, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "line": { - "color": "#3987e5", - "width": 1.6 - }, - "mode": "lines", - "name": "1 business day", - "showlegend": false, - "type": "scatter", - "x": [ - "2000-01-04T00:00:00.000000", - "2000-01-05T00:00:00.000000", - "2000-01-06T00:00:00.000000", - "2000-01-07T00:00:00.000000", - "2000-01-10T00:00:00.000000", - "2000-01-11T00:00:00.000000", - "2000-01-12T00:00:00.000000", - "2000-01-13T00:00:00.000000", - 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"#283442", - "linecolor": "#506784", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "#283442", - "zerolinewidth": 2 - } - } - }, - "title": { - "text": "BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)" - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0, - 1 - ], - "matches": "x3", - "showticklabels": false - }, - "xaxis2": { - "anchor": "y2", - "domain": [ - 0, - 1 - ], - "matches": "x3", - "showticklabels": false - }, - "xaxis3": { - "anchor": "y3", - "domain": [ - 0, - 1 - ], - "title": { - "text": "Timestamp" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0.7133333333333334, - 1 - ], - "title": { - "text": "Change (pp)" - } - }, - "yaxis2": { - "anchor": "x2", - "domain": [ - 0.3566666666666667, - 0.6433333333333333 - ], - "title": { - "text": "Change (pp)" - } - }, - "yaxis3": { - "anchor": "x3", - "domain": [ - 0, - 0.2866666666666667 - ], - "title": { - "text": "Change (pp)" - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "as_of = \"2027-1-1\"\n", - "series_ids = [\"baa10y_change_1b\", \"baa10y_change_5b\", \"baa10y_change_21b\"]\n", - "series_colors = {\n", - " \"baa10y_change_1b\": \"#3987e5\", # blue (dark-mode step)\n", - " \"baa10y_change_5b\": \"#008300\", # green\n", - " \"baa10y_change_21b\": \"#d55181\", # magenta (dark-mode step)\n", - "}\n", - "series_labels = {\n", - " \"baa10y_change_1b\": \"1 business day\",\n", - " \"baa10y_change_5b\": \"5 business days\",\n", - " \"baa10y_change_21b\": \"21 business days\",\n", - "}\n", - "recession_periods = [\n", - " (\"2001-03-01\", \"2001-11-01\"),\n", - " (\"2007-12-01\", \"2009-06-01\"),\n", - " (\"2020-02-01\", \"2020-04-01\"),\n", - "]\n", - "\n", - "# Each horizon's cumulative change lives on a very different scale (1b is tight\n", - "# around zero, 21b swings much wider), so they get their own y-axis per row\n", - "# rather than being forced onto one shared scale.\n", - "fig = make_subplots(\n", - " rows=len(series_ids),\n", - " cols=1,\n", - " shared_xaxes=True,\n", - " vertical_spacing=0.07,\n", - " subplot_titles=[series_labels[sid] for sid in series_ids],\n", - ")\n", - "\n", - "for row, series_id in enumerate(series_ids, start=1):\n", - " s = svc.get_series(series_id, as_of=as_of).sort_values(\"timestamp\")\n", - " fig.add_trace(\n", - " go.Scatter(\n", - " x=s[\"timestamp\"],\n", - " y=s[\"value\"],\n", - " mode=\"lines\",\n", - " name=series_labels[series_id],\n", - " line=dict(color=series_colors[series_id], width=1.6),\n", - " showlegend=False,\n", - " ),\n", - " row=row,\n", - " col=1,\n", - " )\n", - " fig.update_yaxes(title_text=\"Change (pp)\", row=row, col=1)\n", - "\n", - "for start, end in recession_periods:\n", - " fig.add_vrect(\n", - " x0=start,\n", - " x1=end,\n", - " fillcolor=\"#ffffff\",\n", - " opacity=0.14,\n", - " line_width=0,\n", - " row=\"all\",\n", - " col=1,\n", - " )\n", - "\n", - "fig.update_xaxes(title_text=\"Timestamp\", row=len(series_ids), col=1)\n", - "fig.update_layout(\n", - " title=\"BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)\",\n", - " template=\"plotly_dark\",\n", - " height=700,\n", - ")\n", - "fig.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".venv (3.12.3)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.3" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml b/implementations/BAA10Y_forecasting/specs/baa10y_backtest_2025.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/baa10y_backtest_2025.yaml rename to implementations/BAA10Y_forecasting/specs/baa10y_backtest_2025.yaml diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml b/implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/baa10y_eval_2026.yaml rename to implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml b/implementations/BAA10Y_forecasting/specs/baa10y_smoke.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/baa10y_smoke.yaml rename to implementations/BAA10Y_forecasting/specs/baa10y_smoke.yaml diff --git a/implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml b/implementations/BAA10Y_forecasting/specs/baa10y_stress_2020.yaml similarity index 100% rename from implementations/BAA10Y_forecasting /specs/baa10y_stress_2020.yaml rename to implementations/BAA10Y_forecasting/specs/baa10y_stress_2020.yaml diff --git a/implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml b/implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml deleted file mode 100644 index e1ab30ed..00000000 --- a/implementations/BAA10Y_forecasting/specs/sp500_backtest_2025.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# Main backtest spec — weekly origins across 2025 (post-cutoff). -# -# 2025 is after the Gemini training cutoff (~Jan 2025), so this is the window -# where the conventional methods AND the LLM-Process can be compared *fairly* — -# the LLM has not memorised these outcomes. Mirrors the energy reference's 2025 -# backtest window. Use it for open iteration; spend the protected 2026 eval -# (`sp500_eval_2026.yaml`) sparingly on your finalists. -# -# This spec carries experiment design only (window + one single-horizon task per -# target). The predictor roster and hyperparameters live in the notebook. Note -# the LLMP predictors are token-heavy over ~50 weekly origins — the notebook lets -# you trim the predictor list (or widen the stride here) before enabling them. - -spec_id: sp500_backtest_2025 - -description: >- - Main multivariate comparison: weekly origins across 2025 (post-cutoff), - forecasting close-to-close cumulative returns at 1/5/21 business days. - -tasks: - - task_id: sp500_logret_1b - target_series_id: sp500_logret_1b - horizons: [1] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 1 business day ahead - (next-session return / direction). - - - task_id: sp500_logret_5b - target_series_id: sp500_logret_5b - horizons: [5] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 5 business days ahead - (forward 1-week return). - - - task_id: sp500_logret_21b - target_series_id: sp500_logret_21b - horizons: [21] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 21 business days ahead - (forward 1-month return). - -start: "2025-01-06" -end: "2025-12-22" -stride: 5 # weekly origins (~50) -warmup: 250 diff --git a/implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml b/implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml deleted file mode 100644 index 564368d0..00000000 --- a/implementations/BAA10Y_forecasting/specs/sp500_eval_2026.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# Protected eval — held-out 2026 window, scored through multi_evaluate() with a budget. -# -# This is the honest scoreboard. 2026 is unambiguously after the Gemini training -# cutoff, so neither the numerical methods nor the LLM-Process can have seen the -# outcomes. Treat it as scarce: `max_runs` caps how many times the spec may be -# scored (via EvalTracker), so iterate on `sp500_backtest_2025.yaml` and spend -# this only on a curated set of finalists (chosen in the notebook's eval cell). -# -# Loaded as a MultiTargetEvalSpec: one single-horizon task per target, all under -# a single shared run budget. One multi_evaluate() call across all three -# horizons counts as ONE run against `max_runs` — the budget is keyed by -# `spec_id`, not per-horizon. The predictor roster lives in the notebook. - -spec_id: sp500_eval_2026 - -description: >- - Protected eval: weekly origins across early/mid 2026 (post-cutoff), - forecasting close-to-close cumulative returns at 1/5/21 business days. - -tasks: - - task_id: sp500_logret_1b - target_series_id: sp500_logret_1b - horizons: [1] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 1 business day ahead - (next-session return / direction). - - - task_id: sp500_logret_5b - target_series_id: sp500_logret_5b - horizons: [5] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 5 business days ahead - (forward 1-week return). - - - task_id: sp500_logret_21b - target_series_id: sp500_logret_21b - horizons: [21] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 21 business days ahead - (forward 1-month return). - -start: "2026-02-02" -end: "2026-03-23" # 8 weekly origins; all resolve at h=21 -stride: 5 # weekly origins -warmup: 250 - -# Budget cap: each multi_evaluate() call (all 3 horizons) counts as one run. -max_runs: 5 diff --git a/implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml b/implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml deleted file mode 100644 index bc873e54..00000000 --- a/implementations/BAA10Y_forecasting/specs/sp500_smoke.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# Smoke spec — fast laptop run over a short, post-cutoff (2025) window. -# -# Weekly origins in late 2025: after the Gemini training cutoff (~Jan 2025), so -# the LLM-Process rows in the notebook can be compared *fairly* against the -# conventional methods here. The notebook keeps its LLMP predictors ON for this -# window (the predictors cell gates them on a post-cutoff flag). -# -# This spec carries experiment design only — the window, stride/warmup, and one -# single-horizon task per target. WHICH predictors run (and all their -# hyperparameters, including the covariate panel) is configured in the notebook, -# not here. Each task targets `sp500_logret_{N}b` (the close-to-close cumulative -# log return over N business days), so forecasting it N steps ahead resolves to -# the forward N-session return. -# -# Prerequisites (warm caches to the present first): -# uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC -# uv run python scripts/fetch_fred.py # macro covariates - -spec_id: sp500_smoke - -description: >- - Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), - forecasting close-to-close cumulative returns at 1/5/21 business days. - -tasks: - - task_id: sp500_logret_1b - target_series_id: sp500_logret_1b - horizons: [1] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 1 business day ahead - (next-session return / direction). - - - task_id: sp500_logret_5b - target_series_id: sp500_logret_5b - horizons: [5] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 5 business days ahead - (forward 1-week return). - - - task_id: sp500_logret_21b - target_series_id: sp500_logret_21b - horizons: [21] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 21 business days ahead - (forward 1-month return). - -start: "2025-10-06" -end: "2025-11-14" -stride: 5 # weekly origins (~6) -warmup: 250 diff --git a/implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml b/implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml deleted file mode 100644 index 2e1f3c14..00000000 --- a/implementations/BAA10Y_forecasting/specs/sp500_stress_2020.yaml +++ /dev/null @@ -1,50 +0,0 @@ -# Regime-stress spec — the 2020 COVID crash, NUMERICAL METHODS ONLY. -# -# ⚠️ Keep the notebook's LLM-Process predictors OFF for this window. 2020 is -# BEFORE the Gemini training cutoff (~Jan 2025), so an LLM has effectively -# memorised these outcomes — scoring an LLMP here measures recall, not -# forecasting, and would silently flatter it in the comparison. The numerical -# methods are cutoff-safe by construction (they only see the series up to the -# origin), so this volatile window is a perfectly valid stress test *for them* — -# it's where a covariate edge is most visible. -# -# The notebook enforces "numerical only" in code: its predictors cell gates the -# LLMP variants on a post-cutoff flag that is False for this config. Use this to -# study "when do covariates help?" among the conventional methods; use the 2025 -# backtest / 2026 eval for anything involving the LLMP. - -spec_id: sp500_stress_2020 - -description: >- - COVID-crash regime stress (numerical methods only): daily origins Feb–Apr - 2020, forecasting close-to-close cumulative returns at 1/5/21 business days. - -tasks: - - task_id: sp500_logret_1b - target_series_id: sp500_logret_1b - horizons: [1] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 1 business day ahead - (next-session return / direction). - - - task_id: sp500_logret_5b - target_series_id: sp500_logret_5b - horizons: [5] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 5 business days ahead - (forward 1-week return). - - - task_id: sp500_logret_21b - target_series_id: sp500_logret_21b - horizons: [21] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 21 business days ahead - (forward 1-month return). - -start: "2020-02-03" -end: "2020-04-30" -stride: 1 # daily origins across the crash -warmup: 250 diff --git a/implementations/baa10y_forecasting/specs/baa10y_smoke.yaml b/implementations/baa10y_forecasting/specs/baa10y_smoke.yaml deleted file mode 100644 index a5764c24..00000000 --- a/implementations/baa10y_forecasting/specs/baa10y_smoke.yaml +++ /dev/null @@ -1,52 +0,0 @@ -# Smoke spec — fast laptop run over a short, post-cutoff (2025) window. -# -# Weekly origins in late 2025: after the Gemini training cutoff (~Jan 2025), so -# the LLM-Process rows in the notebook can be compared *fairly* against the -# conventional methods here. The notebook keeps its LLMP predictors ON for this -# window (the predictors cell gates them on a post-cutoff flag). -# -# This spec carries experiment design only — the window, stride/warmup, and one -# single-horizon task per target. WHICH predictors run (and all their -# hyperparameters, including the covariate panel) is configured in the notebook, -# not here. Each task targets `baa10y_{N}b` (the close-to-close spread change over N business days), so forecasting it N steps ahead resolves to -# the forward N-session return. -# -# Prerequisites (warm caches to the present first): -# uv run python scripts/fetch_sp500_market.py --refresh # ^GSPC / ^VIX / ^IXIC -# uv run python scripts/fetch_fred.py # macro covariates - -spec_id: baa10y_smoke - -description: >- - Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), - forecasting close-to-close cumulative returns at 1/5/21 business days. - -tasks: - - task_id: baa10y_1b - target_series_id: baa10y_1b - horizons: [1] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 1 business day ahead - (next-session return / direction). - - - task_id: sp500_logret_5b - target_series_id: sp500_logret_5b - horizons: [5] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 5 business days ahead - (forward 1-week return). - - - task_id: sp500_logret_21b - target_series_id: sp500_logret_21b - horizons: [21] - frequency: B - description: >- - S&P 500 close-to-close cumulative log return, 21 business days ahead - (forward 1-month return). - -start: "2025-10-06" -end: "2025-11-14" -stride: 5 # weekly origins (~6) -warmup: 250 From 22dfbaadd4bc2f66195c84b3fdb795ff833d3082 Mon Sep 17 00:00:00 2001 From: default Date: Sun, 19 Jul 2026 02:32:53 +0000 Subject: [PATCH 09/24] Merge baa10y data exploration.ipynb --- .../00_baa10y_data_exploration.ipynb | 22101 ++++++++++++++++ 1 file changed, 22101 insertions(+) create mode 100644 implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb diff --git a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb new file mode 100644 index 00000000..9bf68a77 --- /dev/null +++ b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb @@ -0,0 +1,22101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7fdbc501", + "metadata": {}, + "source": [ + "# BAA10Y Data Exploration\n", + "\n", + "| Series | Why it's here |\n", + "|---|---|\n", + "| `BAA10Y` | Moody's Seasoned Baa Corporate Bond Yield Relative to Yield on 10-Year Treasury Constant Maturity |\n", + "\n", + "**Before running this notebook**, populate the local data cache:\n", + "\n", + "```bash\n", + "uv run python scripts/fetch_fred.py\n", + "```\n", + "\n", + "After that, no network calls are made — the notebook reads entirely from\n", + "the local cache, which is what the `CutoffEnforcer` discipline requires." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4504704b", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:29.656595Z", + "iopub.status.busy": "2026-07-18T18:48:29.656335Z", + "iopub.status.idle": "2026-07-18T18:48:31.682737Z", + "shell.execute_reply": "2026-07-18T18:48:31.681879Z" + } + }, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from pathlib import Path\n", + "\n", + "import plotly.graph_objects as go\n", + "from plotly.subplots import make_subplots\n", + "\n", + "from aieng.forecasting.data import DataService, SeriesMetadata\n", + "from aieng.forecasting.data.adapters import StatCanAdapter\n", + "from aieng.forecasting.evaluation import ForecastingTask\n", + "\n", + "from data import build_baa10y_target_service" + ] + }, + { + "cell_type": "markdown", + "id": "f392ac73", + "metadata": {}, + "source": [ + "## 1. Build the DataService" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "884638ec", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:31.692776Z", + "iopub.status.busy": "2026-07-18T18:48:31.692609Z", + "iopub.status.idle": "2026-07-18T18:48:31.900662Z", + "shell.execute_reply": "2026-07-18T18:48:31.899754Z" + } + }, + "outputs": [], + "source": [ + "svc = build_baa10y_target_service()" + ] + }, + { + "cell_type": "markdown", + "id": "6567148e", + "metadata": {}, + "source": [ + "## 2. Inspect the registered BAA10Y time series" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "3d4d98ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_iddescriptionsourceunitsfrequencyn_obsstartend
0baa10y_change_1bBAA10Y cumulative spread change over 1 busines...FRED (BAA10Y), derivedpercentage-pointsB69112000-01-042026-06-30
1baa10y_change_21bBAA10Y cumulative spread change over 21 busine...FRED (BAA10Y), derivedpercentage-pointsB68912000-02-012026-06-30
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" + ], + "text/plain": [ + " series_id description \\\n", + "0 baa10y_change_1b BAA10Y cumulative spread change over 1 busines... \n", + "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", + "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", + "\n", + " source units frequency n_obs start \\\n", + "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", + "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", + "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", + "\n", + " end \n", + "0 2026-06-30 \n", + "1 2026-06-30 \n", + "2 2026-06-30 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "svc.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "da1cd72e", + "metadata": {}, + "source": [ + "## 3. Cutoff filtering\n", + "\n", + "TODO" + ] + }, + { + "cell_type": "markdown", + "id": "0449690a", + "metadata": {}, + "source": [ + "## 4. Plot the three BAA10Y time series\n", + "\n", + "Code generated by Claude Sonnet 5." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1bd6aed", + "metadata": { + "execution": { + "iopub.execute_input": "2026-07-18T18:48:31.913662Z", + "iopub.status.busy": "2026-07-18T18:48:31.913456Z", + "iopub.status.idle": "2026-07-18T18:48:33.482648Z", + "shell.execute_reply": "2026-07-18T18:48:33.481450Z" + } + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#3987e5", + "width": 1.6 + }, + "mode": "lines", + "name": "1 business day", + "showlegend": false, + "type": "scatter", + "x": [ + "2000-01-04T00:00:00.000000", + "2000-01-05T00:00:00.000000", + "2000-01-06T00:00:00.000000", + "2000-01-07T00:00:00.000000", + "2000-01-10T00:00:00.000000", + "2000-01-11T00:00:00.000000", + "2000-01-12T00:00:00.000000", + "2000-01-13T00:00:00.000000", + 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"#283442", + "linecolor": "#506784", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#283442", + "zerolinewidth": 2 + } + } + }, + "title": { + "text": "BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)" + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 1 + ], + "matches": "x3", + "showticklabels": false + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0, + 1 + ], + "matches": "x3", + "showticklabels": false + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0, + 1 + ], + "title": { + "text": "Timestamp" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.7133333333333334, + 1 + ], + "title": { + "text": "Change (pp)" + } + }, + "yaxis2": { + "anchor": "x2", + "domain": [ + 0.3566666666666667, + 0.6433333333333333 + ], + "title": { + "text": "Change (pp)" + } + }, + "yaxis3": { + "anchor": "x3", + "domain": [ + 0, + 0.2866666666666667 + ], + "title": { + "text": "Change (pp)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "as_of = \"2027-1-1\"\n", + "series_ids = [\"baa10y_change_1b\", \"baa10y_change_5b\", \"baa10y_change_21b\"]\n", + "series_colors = {\n", + " \"baa10y_change_1b\": \"#3987e5\", # blue (dark-mode step)\n", + " \"baa10y_change_5b\": \"#008300\", # green\n", + " \"baa10y_change_21b\": \"#d55181\", # magenta (dark-mode step)\n", + "}\n", + "series_labels = {\n", + " \"baa10y_change_1b\": \"1 business day\",\n", + " \"baa10y_change_5b\": \"5 business days\",\n", + " \"baa10y_change_21b\": \"21 business days\",\n", + "}\n", + "recession_periods = [\n", + " (\"2001-03-01\", \"2001-11-01\"),\n", + " (\"2007-12-01\", \"2009-06-01\"),\n", + " (\"2020-02-01\", \"2020-04-01\"),\n", + "]\n", + "\n", + "# Each horizon's cumulative change lives on a very different scale (1b is tight\n", + "# around zero, 21b swings much wider), so they get their own y-axis per row\n", + "# rather than being forced onto one shared scale.\n", + "fig = make_subplots(\n", + " rows=len(series_ids),\n", + " cols=1,\n", + " shared_xaxes=True,\n", + " vertical_spacing=0.07,\n", + " subplot_titles=[series_labels[sid] for sid in series_ids],\n", + ")\n", + "\n", + "for row, series_id in enumerate(series_ids, start=1):\n", + " s = svc.get_series(series_id, as_of=as_of).sort_values(\"timestamp\")\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=s[\"timestamp\"],\n", + " y=s[\"value\"],\n", + " mode=\"lines\",\n", + " name=series_labels[series_id],\n", + " line=dict(color=series_colors[series_id], width=1.6),\n", + " showlegend=False,\n", + " ),\n", + " row=row,\n", + " col=1,\n", + " )\n", + " fig.update_yaxes(title_text=\"Change (pp)\", row=row, col=1)\n", + "\n", + "for start, end in recession_periods:\n", + " fig.add_vrect(\n", + " x0=start,\n", + " x1=end,\n", + " fillcolor=\"#ffffff\",\n", + " opacity=0.14,\n", + " line_width=0,\n", + " row=\"all\",\n", + " col=1,\n", + " )\n", + "\n", + "fig.update_xaxes(title_text=\"Timestamp\", row=len(series_ids), col=1)\n", + "fig.update_layout(\n", + " title=\"BAA10Y cumulative spread change by horizon (independent y-scales, shaded: NBER recessions)\",\n", + " template=\"plotly_dark\",\n", + " height=700,\n", + ")\n", + "fig.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv (3.12.3)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 1f2c4ea69ec8c63b175acbb1956aac10088f3714 Mon Sep 17 00:00:00 2001 From: default Date: Sun, 19 Jul 2026 14:51:34 +0000 Subject: [PATCH 10/24] added covariate service and plots --- .../00_baa10y_data_exploration.ipynb | 29180 +++++++++++++++- 1 file changed, 29173 insertions(+), 7 deletions(-) diff --git a/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb index 9bf68a77..df4e53b4 100644 --- a/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb +++ b/implementations/BAA10Y_forecasting /00_baa10y_data_exploration.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "4504704b", "metadata": { "execution": { @@ -53,12 +53,12 @@ "id": "f392ac73", "metadata": {}, "source": [ - "## 1. Build the DataService" + "## 1. Build the BAA10Y DataService" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "id": "884638ec", "metadata": { "execution": { @@ -70,7 +70,7 @@ }, "outputs": [], "source": [ - "svc = build_baa10y_target_service()" + "svc = build_baa10y_target_service(windows=(1, 5, 21))" ] }, { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "id": "3d4d98ad", "metadata": {}, "outputs": [ @@ -173,7 +173,7 @@ "2 2026-06-30 " ] }, - "execution_count": 18, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "d1bd6aed", "metadata": { "execution": { @@ -22075,6 +22075,29172 @@ ")\n", "fig.show()" ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "75cac7f3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coder/agentic-forecasting/implementations/sp500_forecasting/data.py:765: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", + " _handle_covariate_error(SERIES_ID_GOLD_RETURN, exc)\n" + ] + }, + { + "data": { + "text/html": [ + "
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series_iddescriptionsourceunitsfrequencyn_obsstartend
0cpi_mom_logdiff_l1bUS CPI MoM log change, conservative release la...FRED (CPIAUCSL), derivedlog-changeB27172016-01-152026-06-15
1dollar_index_log_ret_1b_l1bTrade-weighted dollar index log return, lagged...FRED (DTWEXBGS), derivedlog-returnB53472006-01-042026-07-02
2fed_funds_level_l1bEffective federal funds rate, lagged 1 busines...FRED (DFF)percentB187871954-07-022026-07-06
3nasdaq_log_ret_1b_l1bNASDAQ Composite close-to-close log return, la...Yahoo Finance (^IXIC), derivedlog-returnB26472016-01-062026-07-17
4oil_log_ret_1b_l1bWTI oil spot log return, lagged 1 business dayFRED (DCOILWTICO), derivedlog-returnB105611986-01-062026-06-29
5unemployment_rate_l1bUS unemployment rate, conservative release lag...FRED (UNRATE)percentB27392016-01-152026-07-15
6ust10y_level_l1bUS 10-year Treasury yield level, lagged 1 busi...FRED (DGS10)percentB168291962-01-032026-07-06
7ust2y10y_spread_l1bUS 10Y minus 2Y Treasury spread, lagged 1 busi...FRED (DGS10, DGS2), derivedpercent-pointsB130691976-06-022026-07-06
8vix_level_l1bCBOE VIX close level, lagged 1 business dayYahoo Finance (^VIX)index-levelB26472016-01-052026-07-15
9vix_log_ret_1b_l1bCBOE VIX close-to-close log return, lagged 1 b...Yahoo Finance (^VIX), derivedlog-returnB26462016-01-062026-07-15
\n", + "
" + ], + "text/plain": [ + " series_id \\\n", + "0 cpi_mom_logdiff_l1b \n", + "1 dollar_index_log_ret_1b_l1b \n", + "2 fed_funds_level_l1b \n", + "3 nasdaq_log_ret_1b_l1b \n", + "4 oil_log_ret_1b_l1b \n", + "5 unemployment_rate_l1b \n", + "6 ust10y_level_l1b \n", + "7 ust2y10y_spread_l1b \n", + "8 vix_level_l1b \n", + "9 vix_log_ret_1b_l1b \n", + "\n", + " description \\\n", + "0 US CPI MoM log change, conservative release la... \n", + "1 Trade-weighted dollar index log return, lagged... \n", + "2 Effective federal funds rate, lagged 1 busines... \n", + "3 NASDAQ Composite close-to-close log return, la... \n", + "4 WTI oil spot log return, lagged 1 business day \n", + "5 US unemployment rate, conservative release lag... \n", + "6 US 10-year Treasury yield level, lagged 1 busi... \n", + "7 US 10Y minus 2Y Treasury spread, lagged 1 busi... \n", + "8 CBOE VIX close level, lagged 1 business day \n", + "9 CBOE VIX close-to-close log return, lagged 1 b... \n", + "\n", + " source units frequency n_obs start \\\n", + "0 FRED (CPIAUCSL), derived log-change B 2717 2016-01-15 \n", + "1 FRED (DTWEXBGS), derived log-return B 5347 2006-01-04 \n", + "2 FRED (DFF) percent B 18787 1954-07-02 \n", + "3 Yahoo Finance (^IXIC), derived log-return B 2647 2016-01-06 \n", + "4 FRED (DCOILWTICO), derived log-return B 10561 1986-01-06 \n", + "5 FRED (UNRATE) percent B 2739 2016-01-15 \n", + "6 FRED (DGS10) percent B 16829 1962-01-03 \n", + "7 FRED (DGS10, DGS2), derived percent-points B 13069 1976-06-02 \n", + "8 Yahoo Finance (^VIX) index-level B 2647 2016-01-05 \n", + "9 Yahoo Finance (^VIX), derived log-return B 2646 2016-01-06 \n", + "\n", + " end \n", + "0 2026-06-15 \n", + "1 2026-07-02 \n", + "2 2026-07-06 \n", + "3 2026-07-17 \n", + "4 2026-06-29 \n", + "5 2026-07-15 \n", + "6 2026-07-06 \n", + "7 2026-07-06 \n", + "8 2026-07-15 \n", + "9 2026-07-15 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", + "\n", + "DATA_HISTORY_START = \"2016-01-01\"\n", + "REFRESH_CACHE = False\n", + "\n", + "covariate_svc = build_sp500_multivariate_service(\n", + " windows=(), #(1, 5, 21),\n", + " include_covariates=True,\n", + " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", + " start=DATA_HISTORY_START,\n", + " refresh=REFRESH_CACHE,\n", + ")\n", + "\n", + "covariate_svc.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "1c9c661e", + "metadata": {}, + "source": [ + "## 5. Plot the covariate time series\n", + "\n", + "Code generated by Claude Sonnet 5." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "41467da9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#3987e5", + "width": 1.2 + }, + "mode": "lines", + "name": "cpi_mom_logdiff_l1b", + "showlegend": false, + "type": "scatter", + "x": [ + "2016-01-15T00:00:00.000000", + "2016-01-18T00:00:00.000000", + "2016-01-19T00:00:00.000000", + "2016-01-20T00:00:00.000000", + "2016-01-21T00:00:00.000000", + "2016-01-22T00:00:00.000000", + "2016-01-25T00:00:00.000000", + "2016-01-26T00:00:00.000000", + "2016-01-27T00:00:00.000000", + "2016-01-28T00:00:00.000000", + "2016-01-29T00:00:00.000000", + "2016-02-01T00:00:00.000000", + "2016-02-02T00:00:00.000000", + "2016-02-03T00:00:00.000000", + "2016-02-04T00:00:00.000000", + "2016-02-05T00:00:00.000000", + 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"domain": [ + 0.7140000000000001, + 0.796 + ], + "title": { + "text": "percent" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.612, + 0.694 + ], + "title": { + "text": "log-return" + } + }, + "yaxis5": { + "anchor": "x5", + "domain": [ + 0.51, + 0.592 + ], + "title": { + "text": "log-return" + } + }, + "yaxis6": { + "anchor": "x6", + "domain": [ + 0.40800000000000003, + 0.49000000000000005 + ], + "title": { + "text": "percent" + } + }, + "yaxis7": { + "anchor": "x7", + "domain": [ + 0.306, + 0.388 + ], + "title": { + "text": "percent" + } + }, + "yaxis8": { + "anchor": "x8", + "domain": [ + 0.20400000000000001, + 0.28600000000000003 + ], + "title": { + "text": "percent-points" + } + }, + "yaxis9": { + "anchor": "x9", + "domain": [ + 0.10200000000000001, + 0.184 + ], + "title": { + "text": "index-level" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covariate_ids = covariate_svc.series_ids\n", + "covariate_plot_start = \"2016-01-01\"\n", + "\n", + "# Fixed categorical order (validated for CVD-safety), cycled since there are\n", + "# more covariates than hues in the base palette.\n", + "covariate_palette = [\n", + " \"#3987e5\", # blue\n", + " \"#008300\", # green\n", + " \"#d55181\", # magenta\n", + " \"#c98500\", # yellow\n", + " \"#199e70\", # aqua\n", + " \"#d95926\", # orange\n", + " \"#9085e9\", # violet\n", + " \"#e66767\", # red\n", + "]\n", + "\n", + "# Each covariate lives on its own scale (percent, log-return, index level, ...),\n", + "# so — as with the BAA10Y horizons above — every series gets its own row and\n", + "# y-axis rather than being forced onto one shared scale.\n", + "fig = make_subplots(\n", + " rows=len(covariate_ids),\n", + " cols=1,\n", + " shared_xaxes=True,\n", + " vertical_spacing=0.02,\n", + " subplot_titles=[covariate_svc.get_metadata(sid).description for sid in covariate_ids],\n", + ")\n", + "\n", + "covariate_ends = []\n", + "for row, series_id in enumerate(covariate_ids, start=1):\n", + " meta = covariate_svc.get_metadata(series_id)\n", + " s = covariate_svc.get_series(series_id, as_of=as_of).sort_values(\"timestamp\")\n", + " # Some covariates (fed funds, 10Y yield, ...) have decades of history that\n", + " # predate the others, so clip everyone to the same start for a fair view.\n", + " s = s[s[\"timestamp\"] >= covariate_plot_start]\n", + " if len(s) > 0:\n", + " covariate_ends.append(s[\"timestamp\"].max())\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=s[\"timestamp\"],\n", + " y=s[\"value\"],\n", + " mode=\"lines\",\n", + " name=series_id,\n", + " line=dict(color=covariate_palette[(row - 1) % len(covariate_palette)], width=1.2),\n", + " showlegend=False,\n", + " ),\n", + " row=row,\n", + " col=1,\n", + " )\n", + " fig.update_yaxes(title_text=meta.units, row=row, col=1)\n", + "\n", + "for start, end in recession_periods:\n", + " fig.add_vrect(\n", + " x0=start,\n", + " x1=end,\n", + " fillcolor=\"#ffffff\",\n", + " opacity=0.14,\n", + " line_width=0,\n", + " row=\"all\",\n", + " col=1,\n", + " )\n", + "\n", + "# Shared x-axes are linked via `matches` to this bottom axis, so the range\n", + "# only needs to (and, in plotly, only *can*) be set here — setting it with\n", + "# row=\"all\" is a silent no-op on matched axes.\n", + "covariate_plot_end = max(covariate_ends)\n", + "fig.update_xaxes(title_text=\"Timestamp\", row=len(covariate_ids), col=1)\n", + "fig.update_xaxes(range=[covariate_plot_start, covariate_plot_end], row=len(covariate_ids), col=1)\n", + "fig.update_annotations(font_size=11)\n", + "fig.update_layout(\n", + " title=\"Covariates by series (independent y-scales, shaded: NBER recessions)\",\n", + " template=\"plotly_dark\",\n", + " height=220 * len(covariate_ids),\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a44abf3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 5722f38249c147617b9f5a6d13be8bc52e48808c Mon Sep 17 00:00:00 2001 From: default Date: Sun, 19 Jul 2026 18:03:19 +0000 Subject: [PATCH 11/24] converted from SP500 to BAA10Y through Step 5. --- .../01_BAA10Y_multivariate_backtest.ipynb | 1376 +++++++++-------- 1 file changed, 714 insertions(+), 662 deletions(-) diff --git a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb index 35744e6f..127d876e 100644 --- a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb +++ b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb @@ -32,7 +32,7 @@ "| LLM-Process | `SampledTrajectoryLLMPredictor` | ✅ optional covariate prompt blocks |\n", "\n", "**How it's organised.** Specs (`specs/*.yaml`) carry only the **experiment\n", - "design** — the window and one single-horizon task per `sp500_logret_{N}b` target.\n", + "design** — the window and one single-horizon task per `baa10y_change_{N}b` target.\n", "The **predictors and their hyperparameters live in code** (the predictors cell in\n", "Section 4), so you tune models by editing Python, not YAML.\n", "\n", @@ -50,16 +50,12 @@ "\n", "- **Target = returns, not the index level.** We forecast **close-to-close\n", " cumulative log returns** over a few horizons, one series per window:\n", - " `sp500_logret_1b` (next session), `sp500_logret_5b` (forward 1 week),\n", - " `sp500_logret_21b` (forward 1 month). Forecasting `sp500_logret_Nb` exactly `N`\n", - " business days ahead resolves to the **forward** cumulative return over the next\n", + " `baa10y_change_1b` (next session), `baa10y_change_5b` (forward 1 week),\n", + " `baa10y_change_21b` (forward 1 month). Forecasting `baa10y_change_Nb` exactly `N`\n", + " business days ahead resolves to the **forward** cumulative change over the next\n", " `N` sessions.\n", "- **Per horizon:** `h=1` → direction / next-day **risk management**; `h=5`/`h=21`\n", - " → tactical rebalancing and option tenors as cumulative returns.\n", - "- **The caveat:** for an index the return *level* is close to a martingale, so\n", - " far-ahead point forecasts trend toward ~0; the forecastable, actionable objects\n", - " are **volatility, tail risk, and direction**. That's why a VIX-led panel helps\n", - " most at short horizons — watch the edge shrink as the horizon grows." + " → tactical rebalancing and option tenors as cumulative change.\n" ] }, { @@ -84,9 +80,9 @@ "\n", "| Window | Spec | Role | LLMP? |\n", "|---|---|---|---|\n", - "| **2025** | `sp500_smoke.yaml` / `sp500_backtest_2025.yaml` | open iteration & comparison | ✅ post-cutoff |\n", - "| **2026** | `sp500_eval_2026.yaml` | **protected** held-out test (budgeted) | ✅ post-cutoff |\n", - "| **2020 COVID** | `sp500_stress_2020.yaml` | volatile-regime stress, **numerical only** | ❌ leaked for LLMs |\n", + "| **2025** | `baa10y_smoke.yaml` / `baa10y_backtest_2025.yaml` | open iteration & comparison | ✅ post-cutoff |\n", + "| **2026** | `baa10y_eval_2026.yaml` | **protected** held-out test (budgeted) | ✅ post-cutoff |\n", + "| **2020 COVID** | `baa10y_stress_2020.yaml` | volatile-regime stress, **numerical only** | ❌ leaked for LLMs |\n", "\n", "This discipline is enforced **in code**: the predictors cell (Section 4) gates the\n", "LLM-Process rows on a `POST_CUTOFF` flag, so selecting `EXPERIMENT_CONFIG =\n", @@ -105,7 +101,7 @@ "The heavy lifting lives in helper modules alongside this notebook:\n", "\n", "- `data.py` builds the `DataService` (return targets + the covariate panel).\n", - "- `predictors/` the S&P 500 LLM-Process recipe (prompt framing + sampling budget).\n", + "- `predictors/` the S&P 500 (TODO) LLM-Process recipe (prompt framing + sampling budget).\n", "- `leaderboard.py` turns cached results into the `RESULTS_DF` leaderboard frame.\n", "- `analysis.py` / `plots.py` direction metrics, styled tables, and figures.\n", "\n", @@ -120,19 +116,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "be2f6012", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "targets: ['sp500_logret_1b', 'sp500_logret_21b', 'sp500_logret_5b']\n", - "covariates registered: 10 / 11 requested\n" - ] - } - ], + "outputs": [], "source": [ "from __future__ import annotations\n", "\n", @@ -161,6 +148,7 @@ "ROOT = _repo_root()\n", "load_dotenv(ROOT / \".env\", override=False) # LLMP rows call the Vector proxy — need PROXY_* set\n", "\n", + "\n", "from aieng.forecasting.evaluation import (\n", " MultiTargetBacktestSpec,\n", " MultiTargetEvalSpec,\n", @@ -175,26 +163,88 @@ " DartsLightGBMPredictor,\n", " DartsLinearRegressionPredictor,\n", " LastValuePredictor,\n", - ")\n", - "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", - "from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ec28170", + "metadata": {}, + "outputs": [], + "source": [ + "# SP500 imports\n", + "\n", + "#from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", + "#from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame\n", "from sp500_forecasting.plots import (\n", - " display_multivariate_backtest_leaderboard,\n", + "# display_multivariate_backtest_leaderboard,\n", " plot_return_forecast_vs_actual_multi,\n", - " plot_sp500_log_return_recent,\n", + "# plot_sp500_log_return_recent,\n", ")\n", - "from sp500_forecasting.predictors import build_sp500_llmp_sampled_trajectory\n", + "from sp500_forecasting.predictors import build_sp500_llmp_sampled_trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e189b4fe", + "metadata": {}, + "outputs": [], + "source": [ + "# BAA10Y imports\n", + "\n", + "from BAA10Y_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_baa10y_multivariate_service\n", "\n", + "from BAA10Y_forecasting.leaderboard import build_leaderboard, build_spread_change_compare_frame\n", + "\n", + "from BAA10Y_forecasting.plots import (\n", + " display_multivariate_backtest_leaderboard,\n", + " plot_spread_change_forecast_vs_actual_multi,\n", + " plot_baa10y_spread_change_recent,\n", + ")\n", + "from BAA10Y_forecasting.predictors import build_baa10y_llmp_sampled_trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cecd0b36", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", "\n", - "SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", - "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n", + "#SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", + "SPECS_DIR = ROOT / \"implementations\" / \"BAA10Y_forecasting\" / \"specs\"\n", "\n", + "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c93b6090", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "targets: ['baa10y_change_1b', 'baa10y_change_21b', 'baa10y_change_5b']\n", + "covariates registered: 10 / 11 requested\n" + ] + } + ], + "source": [ "# Data environment (not part of any single experiment): how far back to load\n", "# price/covariate history, and whether to re-fetch caches from source.\n", "DATA_HISTORY_START = \"2016-01-01\"\n", "REFRESH_CACHE = False\n", "\n", - "svc = build_sp500_multivariate_service(\n", + "#\n", + "\n", + "svc = build_baa10y_multivariate_service(\n", " windows=(1, 5, 21),\n", " include_covariates=True,\n", " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", @@ -206,7 +256,7 @@ "# optional FRED feed may be skipped, so filter to what actually registered).\n", "registered = set(svc.series_ids)\n", "COVARIATES = [c for c in DEFAULT_COVARIATE_SERIES_IDS if c in registered]\n", - "print(\"targets:\", sorted(s for s in registered if s.startswith(\"sp500_logret_\")))\n", + "print(\"targets:\", sorted(s for s in registered if s.startswith(\"baa10y_change\")))\n", "print(f\"covariates registered: {len(COVARIATES)} / {len(DEFAULT_COVARIATE_SERIES_IDS)} requested\")" ] }, @@ -224,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "id": "d8d4a643", "metadata": {}, "outputs": [ @@ -232,49 +282,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "Config: 'smoke' → sp500_smoke.yaml (LLMP on)\n", - "MultiTargetBacktestSpec (spec_id=sp500_smoke)\n", - " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days.\n", + "Config: 'smoke' → baa10y_smoke.yaml (LLMP on)\n", + "MultiTargetBacktestSpec (spec_id=baa10y_smoke)\n", + " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative changes at 1/5/21 business days.\n", " start: 2025-10-06 00:00:00\n", " end: 2025-11-14 00:00:00\n", " stride: 5\n", " warmup: 250\n", " tasks: 3\n", "\n", - "Task: sp500_logret_1b\n", - " description: S&P 500 close-to-close cumulative log return, 1 business day ahead (next-session return / direction).\n", + "Task: baa10y_change_1b\n", + " description: BAA10Y close-to-close cumulative changes, 1 business day ahead (next-session change / direction).\n", " horizons: 1\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_1b\n", - " description: S&P 500 close-to-close cumulative log return over 1 business day(s) (next-session) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_1b\n", + " description: BAA10Y cumulative spread change over 1 business day(s) (next business day)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n", - "Task: sp500_logret_5b\n", - " description: S&P 500 close-to-close cumulative log return, 5 business days ahead (forward 1-week return).\n", + "Task: baa10y_change_5b\n", + " description: BAA10Y close-to-close cumulative changes, 5 business days ahead (forward 1-week change).\n", " horizons: 5\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_5b\n", - " description: S&P 500 close-to-close cumulative log return over 5 business day(s) (forward 1-week (5 business days)) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_5b\n", + " description: BAA10Y cumulative spread change over 5 business day(s) (5 business days ahead)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n", - "Task: sp500_logret_21b\n", - " description: S&P 500 close-to-close cumulative log return, 21 business days ahead (forward 1-month return).\n", + "Task: baa10y_change_21b\n", + " description: BAA10Y close-to-close cumulative changes, 21 business days ahead (forward 1-month change).\n", " horizons: 21\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_21b\n", - " description: S&P 500 close-to-close cumulative log return over 21 business day(s) (forward 1-month (21 business days)) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_21b\n", + " description: BAA10Y cumulative spread change over 21 business day(s) (21 business days ahead)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n" ] @@ -296,11 +346,11 @@ "EXPERIMENT_CONFIG = \"smoke\"\n", "\n", "_BACKTEST_SPEC_FILES = {\n", - " \"smoke\": \"sp500_smoke.yaml\",\n", - " \"backtest_2025\": \"sp500_backtest_2025.yaml\",\n", - " \"stress_2020\": \"sp500_stress_2020.yaml\",\n", + " \"smoke\": \"baa10y_smoke.yaml\",\n", + " \"backtest_2025\": \"baa10y_backtest_2025.yaml\",\n", + " \"stress_2020\": \"baa10y_stress_2020.yaml\",\n", "}\n", - "_EVAL_SPEC_FILE = \"sp500_eval_2026.yaml\"\n", + "_EVAL_SPEC_FILE = \"baa10y_eval_2026.yaml\"\n", "\n", "# 2020 is pre-cutoff → keep the LLM-Process out of the comparison (see Section 0).\n", "POST_CUTOFF = EXPERIMENT_CONFIG in {\"smoke\", \"backtest_2025\"}\n", @@ -329,13 +379,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "id": "c2e433fb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -345,7 +395,7 @@ } ], "source": [ - "fig, _ = plot_sp500_log_return_recent(svc, n_trading_days=504)\n", + "fig, _ = plot_baa10y_spread_change_recent(svc, n_trading_days=504)\n", "plt.show()" ] }, @@ -377,7 +427,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "2e1da155", "metadata": {}, "outputs": [ @@ -394,8 +444,8 @@ " darts_linreg_cov\n", " darts_lightgbm\n", " darts_lightgbm_cov\n", - " llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]\n", - " llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]\n" + " llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]\n", + " llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]\n" ] } ], @@ -453,8 +503,10 @@ "# framing, defaults) lives in predictors/llmp_sampled_trajectory.py; tune it\n", "# there, or override model= / n_samples= / history_window= per call here.\n", "if POST_CUTOFF:\n", - " llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", - " llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", + "# llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", + "# llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", + " llmp = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", + " llmp_cov = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", " all_predictors += [llmp, llmp_cov]\n", " PREDICTOR_COVARIATES[llmp_cov.predictor_id] = COVARIATES\n", " PREDICTOR_LABELS[llmp.predictor_id] = \"LLMP (target)\"\n", @@ -481,7 +533,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -490,45 +542,45 @@ "output_type": "stream", "text": [ "Running last_value_naive ...\n", - " sp500_logret_1b mean CRPS = 0.01196 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01721 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02964 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.02000 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.06500 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.08500 (6 preds)\n", "Running darts_ets ...\n", - " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01345 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02101 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01025 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04531 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07737 (6 preds)\n", "Running darts_kalman ...\n", - " sp500_logret_1b mean CRPS = 0.00385 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00931 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01000 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01042 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03409 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06008 (6 preds)\n", "Running darts_autoarima ...\n", - " sp500_logret_1b mean CRPS = 0.00415 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00962 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01406 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01071 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03295 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06845 (6 preds)\n", "Running darts_linreg ...\n", - " sp500_logret_1b mean CRPS = 0.00352 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00994 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01427 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00985 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03535 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07058 (6 preds)\n", "Running darts_linreg_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00345 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00943 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01413 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00911 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03176 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06482 (6 preds)\n", "Running darts_lightgbm ...\n", - " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01003 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01475 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00941 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03526 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05892 (6 preds)\n", "Running darts_lightgbm_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00356 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00919 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01121 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00360 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01258 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01778 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00383 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01182 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01969 (6 preds)\n" + " baa10y_change_1b mean CRPS = 0.00990 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04347 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07518 (6 preds)\n", + "Running llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " baa10y_change_1b mean CRPS = 0.01167 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04401 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.04812 (6 preds)\n", + "Running llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " baa10y_change_1b mean CRPS = 0.01335 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04498 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.04327 (6 preds)\n" ] } ], @@ -568,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 15, "id": "4f601c83", "metadata": {}, "outputs": [ @@ -577,635 +629,635 @@ "text/html": [ "\n", - "\n", + "
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01sp500_logret_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.003456600.5001.0000.6670.5000.222601baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.009116600.0000.0000.0000.5000.3896
11sp500_logret_1bLinRegFalse0—darts_linreg0.003526600.5001.0000.6670.5000.778611baa10y_change_1bLightGBMFalse0—darts_lightgbm0.009416600.5000.3330.4000.5000.2226
21sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.003566600.3330.3330.3330.3330.333621baa10y_change_1bLinRegFalse0—darts_linreg0.009856600.0000.0000.0000.5000.0006
31sp500_logret_1bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.003606600.0000.0000.0000.5000.500631baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009906600.0000.0000.0000.1670.0006
41sp500_logret_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.003836600.0000.0000.0000.5000.222641baa10y_change_1bETSFalse0—darts_ets0.010256600.3330.3330.3330.3330.4446
51sp500_logret_1bKalmanFalse0—darts_kalman0.003856600.6670.6670.6670.6670.556651baa10y_change_1bKalmanFalse0—darts_kalman0.010426600.5000.6670.5710.5000.5566
61sp500_logret_1bLightGBMFalse0—darts_lightgbm0.003916600.5000.6670.5710.5000.556661baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.010716600.5000.3330.4000.5000.2226
71sp500_logret_1bETSFalse0—darts_ets0.003916600.6670.6670.6670.6670.556671baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.011676600.5001.0000.6670.5000.5006
81sp500_logret_1bAutoARIMAFalse0—darts_autoarima0.004156600.6670.6670.6670.6670.444681baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013356600.5001.0000.6670.5000.5006
91sp500_logret_1bNaiveFalse0—last_value_naive0.011966600.5001.0000.6670.5000.500691baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.5566
105sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009196600.3331.0000.5000.3330.8756105baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.031766601.0000.2500.4000.5000.8756
115sp500_logret_5bKalmanFalse0—darts_kalman0.009316600.0000.0000.0000.3330.0006115baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.032956600.8001.0000.8890.8330.6256
125sp500_logret_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.009436600.3331.0000.5000.3330.3756125baa10y_change_5bKalmanFalse0—darts_kalman0.034096600.7500.7500.7500.6670.6256
135sp500_logret_5bAutoARIMAFalse0—darts_autoarima0.009626600.5001.0000.6670.6670.5006135baa10y_change_5bLightGBMFalse0—darts_lightgbm0.035266600.3330.2500.2860.1670.2506
145sp500_logret_5bLinRegFalse0—darts_linreg0.009946600.3331.0000.5000.3330.7506145baa10y_change_5bLinRegFalse0—darts_linreg0.035356601.0000.2500.4000.5000.2506
155sp500_logret_5bLightGBMFalse0—darts_lightgbm0.010036600.2000.5000.2860.1670.3756155baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.043476600.0000.0000.0000.3330.0006
165sp500_logret_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.011826600.5001.0000.6670.6670.5006165baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044016600.6000.7500.6670.5000.6256
175sp500_logret_5bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.012586600.2500.5000.3330.3330.2506175baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.044986600.6000.7500.6670.5000.5626
185sp500_logret_5bETSFalse0—darts_ets0.013456600.3330.5000.4000.5000.2506185baa10y_change_5bETSFalse0—darts_ets0.045316600.7500.7500.7500.6670.6256
195sp500_logret_5bNaiveFalse0—last_value_naive0.017216600.3330.5000.4000.5000.5006195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256
2021sp500_logret_21bKalmanFalse0—darts_kalman0.010006600.5001.0000.6670.5000.88962021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.043276600.8000.8000.8000.6670.4006
2121sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.011216600.5000.6670.5710.5000.44462121baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.048126600.8331.0000.9090.8330.6006
2221sp500_logret_21bAutoARIMAFalse0—darts_autoarima0.014066600.7501.0000.8570.8330.88962221baa10y_change_21bLightGBMFalse0—darts_lightgbm0.058926600.6670.4000.5000.3330.2006
2321sp500_logret_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.014136600.5001.0000.6670.5000.66762321baa10y_change_21bKalmanFalse0—darts_kalman0.060086600.7500.6000.6670.5000.4006
2421sp500_logret_21bLinRegFalse0—darts_linreg0.014276600.5001.0000.6670.5000.55662421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.064826600.0000.0000.0000.1670.8006
2521sp500_logret_21bLightGBMFalse0—darts_lightgbm0.014756600.5001.0000.6670.5000.44462521baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.068456601.0000.8000.8890.8330.8006
2621sp500_logret_21bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.017786600.5001.0000.6670.5000.33362621baa10y_change_21bLinRegFalse0—darts_linreg0.070586600.0000.0000.0000.1670.2006
2721sp500_logret_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.019696600.5001.0000.6670.5000.16762721baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075186601.0000.2000.3330.3330.8006
2821sp500_logret_21bETSFalse0—darts_ets0.021016600.5001.0000.6670.5000.77862821baa10y_change_21bETSFalse0—darts_ets0.077376600.6670.4000.5000.3330.2006
2921sp500_logret_21bNaiveFalse0—last_value_naive0.029646600.5001.0000.6670.5000.50062921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.2006
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" ] From cbe88ec1b1d2b2631e03b1152ecf44682025b892 Mon Sep 17 00:00:00 2001 From: default Date: Mon, 20 Jul 2026 03:37:49 +0000 Subject: [PATCH 12/24] Update BAA10Y - Update notebooks - Update analysis metrics - Skip holiday eval origin --- .../00_baa10y_data_exploration.ipynb | 29 +- .../01_BAA10Y_multivariate_backtest.ipynb | 1671 +++++++++-------- .../BAA10Y_forecasting/99_starter_agent.ipynb | 145 +- .../BAA10Y_forecasting/analysis.py | 27 +- .../specs/baa10y_eval_2026.yaml | 12 +- 5 files changed, 1014 insertions(+), 870 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb index 9bf68a77..87618d34 100644 --- a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb +++ b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "4504704b", "metadata": { "execution": { @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 2, "id": "884638ec", "metadata": { "execution": { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 3, "id": "3d4d98ad", "metadata": {}, "outputs": [ @@ -124,7 +124,7 @@ " baa10y_change_1b\n", " BAA10Y cumulative spread change over 1 busines...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6911\n", " 2000-01-04\n", @@ -135,7 +135,7 @@ " baa10y_change_21b\n", " BAA10Y cumulative spread change over 21 busine...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6891\n", " 2000-02-01\n", @@ -146,7 +146,7 @@ " baa10y_change_5b\n", " BAA10Y cumulative spread change over 5 busines...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6907\n", " 2000-01-10\n", @@ -162,18 +162,13 @@ "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", "\n", - " source units frequency n_obs start \\\n", - "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", - "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", - "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", - "\n", - " end \n", - "0 2026-06-30 \n", - "1 2026-06-30 \n", - "2 2026-06-30 " + " source units frequency n_obs start end \n", + "0 FRED (BAA10Y), derived basis-points B 6911 2000-01-04 2026-06-30 \n", + "1 FRED (BAA10Y), derived basis-points B 6891 2000-02-01 2026-06-30 \n", + "2 FRED (BAA10Y), derived basis-points B 6907 2000-01-10 2026-06-30 " ] }, - "execution_count": 18, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d1bd6aed", "metadata": { "execution": { diff --git a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb index 35744e6f..93bf764b 100644 --- a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb +++ b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb @@ -32,7 +32,7 @@ "| LLM-Process | `SampledTrajectoryLLMPredictor` | ✅ optional covariate prompt blocks |\n", "\n", "**How it's organised.** Specs (`specs/*.yaml`) carry only the **experiment\n", - "design** — the window and one single-horizon task per `sp500_logret_{N}b` target.\n", + "design** — the window and one single-horizon task per `baa10y_change_{N}b` target.\n", "The **predictors and their hyperparameters live in code** (the predictors cell in\n", "Section 4), so you tune models by editing Python, not YAML.\n", "\n", @@ -50,16 +50,12 @@ "\n", "- **Target = returns, not the index level.** We forecast **close-to-close\n", " cumulative log returns** over a few horizons, one series per window:\n", - " `sp500_logret_1b` (next session), `sp500_logret_5b` (forward 1 week),\n", - " `sp500_logret_21b` (forward 1 month). Forecasting `sp500_logret_Nb` exactly `N`\n", - " business days ahead resolves to the **forward** cumulative return over the next\n", + " `baa10y_change_1b` (next session), `baa10y_change_5b` (forward 1 week),\n", + " `baa10y_change_21b` (forward 1 month). Forecasting `baa10y_change_Nb` exactly `N`\n", + " business days ahead resolves to the **forward** cumulative change over the next\n", " `N` sessions.\n", "- **Per horizon:** `h=1` → direction / next-day **risk management**; `h=5`/`h=21`\n", - " → tactical rebalancing and option tenors as cumulative returns.\n", - "- **The caveat:** for an index the return *level* is close to a martingale, so\n", - " far-ahead point forecasts trend toward ~0; the forecastable, actionable objects\n", - " are **volatility, tail risk, and direction**. That's why a VIX-led panel helps\n", - " most at short horizons — watch the edge shrink as the horizon grows." + " → tactical rebalancing and option tenors as cumulative change.\n" ] }, { @@ -84,9 +80,9 @@ "\n", "| Window | Spec | Role | LLMP? |\n", "|---|---|---|---|\n", - "| **2025** | `sp500_smoke.yaml` / `sp500_backtest_2025.yaml` | open iteration & comparison | ✅ post-cutoff |\n", - "| **2026** | `sp500_eval_2026.yaml` | **protected** held-out test (budgeted) | ✅ post-cutoff |\n", - "| **2020 COVID** | `sp500_stress_2020.yaml` | volatile-regime stress, **numerical only** | ❌ leaked for LLMs |\n", + "| **2025** | `baa10y_smoke.yaml` / `baa10y_backtest_2025.yaml` | open iteration & comparison | ✅ post-cutoff |\n", + "| **2026** | `baa10y_eval_2026.yaml` | **protected** held-out test (budgeted) | ✅ post-cutoff |\n", + "| **2020 COVID** | `baa10y_stress_2020.yaml` | volatile-regime stress, **numerical only** | ❌ leaked for LLMs |\n", "\n", "This discipline is enforced **in code**: the predictors cell (Section 4) gates the\n", "LLM-Process rows on a `POST_CUTOFF` flag, so selecting `EXPERIMENT_CONFIG =\n", @@ -105,7 +101,7 @@ "The heavy lifting lives in helper modules alongside this notebook:\n", "\n", "- `data.py` builds the `DataService` (return targets + the covariate panel).\n", - "- `predictors/` the S&P 500 LLM-Process recipe (prompt framing + sampling budget).\n", + "- `predictors/` the BAA10Y LLM-Process prompt and sampling recipe.\n", "- `leaderboard.py` turns cached results into the `RESULTS_DF` leaderboard frame.\n", "- `analysis.py` / `plots.py` direction metrics, styled tables, and figures.\n", "\n", @@ -114,8 +110,8 @@ "covariates; the covariate variants read them.\n", "\n", "**First run on the 2025/2026 windows?** Warm the caches to the present first:\n", - "`uv run python scripts/fetch_sp500_market.py --refresh` (Yahoo: ^GSPC/^VIX/^IXIC)\n", - "and `uv run python scripts/fetch_fred.py` (macro covariates)." + "`uv run python scripts/fetch_sp500_market.py --refresh` (Yahoo)\n", + "and `uv run python scripts/fetch_fred.py` (traget and macro covariates)." ] }, { @@ -123,16 +119,7 @@ "execution_count": 1, "id": "be2f6012", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "targets: ['sp500_logret_1b', 'sp500_logret_21b', 'sp500_logret_5b']\n", - "covariates registered: 10 / 11 requested\n" - ] - } - ], + "outputs": [], "source": [ "from __future__ import annotations\n", "\n", @@ -161,6 +148,7 @@ "ROOT = _repo_root()\n", "load_dotenv(ROOT / \".env\", override=False) # LLMP rows call the Vector proxy — need PROXY_* set\n", "\n", + "\n", "from aieng.forecasting.evaluation import (\n", " MultiTargetBacktestSpec,\n", " MultiTargetEvalSpec,\n", @@ -175,26 +163,69 @@ " DartsLightGBMPredictor,\n", " DartsLinearRegressionPredictor,\n", " LastValuePredictor,\n", - ")\n", - "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", - "from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame\n", - "from sp500_forecasting.plots import (\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e189b4fe", + "metadata": {}, + "outputs": [], + "source": [ + "# BAA10Y imports\n", + "\n", + "from BAA10Y_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_baa10y_multivariate_service\n", + "\n", + "from BAA10Y_forecasting.leaderboard import build_leaderboard, build_spread_change_compare_frame\n", + "\n", + "from BAA10Y_forecasting.plots import (\n", " display_multivariate_backtest_leaderboard,\n", - " plot_return_forecast_vs_actual_multi,\n", - " plot_sp500_log_return_recent,\n", + " plot_spread_change_forecast_vs_actual_multi,\n", + " plot_baa10y_spread_change_recent,\n", ")\n", - "from sp500_forecasting.predictors import build_sp500_llmp_sampled_trajectory\n", - "\n", + "from BAA10Y_forecasting.predictors import build_baa10y_llmp_sampled_trajectory" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cecd0b36", + "metadata": {}, + "outputs": [], + "source": [ + "#\n", "\n", - "SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", - "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n", + "#SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", + "SPECS_DIR = ROOT / \"implementations\" / \"BAA10Y_forecasting\" / \"specs\"\n", "\n", + "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c93b6090", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "targets: ['baa10y_change_1b', 'baa10y_change_21b', 'baa10y_change_5b']\n", + "covariates registered: 10 / 11 requested\n" + ] + } + ], + "source": [ "# Data environment (not part of any single experiment): how far back to load\n", "# price/covariate history, and whether to re-fetch caches from source.\n", "DATA_HISTORY_START = \"2016-01-01\"\n", "REFRESH_CACHE = False\n", "\n", - "svc = build_sp500_multivariate_service(\n", + "#\n", + "\n", + "svc = build_baa10y_multivariate_service(\n", " windows=(1, 5, 21),\n", " include_covariates=True,\n", " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", @@ -206,7 +237,7 @@ "# optional FRED feed may be skipped, so filter to what actually registered).\n", "registered = set(svc.series_ids)\n", "COVARIATES = [c for c in DEFAULT_COVARIATE_SERIES_IDS if c in registered]\n", - "print(\"targets:\", sorted(s for s in registered if s.startswith(\"sp500_logret_\")))\n", + "print(\"targets:\", sorted(s for s in registered if s.startswith(\"baa10y_change\")))\n", "print(f\"covariates registered: {len(COVARIATES)} / {len(DEFAULT_COVARIATE_SERIES_IDS)} requested\")" ] }, @@ -224,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "id": "d8d4a643", "metadata": {}, "outputs": [ @@ -232,49 +263,49 @@ "name": "stdout", "output_type": "stream", "text": [ - "Config: 'smoke' → sp500_smoke.yaml (LLMP on)\n", - "MultiTargetBacktestSpec (spec_id=sp500_smoke)\n", - " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days.\n", + "Config: 'smoke' → baa10y_smoke.yaml (LLMP on)\n", + "MultiTargetBacktestSpec (spec_id=baa10y_smoke)\n", + " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative changes at 1/5/21 business days.\n", " start: 2025-10-06 00:00:00\n", " end: 2025-11-14 00:00:00\n", " stride: 5\n", " warmup: 250\n", " tasks: 3\n", "\n", - "Task: sp500_logret_1b\n", - " description: S&P 500 close-to-close cumulative log return, 1 business day ahead (next-session return / direction).\n", + "Task: baa10y_change_1b\n", + " description: BAA10Y close-to-close cumulative changes, 1 business day ahead (next-session change / direction).\n", " horizons: 1\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_1b\n", - " description: S&P 500 close-to-close cumulative log return over 1 business day(s) (next-session) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_1b\n", + " description: BAA10Y cumulative spread change over 1 business day(s) (next business day)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n", - "Task: sp500_logret_5b\n", - " description: S&P 500 close-to-close cumulative log return, 5 business days ahead (forward 1-week return).\n", + "Task: baa10y_change_5b\n", + " description: BAA10Y close-to-close cumulative changes, 5 business days ahead (forward 1-week change).\n", " horizons: 5\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_5b\n", - " description: S&P 500 close-to-close cumulative log return over 5 business day(s) (forward 1-week (5 business days)) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_5b\n", + " description: BAA10Y cumulative spread change over 5 business day(s) (5 business days ahead)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n", - "Task: sp500_logret_21b\n", - " description: S&P 500 close-to-close cumulative log return, 21 business days ahead (forward 1-month return).\n", + "Task: baa10y_change_21b\n", + " description: BAA10Y close-to-close cumulative changes, 21 business days ahead (forward 1-month change).\n", " horizons: 21\n", " frequency: B\n", " payload: continuous\n", " resolution: observed_value_at_resolution_timestamp\n", - "- target_series_id: sp500_logret_21b\n", - " description: S&P 500 close-to-close cumulative log return over 21 business day(s) (forward 1-month (21 business days)) (Yahoo Finance ^GSPC, derived)\n", - " source: Yahoo Finance (^GSPC), derived\n", - " units: log-return\n", + "- target_series_id: baa10y_change_21b\n", + " description: BAA10Y cumulative spread change over 21 business day(s) (21 business days ahead)\n", + " source: FRED (BAA10Y), derived\n", + " units: basis-points\n", " frequency: B\n", "\n" ] @@ -296,11 +327,11 @@ "EXPERIMENT_CONFIG = \"smoke\"\n", "\n", "_BACKTEST_SPEC_FILES = {\n", - " \"smoke\": \"sp500_smoke.yaml\",\n", - " \"backtest_2025\": \"sp500_backtest_2025.yaml\",\n", - " \"stress_2020\": \"sp500_stress_2020.yaml\",\n", + " \"smoke\": \"baa10y_smoke.yaml\",\n", + " \"backtest_2025\": \"baa10y_backtest_2025.yaml\",\n", + " \"stress_2020\": \"baa10y_stress_2020.yaml\",\n", "}\n", - "_EVAL_SPEC_FILE = \"sp500_eval_2026.yaml\"\n", + "_EVAL_SPEC_FILE = \"baa10y_eval_2026.yaml\"\n", "\n", "# 2020 is pre-cutoff → keep the LLM-Process out of the comparison (see Section 0).\n", "POST_CUTOFF = EXPERIMENT_CONFIG in {\"smoke\", \"backtest_2025\"}\n", @@ -329,13 +360,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c2e433fb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -345,7 +376,7 @@ } ], "source": [ - "fig, _ = plot_sp500_log_return_recent(svc, n_trading_days=504)\n", + "fig, _ = plot_baa10y_spread_change_recent(svc, n_trading_days=504) # Approximately two years\n", "plt.show()" ] }, @@ -360,7 +391,7 @@ "This is where you choose the roster. Each predictor implements the same\n", "`Predictor` API against the loaded spec; the conventional methods come straight\n", "from `aieng.forecasting.methods`, and the LLM-Process variants come from the\n", - "S&P 500 recipe in `predictors/`. The covariate variants read the registered\n", + "BAA10Y recipe in `predictors/`. The covariate variants read the registered\n", "`COVARIATES` panel; the target-only variants pass `covariate_series_ids=None`.\n", "\n", "| Group | Predictor | Covariates? |\n", @@ -375,9 +406,15 @@ "answers whether an LLM can use the same exogenous panel the ML methods do." ] }, + { + "cell_type": "markdown", + "id": "6023e09c", + "metadata": {}, + "source": [] + }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "id": "2e1da155", "metadata": {}, "outputs": [ @@ -394,8 +431,8 @@ " darts_linreg_cov\n", " darts_lightgbm\n", " darts_lightgbm_cov\n", - " llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]\n", - " llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]\n" + " llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]\n", + " llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]\n" ] } ], @@ -453,8 +490,10 @@ "# framing, defaults) lives in predictors/llmp_sampled_trajectory.py; tune it\n", "# there, or override model= / n_samples= / history_window= per call here.\n", "if POST_CUTOFF:\n", - " llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", - " llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", + "# llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", + "# llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", + " llmp = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", + " llmp_cov = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", " all_predictors += [llmp, llmp_cov]\n", " PREDICTOR_COVARIATES[llmp_cov.predictor_id] = COVARIATES\n", " PREDICTOR_LABELS[llmp.predictor_id] = \"LLMP (target)\"\n", @@ -481,7 +520,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 8, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -490,45 +529,45 @@ "output_type": "stream", "text": [ "Running last_value_naive ...\n", - " sp500_logret_1b mean CRPS = 0.01196 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01721 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02964 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.02000 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.06500 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.08500 (6 preds)\n", "Running darts_ets ...\n", - " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01345 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02101 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01019 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04888 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07463 (6 preds)\n", "Running darts_kalman ...\n", - " sp500_logret_1b mean CRPS = 0.00385 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00931 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01000 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01048 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03412 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05678 (6 preds)\n", "Running darts_autoarima ...\n", - " sp500_logret_1b mean CRPS = 0.00415 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00962 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01406 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01000 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03439 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06671 (6 preds)\n", "Running darts_linreg ...\n", - " sp500_logret_1b mean CRPS = 0.00352 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00994 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01427 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00981 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03267 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06396 (6 preds)\n", "Running darts_linreg_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00345 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00943 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01413 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00891 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03311 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06245 (6 preds)\n", "Running darts_lightgbm ...\n", - " sp500_logret_1b mean CRPS = 0.00391 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01003 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01475 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00892 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03605 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05931 (6 preds)\n", "Running darts_lightgbm_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00356 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00919 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01121 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00360 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01258 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01778 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00383 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01182 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01969 (6 preds)\n" + " baa10y_change_1b mean CRPS = 0.00980 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04113 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07562 (6 preds)\n", + "Running llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " baa10y_change_1b mean CRPS = 0.01140 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04425 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.04177 (6 preds)\n", + "Running llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " baa10y_change_1b mean CRPS = 0.01389 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04462 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.04493 (6 preds)\n" ] } ], @@ -568,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "id": "4f601c83", "metadata": {}, "outputs": [ @@ -577,635 +616,635 @@ "text/html": [ "\n", - "\n", + "
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01sp500_logret_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.003456600.5001.0000.6670.5000.222601baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008916600.0000.0000.0000.5000.6116
11sp500_logret_1bLinRegFalse0—darts_linreg0.003526600.5001.0000.6670.5000.778611baa10y_change_1bLightGBMFalse0—darts_lightgbm0.008926600.4000.6670.5000.3330.1116
21sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.003566600.3330.3330.3330.3330.333621baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009806600.0000.0000.0000.1670.1116
31sp500_logret_1bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.003606600.0000.0000.0000.5000.500631baa10y_change_1bLinRegFalse0—darts_linreg0.009816600.0000.0000.0000.5000.0006
41sp500_logret_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.003836600.0000.0000.0000.5000.222641baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.010006600.4000.6670.5000.3330.5566
51sp500_logret_1bKalmanFalse0—darts_kalman0.003856600.6670.6670.6670.6670.556651baa10y_change_1bETSFalse0—darts_ets0.010196600.6670.6670.6670.6670.5566
61sp500_logret_1bLightGBMFalse0—darts_lightgbm0.003916600.5000.6670.5710.5000.556661baa10y_change_1bKalmanFalse0—darts_kalman0.010486600.3330.3330.3330.3330.4446
71sp500_logret_1bETSFalse0—darts_ets0.003916600.6670.6670.6670.6670.556671baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.011406600.5001.0000.6670.5000.5006
81sp500_logret_1bAutoARIMAFalse0—darts_autoarima0.004156600.6670.6670.6670.6670.444681baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013896600.5001.0000.6670.5000.1676
91sp500_logret_1bNaiveFalse0—last_value_naive0.011966600.5001.0000.6670.5000.500691baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.5566
105sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009196600.3331.0000.5000.3330.8756105baa10y_change_5bLinRegFalse0—darts_linreg0.032676600.5000.2500.3330.3330.3756
115sp500_logret_5bKalmanFalse0—darts_kalman0.009316600.0000.0000.0000.3330.0006115baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.033116600.0000.0000.0000.3330.5006
125sp500_logret_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.009436600.3331.0000.5000.3330.3756125baa10y_change_5bKalmanFalse0—darts_kalman0.034126600.7500.7500.7500.6670.5006
135sp500_logret_5bAutoARIMAFalse0—darts_autoarima0.009626600.5001.0000.6670.6670.5006135baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.034396600.6670.5000.5710.5000.7506
145sp500_logret_5bLinRegFalse0—darts_linreg0.009946600.3331.0000.5000.3330.7506145baa10y_change_5bLightGBMFalse0—darts_lightgbm0.036056600.5000.5000.5000.3330.3756
155sp500_logret_5bLightGBMFalse0—darts_lightgbm0.010036600.2000.5000.2860.1670.3756155baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.041136600.0000.0000.0000.1670.0006
165sp500_logret_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.011826600.5001.0000.6670.6670.5006165baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044256600.7500.7500.7500.6670.7506
175sp500_logret_5bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.012586600.2500.5000.3330.3330.2506175baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.044626600.6000.7500.6670.5000.1256
185sp500_logret_5bETSFalse0—darts_ets0.013456600.3330.5000.4000.5000.2506185baa10y_change_5bETSFalse0—darts_ets0.048886600.7500.7500.7500.6670.6256
195sp500_logret_5bNaiveFalse0—last_value_naive0.017216600.3330.5000.4000.5000.5006195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256
2021sp500_logret_21bKalmanFalse0—darts_kalman0.010006600.5001.0000.6670.5000.88962021baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.041776600.8331.0000.9090.8330.6006
2121sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.011216600.5000.6670.5710.5000.44462121baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.044936600.8000.8000.8000.6670.8006
2221sp500_logret_21bAutoARIMAFalse0—darts_autoarima0.014066600.7501.0000.8570.8330.88962221baa10y_change_21bKalmanFalse0—darts_kalman0.056786600.7500.6000.6670.5000.4006
2321sp500_logret_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.014136600.5001.0000.6670.5000.66762321baa10y_change_21bLightGBMFalse0—darts_lightgbm0.059316600.6670.4000.5000.3330.4006
2421sp500_logret_21bLinRegFalse0—darts_linreg0.014276600.5001.0000.6670.5000.55662421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.062456601.0000.2000.3330.3330.8006
2521sp500_logret_21bLightGBMFalse0—darts_lightgbm0.014756600.5001.0000.6670.5000.44462521baa10y_change_21bLinRegFalse0—darts_linreg0.063966600.0000.0000.0000.1671.0006
2621sp500_logret_21bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.017786600.5001.0000.6670.5000.33362621baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066716601.0000.8000.8890.8331.0006
2721sp500_logret_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.019696600.5001.0000.6670.5000.16762721baa10y_change_21bETSFalse0—darts_ets0.074636601.0000.8000.8890.8330.8006
2821sp500_logret_21bETSFalse0—darts_ets0.021016600.5001.0000.6670.5000.77862821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075626600.0000.0000.0000.1670.6006
2921sp500_logret_21bNaiveFalse0—last_value_naive0.029646600.5001.0000.6670.5000.50062921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.2006
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" ] @@ -1260,13 +1299,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "id": "97076392", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -1277,7 +1316,7 @@ ], "source": [ "# A few representative models, pulled straight from the cached backtest results.\n", - "H1_TASK = \"sp500_logret_1b\"\n", + "H1_TASK = \"baa10y_change_1b\"\n", "demo_ids = [naive.predictor_id, ets.predictor_id, lightgbm_cov.predictor_id]\n", "\n", "compare_by_run: dict[str, pd.DataFrame] = {}\n", @@ -1286,9 +1325,9 @@ " if result is None:\n", " continue\n", " label = PREDICTOR_LABELS.get(pid, pid)\n", - " compare_by_run[label] = build_return_compare_frame(result.predictions, svc, H1_TASK)\n", + " compare_by_run[label] = build_spread_change_compare_frame(result.predictions, svc, H1_TASK)\n", "\n", - "fig, _ = plot_return_forecast_vs_actual_multi(compare_by_run, title=\"Next-session return: forecast vs realised (h=1)\")\n", + "fig, _ = plot_spread_change_forecast_vs_actual_multi(compare_by_run, title=\"Next-session return: forecast vs realised (h=1)\")\n", "plt.show()" ] }, @@ -1301,7 +1340,7 @@ "## 7. Protected 2026 eval — the honest scoreboard\n", "\n", "`multi_evaluate()` against the held-out 2026 window. This is **scarce**:\n", - "`sp500_eval_2026.yaml` carries `max_runs`, and one `multi_evaluate` call across\n", + "`baa10y_eval_2026.yaml` carries `max_runs`, and one `multi_evaluate` call across\n", "all three horizons counts as **one** run. Pass an `EvalTracker` to enforce that\n", "budget across sessions (commented below — it would block re-runs once exhausted,\n", "so we leave it off here and just show `run_number`).\n", @@ -1318,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 13, "id": "5570e8b0", "metadata": {}, "outputs": [ @@ -1327,19 +1366,19 @@ "output_type": "stream", "text": [ "Evaluating last_value_naive ...\n", - " sp500_logret_1b mean CRPS = 0.01043 (run #1)\n", - " sp500_logret_5b mean CRPS = 0.00984 (run #1)\n", - " sp500_logret_21b mean CRPS = 0.04483 (run #1)\n", + " baa10y_change_1b mean CRPS = 0.03000 (run #1)\n", + " baa10y_change_5b mean CRPS = 0.06500 (run #1)\n", + " baa10y_change_21b mean CRPS = 0.16500 (run #1)\n", "Evaluating darts_lightgbm_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00429 (run #1)\n", - " sp500_logret_5b mean CRPS = 0.01265 (run #1)\n", - " sp500_logret_21b mean CRPS = 0.03705 (run #1)\n" + " baa10y_change_1b mean CRPS = 0.01965 (run #1)\n", + " baa10y_change_5b mean CRPS = 0.03426 (run #1)\n", + " baa10y_change_21b mean CRPS = 0.07850 (run #1)\n" ] } ], "source": [ "# from aieng.forecasting.evaluation import EvalTracker\n", - "# tracker = EvalTracker(ROOT / \"data\" / \"sp500_eval_runs.yaml\") # enforces max_runs across sessions\n", + "# tracker = EvalTracker(ROOT / \"data\" / \"baa10y_eval_runs.yaml\") # enforces max_runs across sessions\n", "\n", "with (SPECS_DIR / _EVAL_SPEC_FILE).open() as f:\n", " eval_spec = MultiTargetEvalSpec.model_validate(yaml.safe_load(f))\n", @@ -1371,7 +1410,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "id": "e6a1826b", "metadata": {}, "outputs": [ @@ -1380,162 +1419,162 @@ "text/html": [ "\n", - "\n", + "
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01sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.004298800.2500.3330.2860.3750.4678101baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.019658800.5000.2500.3330.5000.37581
11sp500_logret_1bNaiveFalse0—last_value_naive0.010438800.2860.6670.4000.2500.3338111baa10y_change_1bNaiveFalse0—last_value_naive0.030008801.0000.5000.6670.7500.56281
25sp500_logret_5bNaiveFalse0—last_value_naive0.009847710.3330.5000.4000.5710.5507125baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.034268800.7500.5000.6000.5000.41781
35sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.012657710.3331.0000.5000.4290.6007135baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.33381
421sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037058800.2501.0000.4000.2500.41781421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.078508800.0000.0000.0000.5000.31281
521sp500_logret_21bNaiveFalse0—last_value_naive0.044838800.0000.0000.0000.5000.33381521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.25081
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fM2zYMM2dO1cVK1bU0qVL9eabb6py5cqqVKmS9uzZk6qYatSoocqVK9tvFyhQQM2bN9fSpUvtpwBp3769rl27pgULFtjHffHFF7px44bD+ktO586dHU5RUq1aNRlj1LlzZ/s0Nzc3ValSxWH9p+W5T8mLL77oEEPt2rV18+ZNHT58WJK0fPlyxcbGqm/fvsqS5f/eInft2lX+/v76/vvvJUmbN2/WqVOn1L17d4cjuzt27JjqC4je/jo9d+6c/vnnH4WHh+vgwYM6d+6cw9hSpUrZT4Ek3TrSvHjx4g7r6YcfflD16tVVtWpVh3HPPfdcquKJ1717d4fbtWvXTvA4bm5u6tOnj8O4V199VcYYLVmyxGF6eHi4SpUqlaYYZs2apcmTJ+v9999XvXr1JP3fOu/Zs6fD+eqbNm2qEiVK2J+btOSSlDNnzqTq1yypfb1kyZJFNWvWtJ8uas+ePTpz5oxef/11GWPsp4VavXq1ypQpY/8FQFpf+4UKFVKjRo1SjDsx2bNn16VLlxxONZYWLVq0UL58+ey3q1atqmrVqumHH36QJP37779asWKFWrdubd8v//PPPzpz5owaNWqkffv2JTg9UVrMnz9ftWvXVmBgoMO6ioiI0M2bNx1O1SVJTz/9tIKCghJdVvxz7wrXPgEAAA8GTmsDAAAsFxQUpIiICM2dO1eXL1/WzZs31bJly0TH7tu3T+fOnUvyPOq3X/x0165deuutt7RixQp7Ez3enU3P/PnzJzindWBgoLZv355s7PGnOrhw4UKy4+700UcfqVixYjp37pw+/fRT/frrr/Ly8kp0bMGCBTV9+nTZbDZ5e3uraNGiiebftm1btW3bVufPn9fGjRsVFRWluXPnqlmzZtq5c2eKF94sWrRogmnFihXT5cuXdfr0aeXOnVslSpTQww8/rDlz5tib6XPmzFH16tVVpEiRVOVeoEABh9vxTew7TwETEBDgcD7ttDz3aY0hvgkX/3jxTfo7Twfi6empwoUL2++P//fOdefh4aHChQunKpa1a9dqyJAhWr9+vS5fvuxw37lz5xya/HfGHR/77evp8OHDqlatWoJxaTm1ibe3d4KGZWKPkzdv3gRfTMWffiR+3cQrVKhQqh9fkrZu3aru3burbdu2DudjT+q5kaQSJUpozZo1ac4lOeaO8+cnJrWvF+nWFwNDhw7VlStXtHr1auXJk0eVKlVS+fLltXr1ajVo0EBr1qxxuDB0Wl/7aV3Xt+vZs6e+/PJLNWnSRPny5VPDhg3VunVrNW7cOFXzJ7Uf+fLLLyXdOjWTMUZvv/223n777USXcerUKYcGf1rs27dP27dvT7LhnpZ1Ff/c31kbAAAAMgrNeQAA4BTPPvusunbtqpMnT6pJkyYO54y+XVxcnIKDgzVnzpxE749vyJw9e1bh4eHy9/fX8OHDFRYWJm9vb/3+++967bXXElwc083NLdHlpdSYK1GihCRpx44dqlChQrJjb1e1alVVqVJF0q0jTR955BE9++yz2rt3r/z8/BzG+vr6KiIiItXL9vf3V4MGDdSgQQN5eHho5syZ2rhxo8LDw1O9jOS0b99eL7/8so4dO6Zr165pw4YN+vDDD1M9f1LrOrHpt6//1D739xJDahqx6enAgQN69NFHVaJECf3vf/9TSEiIPD099cMPP2j8+PHp9jpNq6Qe516k5Zz3//33n55++mkVK1ZMn3zyyT097r3kkjNnzlQ38VPrkUce0fXr17V+/XqtXr3a/kuI2rVra/Xq1frjjz90+vRph19IpPW1n5Z1fafg4GBt3bpVS5cu1ZIlS7RkyRJFRkaqffv2mjlz5l0vN178a7p///5JHt2f2i/6klp+gwYNNHDgwETvL1asmMPt5NZV/HOfK1euu44HAAAgLWjOAwAAp3jyySfVrVs3bdiwweGinHcKCwvT8uXLVatWrWSbKqtWrdKZM2e0cOFCh4sqxsTEpGvcTZo0kZubm2bPnn3XF4V1c3PTyJEjVa9ePX344Yd6/fXX0y2+KlWqaObMmTpx4kSKY/ft25dg2p9//qmsWbM6NP/atGmjV155RZ9//rmuXLkiDw8PPfPMM+kWc1JS+9xL936ka2hoqKRbF9a8/Qj42NhYxcTE2L8siR+3b98+1a9f3z7u+vXriomJUfny5ZN9nG+//VbXrl3T4sWLHY6KT8spehKLPbHncu/evXe9zKQeZ/ny5bpw4YLD0fPxp4yKXzdpFRcXp+eee05nz57V8uXLlTVr1gSPK93K5/Z1Hj/tbh83MSVKlEjVPiO1rxfp1hdznp6eWr16tVavXq0BAwZIkurUqaPp06fr559/tt+Ol5bXfnrw9PRUs2bN1KxZM8XFxalnz56aOnWq3n777RQb50ntR+IvABy/fjw8PFL80jG57Tip+8LCwnTx4sU0faGZlJiYGGXJkiVBQx8AACCjcM55AADgFH5+fpoyZYqGDh2qZs2aJTmudevWunnzpkaMGJHgvhs3bujs2bOS/u9o2duPKI6NjdXkyZPTNe6QkBB17dpVP/30kyZNmpTg/ri4OI0bN07Hjh1Ldjl169ZV1apV9cEHH+jq1atpiuHy5cv2c1XfKf6836k5pcn69ev1+++/228fPXpU33zzjRo2bOhw9HGuXLnUpEkTzZ49W3PmzFHjxo0tObI0tc+9dOvXBrffTquIiAh5enpq4sSJDq+hGTNm6Ny5c2ratKmkW19+BAUF6eOPP1ZsbKx9XFRUVKoeP7HX6blz5xQZGXnXsT/22GPasGGDNm3aZJ92+vTpJI+6vpfHuXnzZoJfTYwfP142m01NmjS5q+UOGzZMS5cu1eeff57oKUeqVKmi4OBgffzxx7p27Zp9+pIlS7Rnzx77c5MeatSooZ07dzo8TmJS+3qRbp1m5+GHH9bnn3+uI0eOOBw5f+XKFU2cOFFhYWHKkyePfZ60vPbv1ZkzZxxuZ8mSReXKlZOkFNeDJC1atMjhnPGbNm3Sxo0b7a+H4OBg1a1bV1OnTk30S8PTp0/b/+/r6ytJieaX1DbeunVrrV+/XkuXLk1w39mzZ3Xjxo0Uc4i3ZcsWlS5dOtXXjwAAALhXHDkPAACcpkOHDimOCQ8PV7du3TRy5Eht3bpVDRs2lIeHh/bt26f58+drwoQJatmypWrWrKnAwEB16NBBffr0kc1m02effZYhpy0ZN26cDhw4oD59+mjhwoV6/PHHFRgYqCNHjmj+/Pn6448/1KZNmxSXM2DAALVq1UpRUVEJLmCZnMuXL6tmzZqqXr26GjdurJCQEJ09e1aLFi3S6tWr1aJFC1WsWDHF5ZQpU0aNGjVSnz595OXlZf8iY9iwYQnGtm/f3n5dgMQahhkhtc+9JFWuXFlTpkzRO++8oyJFiig4ODjBUdbJCQoK0qBBgzRs2DA1btxYTzzxhPbu3avJkyfr4Ycftl/81sPDQ++88466deum+vXr65lnnlFMTIwiIyNTdc75hg0b2o9S7tatmy5evKjp06crODg4Vb92SMzAgQP12WefqXHjxnr55Zfl6+uradOmKTQ0NMVrKKRFs2bNVK9ePb355ps6dOiQypcvr59++knffPON+vbtq7CwsDQvc8eOHRoxYoTq1KmjU6dOafbs2Q73t2vXTh4eHho9erQ6deqk8PBwtW3bVn///bcmTJigggULql+/fumVopo3b64RI0bol19+UcOGDZMcl9rXS7zatWtr1KhRCggIUNmyZSXdaloXL15ce/fuVceOHR3Gp+W1f6+6dOmif//9V/Xr11f+/Pl1+PBhTZo0SRUqVLBfTyA5RYoU0SOPPKIePXro2rVr+uCDD5QzZ06H08x89NFHeuSRR1S2bFl17dpVhQsX1t9//63169fr2LFj2rZtmySpQoUKcnNz0+jRo3Xu3Dl5eXmpfv36Cg4OTnIbHzBggBYvXqzHH39cHTt2VOXKlXXp0iXt2LFDCxYs0KFDh1L1ZeL169f1yy+/qGfPnne/MgEAANLKAAAAWCAyMtJIMr/99luy40JDQ03Tpk0TTJ82bZqpXLmy8fHxMdmyZTNly5Y1AwcONH/99Zd9zNq1a0316tWNj4+PyZs3rxk4cKBZunSpkWRWrlxpHxceHm5Kly6d4DE6dOhgQkNDU5XPjRs3zCeffGJq165tAgICjIeHhwkNDTWdOnUy0dHRqcr75s2bJiwszISFhZkbN24kG9vtrl+/bqZPn25atGhhQkNDjZeXl8maNaupWLGiGTNmjLl27VqK8UsyvXr1MrNnzzZFixY1Xl5epmLFig7r6XbXrl0zgYGBJiAgwFy5ciXF5RuTdO5Dhgwxkszp06cdpnfo0MH4+vomWE5qnvuTJ0+apk2bmmzZshlJJjw8PNkYVq5cmeB1YYwxH374oSlRooTx8PAwDz30kOnRo4f577//EsQ0efJkU6hQIePl5WWqVKlifv31VxMeHm5/3OQsXrzYlCtXznh7e5uCBQua0aNHm08//dRIMjExMfZxSW0LiT3O9u3bTXh4uPH29jb58uUzI0aMMDNmzEiwzMQktd7jn6fbXbhwwfTr18/kzZvXeHh4mKJFi5oxY8aYuLg4h3Hxr6/ESDJDhgwxxvzf85DU3+2++OILU7FiRePl5WVy5MhhnnvuOXPs2LG7ziUp5cqVM507d3aYFv86unNdpvb18v333xtJpkmTJg7Tu3TpYiSZGTNmJBpLal77Sb1OknLnfm7BggWmYcOGJjg42Hh6epoCBQqYbt26mRMnTiS7nJiYGCPJjBkzxowbN86EhIQYLy8vU7t2bbNt27YE4w8cOGDat29vcufObTw8PEy+fPnM448/bhYsWOAwbvr06aZw4cLGzc3NYRtNahs35tbrctCgQaZIkSLG09PT5MqVy9SsWdOMHTvWxMbGJog3MUuWLDGSzL59+1KxFgEAANKHzRiLr4IFAAAAl3Pjxg3lzZtXzZo104wZM5wdDpBhPvvsM/Xq1UtHjhxJ8kLVePC0aNFCNptNX3/9tbNDAQAAmQjnnAcAAECKFi1apNOnT6t9+/bODgXIUM8995wKFCigjz76yNmhwCJ79uzRd999Z9kpuwAAAOJx5DwAAACStHHjRm3fvl0jRoxQrly5HC4gCwAAAAC4exw5DwAAgCRNmTJFPXr0UHBwsGbNmuXscAAAAADggcGR8wAAAAAAAAAAWIwj5wEAAAAAAAAAsBjNeQAAAAAAAAAALEZzHgAAAAAAAAAAi9GcBwAAAAAAAADAYjTnAQAAAAAAAACwGM15AAAAAAAAAAAsRnMeAAAAAAAAAACL0ZwH7lN169ZV3759nR1GiqKiopQ9e/YMfYyOHTuqRYsWGfoY6a1gwYL64IMP0n25M2bMUMOGDVM19uOPP1azZs3SPQYAQNq4Sk0fOnSoKlSokKGP4Srr4nY2m02LFi1K9+W+/fbbevHFF1M19vXXX9dLL72U7jEAAJLmijUrKVZ8ps6oz8AZ5dChQ7LZbNq6dWu6L/v555/Xe++9l6qxbdq00bhx49I9BrgOmvMA7Hbt2qWnn35aBQsWlM1mu28K64QJExQVFeXsMJzu6tWrevvttzVkyJBUjX/hhRf0+++/a/Xq1RkcGQDgfhMVFSWbzebw5+3t7eywtHDhQo0YMcLZYTjdyZMnNWHCBL355pupGt+/f3/NnDlTBw8ezODIAADONH36dNWuXVuBgYEKDAxURESENm3a5DBm4cKFatiwoXLmzJlhzeW78dtvv6X6S+cH2bZt2/TDDz+oT58+qRr/1ltv6d1339W5c+cyODLcr2jOA7C7fPmyChcurFGjRil37tzODscuICAgw4/OdwULFiyQv7+/atWqlarxnp6eevbZZzVx4sQMjgwAcD/y9/fXiRMn7H+HDx92dkjKkSOHsmXL5uwwnO6TTz5RzZo1FRoamqrxuXLlUqNGjTRlypQMjgwA4EyrVq1S27ZttXLlSq1fv14hISFq2LChjh8/bh9z6dIlPfLIIxo9erQTI00oKChIWbNmdXYYTjdp0iS1atVKfn5+qRpfpkwZhYWFafbs2RkcGe5XNOeB+1hcXJwGDhyoHDlyKHfu3Bo6dGiGPt7DDz+sMWPGqE2bNvLy8krTvIsWLVLRokXl7e2tRo0a6ejRo/b7EvsJXd++fVW3bl377QULFqhs2bLy8fFRzpw5FRERoUuXLiU6f926ddWnT59k183Zs2fVpUsXBQUFyd/fX/Xr19e2bdvs92/btk316tVTtmzZ5O/vr8qVK2vz5s2SpMOHD6tZs2YKDAyUr6+vSpcurR9++CHJ3E+dOqVmzZrJx8dHhQoV0pw5cxKM+d///qeyZcvK19dXISEh6tmzpy5evCjp1psrf39/LViwIME69fX11YULFyRJ8+bNS3CamlWrVqlq1ary9fVV9uzZVatWLYfmS7NmzbR48WJduXIlyfgBABnP6pou3TodS+7cue1/Dz30UKrmmzp1qkJCQpQ1a1a1bt3a4UiuxH7i36JFC3Xs2NF+e/Lkyfb3BA899JBatmyZ5PwFCxbUe++9pxdeeEHZsmVTgQIFNG3aNIflHz16VK1bt1b27NmVI0cONW/eXIcOHbLfn1wtTK7eJ2bfvn2qU6eOvL29VapUKS1btizBmNdee03FihVT1qxZVbhwYb399tu6fv26pFs/kc+SJUuCx/jggw8UGhqquLg4SYnX9OTeC0m3avq8efOSjB0AkP6srt9z5sxRz549VaFCBZUoUUKffPKJ4uLi9PPPP9vHPP/88xo8eLAiIiLSvPxhw4bZPyN3795dsbGx9vsSOy1NhQoV7DkbYzR06FAVKFBAXl5eyps3r8PR4XfOb7PZ9Mknn+jJJ59U1qxZVbRoUS1evNhh+Tt37lSTJk3k5+enhx56SM8//7z++ecf+/3J1caUPgvfadOmTapYsaK8vb1VpUoVRUdHO9x/8+ZNde7cWYUKFZKPj4+KFy+uCRMm2O//9ddf5eHhoZMnTzrM17dvX9WuXdu+jAULFiSo8cm9N5Ko8ZkdzXngPjZz5kz5+vpq48aNev/99zV8+PBEPyTGmzNnjvz8/JL9y4hTnFy+fFnvvvuuZs2apbVr1+rs2bNq06ZNquc/ceKE2rZtqxdeeEF79uzRqlWr9NRTT8kYk+Q8Ka2bVq1a6dSpU1qyZIm2bNmiSpUq6dFHH9W///4rSXruueeUP39+/fbbb9qyZYtef/11eXh4SJJ69eqla9eu6ddff9WOHTs0evToZL/17tixo44ePaqVK1dqwYIFmjx5sk6dOuUwJkuWLJo4caJ27dqlmTNnasWKFRo4cKAkydfXV23atFFkZKTDPJGRkWrZsqX9CMM1a9aoSpUq9vtv3LihFi1aKDw8XNu3b9f69ev14osvymaz2cdUqVJFN27c0MaNG5N9DgAAGcsZNf3ixYsKDQ1VSEiImjdvrl27dqUY5/79+/Xll1/q22+/1Y8//qjo6Gj17Nkz1Xlu3rxZffr00fDhw7V37179+OOPqlOnTrLzjBs3zv4huWfPnurRo4f27t0rSbp+/boaNWqkbNmyafXq1Vq7dq38/PzUuHFjxcbGplgLk6v3d4qLi9NTTz0lT09Pbdy4UR9//LFee+21BOOyZcumqKgo7d69WxMmTND06dM1fvx4SbcaExEREYnW9I4dOypLliz6999/tXv3boeanpr3QlWrVtWxY8ccvpgAAGQsZ38mv3z5sq5fv64cOXLccy4///yzvcZ8/vnnWrhwoYYNG5bq+b/66iuNHz9eU6dO1b59+7Ro0SKVLVs22XmGDRum1q1ba/v27Xrsscf03HPP2T+Tnz17VvXr11fFihW1efNm/fjjj/r777/VunVrScnXxtR8Fr7dxYsX9fjjj6tUqVLasmWLhg4dqv79+zuMiYuLU/78+TV//nzt3r1bgwcP1htvvKEvv/xSklSnTh0VLlxYn332mX2e69eva86cOXrhhRckSdu3b9e5c+ccanxq3htVrVpVmzZt0rVr11LzVOBBYwDcl8LDw80jjzziMO3hhx82r732WpLznD9/3uzbty/Zv8uXL6fq8UNDQ8348eNTHBcZGWkkmQ0bNtin7dmzx0gyGzduNMYY06FDB9O8eXOH+V5++WUTHh5ujDFmy5YtRpI5dOhQoo9x5/wprZvVq1cbf39/c/XqVYcxYWFhZurUqcYYY7Jly2aioqISfbyyZcuaoUOHJp/4/7d3714jyWzatMk+LT7/5Nbf/PnzTc6cOe23N27caNzc3Mxff/1ljDHm77//Nu7u7mbVqlXGGGP+++8/I8n8+uuv9nnOnDljJNnHJCUwMDDJXAEAGc8ZNX3dunVm5syZJjo62qxatco8/vjjxt/f3xw9ejTJeYYMGWLc3NzMsWPH7NOWLFlismTJYk6cOGHP5eWXX3aYr3nz5qZDhw7GGGO++uor4+/vb86fP5/kurh9/tDQUNOuXTv77bi4OBMcHGymTJlijDHms88+M8WLFzdxcXH2MdeuXTM+Pj5m6dKlKdbC5Or9nZYuXWrc3d3N8ePHHfKXZL7++usk5xszZoypXLmy/fYXX3xhAgMD7e9DtmzZYmw2m4mJiTHGGBMdHW0kmSNHjtjnSem9kDHGnDt3LlV1HwCQPpz9mdwYY3r06GEKFy5srly5kuC+mJgYI8lER0enuJwOHTqYHDlymEuXLtmnTZkyxfj5+ZmbN28aYxLvAZQvX94MGTLEGGPMuHHjTLFixUxsbGyij3Hn/JLMW2+9Zb998eJFI8ksWbLEGGPMiBEjTMOGDR2WcfToUSPJ7N27N9namNrPwvGmTp1qcubM6bAep0yZkuL669Wrl3n66aftt0ePHm1Klixpv/3VV18ZPz8/c/HiRWOMMV9//bVxc3NzeN+S0nsjY4zZtm1biu8D8ODiyHngPlauXDmH23ny5ElwRPbtsmXLpiJFiiT75+Pjk+5xuru76+GHH7bfLlGihLJnz649e/akav7y5cvr0UcfVdmyZdWqVStNnz5d//33X7LzJLdutm3bposXLypnzpwORyjExMTowIEDkqRXXnlFXbp0UUREhEaNGmWfLkl9+vTRO++8o1q1amnIkCHavn17knHs2bNH7u7uqly5coL8b7d8+XI9+uijypcvn7Jly6bnn39eZ86c0eXLlyXd+qa8dOnSmjlzpiRp9uzZCg0NtX+jHn9amtsv5pcjRw517NhRjRo1UrNmzTRhwgSdOHEiQYw+Pj72xwEAOIfVNb1GjRpq3769KlSooPDwcC1cuFBBQUGaOnVqsnEWKFBA+fLlc1hOXFyc/Uj2lDRo0EChoaEqXLiwnn/+ec2ZMyfFGnT7uok/Fc/tNX3//v3Kli2bvZ7nyJFDV69e1YEDB1KshcnV+zvt2bNHISEhyps3r0P+d/riiy9Uq1Yt5c6dW35+fnrrrbd05MgR+/0tWrSQm5ubvv76a0m3Ls5br149FSxYUFLiNT0174Xin29qOgBYx5mfyUeNGqV58+bp66+/TpeLupcvX97hnPA1atTQxYsXHU5Jm5xWrVrpypUrKly4sLp27aqvv/5aN27cSHae29efr6+v/P39HWr8ypUrHT6zlyhRQpJ04MCBZGtjaj8Lx9uzZ4/KlSvnsB4Tq/EfffSRKleurKCgIPn5+WnatGkONb5jx47av3+/NmzYIOlWjW/durV8fX0l3arxXl5eDkfwp+a9ETU+c6M5D9zH7vzZtc1ms5+rNDHOOq1NSrJkyZLgFDXx52aVJDc3Ny1btkxLlixRqVKlNGnSJBUvXlwxMTFJLjO5dXPx4kXlyZNHW7dudfjbu3evBgwYIEkaOnSodu3apaZNm2rFihUqVaqU/UN0ly5ddPDgQT3//PPasWOHqlSpokmTJt11/ocOHdLjjz+ucuXK6auvvtKWLVv00UcfSZLDOf66dOmiqKgoSbd+/t6pUyd7Uc+ZM6dsNluCD+qRkZFav369atasqS+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" ] diff --git a/implementations/BAA10Y_forecasting/99_starter_agent.ipynb b/implementations/BAA10Y_forecasting/99_starter_agent.ipynb index 150c929d..4d75362a 100644 --- a/implementations/BAA10Y_forecasting/99_starter_agent.ipynb +++ b/implementations/BAA10Y_forecasting/99_starter_agent.ipynb @@ -5,12 +5,11 @@ "id": "cell-00", "metadata": {}, "source": [ - "# S&P 500 — Your Starter Agent\n", + "# BAA10Y — Your Starter Agent\n", "\n", "**If you're not sure what to do next, continue from here.**\n", "\n", - "This notebook is a fresh, hackable agent for the S&P 500 use case — deliberately *not* wired into the numbered curriculum. It gives you our common building blocks behind simple toggles, so you can start building something of your own:\n", - "\n", + "This notebook is a fresh, hackable agent for the BAA10Y corporate credit spread use case. It gives you our common building blocks behind simple toggles:\n", "- **optional news search** — bounded, cutoff-aware Google Search (proxy-only)\n", "- **optional code execution** — an E2B Python sandbox\n", "- **two lightweight skills** — *tool-usage playbooks* in `starter_agent/skills/`\n", @@ -20,10 +19,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "cell-01", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RUN_AGENT = True | model = gemini-3.1-flash-lite-preview\n" + ] + } + ], "source": [ "import warnings\n", "from pathlib import Path\n", @@ -48,9 +55,10 @@ "# ── Run guard ──────────────────────────────────────\n", "# Live agent calls cost tokens and need PROXY_* in the repo-root .env, plus warm\n", "# data caches. Default False so `Run All` is safe; set True to call the model.\n", - "RUN_AGENT = False\n", + "#RUN_AGENT = False\n", + "RUN_AGENT = True\n", "\n", - "from sp500_forecasting.starter_agent import (\n", + "from BAA10Y_forecasting.starter_agent.agent import (\n", " build_starter_agent_config,\n", " build_starter_agent_predictor,\n", ")\n", @@ -72,10 +80,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "cell-03", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Agent: baa10y_starter_agent\n", + "Search enabled: True\n", + "Code-exec enabled: False\n", + "Skills loaded: ['forecasting', 'research-playbook']\n", + "\n", + "── System instruction (edit this in starter_agent/agent.py) ──\n", + "\n", + "## Role\n", + "\n", + "You are a corporate-credit-market analyst specializing in the BAA10Y spread: Moody's Seasoned Baa Corporate Bond Yield Relative to the 10-Year Treasury yield. You understand Federal Reserve policy, Treasury rates, inflation and employment data, economic growth, corporate defaults and downgrades, refinancing conditions, market liquidity, equity volatility, and investor risk sentiment.\n", + "\n", + "keep your reasoning transparent and your claims honest, BAA10Y spread changes are often centered near zero, while volatility and tail risk vary over time. Remain conservative about directional predictability.\n", + "\n", + "## How to respond\n", + "\n", + "- For open-ended questions, scenario analysis, or anything conversational, answer directly and concisely — do NOT ask for a JSON payload.\n", + "- When you are handed a task that asks for a structured probabilistic forecast, produce a calibrated one. ...\n" + ] + } + ], "source": [ "config = build_starter_agent_config(\n", " model=AGENT_MODEL,\n", @@ -104,23 +136,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "cell-05", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The BAA10Y spread is currently governed by a tug-of-war between resilient economic fundamentals and lingering policy-induced volatility.\n", + "\n", + "### Macroeconomic and Credit-Market Risks\n", + "* **Tightening Risks (Spread Compression):**\n", + " * **Economic Soft Landing:** Continued strength in employment and GDP growth reduces default probability, sustaining investor demand for credit risk premium.\n", + " * **Easing Financial Conditions:** If inflation prints remain benign, the Federal Reserve’s trajectory for rate cuts sustains liquidity and suppresses volatility.\n", + "* **Widening Risks (Spread Expansion):**\n", + " * **Policy Miscalculation:** Persistent or re-accelerating inflation forcing a hawkish Fed pivot or delayed cuts, driving Treasury volatility and investor risk aversion.\n", + " * **Refinancing/Liquidity Shocks:** While many firms have termed out debt, a sudden \"risk-off\" event in broader markets could lead to temporary liquidity dry-ups, forcing credit spreads wider as investors demand higher compensation for illiquidity.\n", + " * **Geopolitical/Fiscal Shocks:** Unanticipated fiscal deterioration or trade/tariff policies that weigh on corporate earnings outlooks.\n", + "\n", + "### Impact on Forecast Characteristics\n", + "Whether forecasting at a 5-business-day or 21-business-day horizon, the primary impacts are as follows:\n", + "\n", + "* **Location:** The median expected change remains near zero. The \"drift\" is negligible because the spread is a mean-reverting series; absent a specific shock, the most likely outcome is that the current risk premium remains stable.\n", + "* **Uncertainty (Standard Deviation):** \n", + " * **5-day:** Primarily driven by daily market \"noise\" and high-frequency sentiment shifts (e.g., equity volatility).\n", + " * **21-day:** Heavily influenced by the \"macro calendar\"—the arrival of non-farm payrolls, CPI prints, or FOMC communications—which can fundamentally shift the regime, widening the distribution of outcomes.\n", + "* **Tail Risk:** \n", + " * **Asymmetry:** The distribution of BAA10Y changes is typically **positively skewed (leptokurtic)**. While compression has limits (the floor), widening events (the tails) can be rapid and extreme during \"risk-off\" episodes.\n", + " * **Time Sensitivity:** 21-day forecasts exhibit \"fat tails\" significantly more than 5-day forecasts. Over a month, the cumulative probability of a \"shock\" event (e.g., a geopolitical crisis) increases non-linearly, thickening the left tail (widening risk) relative to a short-term 5-day view.\n", + "\n", + "**Summary:** For any horizon, be wary of mean-reversion assumptions during periods of macro-regime uncertainty. The risk is asymmetric; the downside (spread widening) is historically faster and more volatile than the upside (tightening).\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Root node baa10y_starter_agent was cancelled.\n" + ] + } + ], "source": [ "from aieng.forecasting.methods.agentic import build_adk_agent\n", "from aieng.forecasting.methods.agentic.adk_runner import AdkTextRunner, AdkTextRunnerConfig\n", "\n", "\n", "QUESTION = (\n", - " \"What are the key macro risks for U.S. equities over the next month, and how \"\n", - " \"should they shape the spread of a 1-week S&P 500 return forecast? Be concise.\"\n", + " \"What are the main macroeconomic and credit-market risks that could \"\n", + " \"widen or tighten the BAA10Y spread over the next month? Explain how \"\n", + " \"those risks should affect the location, uncertainty, and tail risk \"\n", + " \"of a 5-business-day or 21-bussiness-day BAA10Y spread-change forecast. Be concise.\"\n", ")\n", "\n", "if RUN_AGENT:\n", " chat_agent = build_adk_agent(config) # schema-free: plain text in, text out\n", - " runner = AdkTextRunner(chat_agent, config=AdkTextRunnerConfig(app_name=\"sp500_starter_chat\"))\n", + " runner = AdkTextRunner(chat_agent, config=AdkTextRunnerConfig(app_name=\"baa10y_starter_chat\"))\n", " reply = await runner.run_text_async(QUESTION) # noqa: F704, PLE1142\n", " print(reply)\n", "else:\n", @@ -135,29 +206,51 @@ "---\n", "## 3. Score one prediction against a known outcome *(Track 1)*\n", "\n", - "Now run the agent as a `Predictor`. We pick the **most recent origin whose horizon has already resolved**, forecast the 1-week S&P 500 return, and check whether the actual return landed inside the agent's 80% band. (One origin can't tell you if the agent is *calibrated*; that's what the backtest in `01_sp500_multivariate_backtest.ipynb` is for.) Live, so gated by `RUN_AGENT`.\n" + "Now run the agent as a `Predictor`. We pick the **most recent origin whose horizon has already resolved**, forecast the 5-business-day BAA10Y spread-change forecast, and check whether the actual return landed inside the agent's 80% band. \n", + "Both the forecast and realised target are already measured in **basis points** so must not multiply them by 100 again.\n", + "(One origin can't tell you if the agent is *calibrated*; that's what the backtest in `01_BAA10Y_multivariate_backtest.ipynb` is for.) Live, so gated by `RUN_AGENT`.\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "cell-07", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Origin as_of=2026-07-08 horizon=5b (1 week) (latest data 2026-07-16)\n", + "\n", + " agent point : +0.0000 (+0.00%)\n", + " agent 80% CI : [-0.0700, +0.0700]\n", + " actual : +0.0400 (+4.00%) in 80% band? yes ✓\n", + "\n", + "Rationale: The forecast reflects a cautious outlook, acknowledging recent slight upward pressure on the spread while adhering to the principle that spread changes are unpredictable and generally centered around zero. Market stability indicators (VIX, yield curve) suggest no immediate catalyst for a significant\n" + ] + } + ], "source": [ "from datetime import datetime, timezone\n", "\n", "\n", "if RUN_AGENT:\n", " from aieng.forecasting.evaluation.task import ForecastingTask\n", - " from sp500_forecasting import build_sp500_multivariate_service\n", - " from sp500_forecasting.data import sp500_logret_series_id\n", + " from BAA10Y_forecasting import build_baa10y_multivariate_service\n", + " from BAA10Y_forecasting.data import baa10y_change_series_id\n", + " from BAA10Y_forecasting.data import DEFAULT_COVARIATE_SERIES_IDS\n", + "\n", + "\n", + " # Valid BAA10Y horizons are 1, 5, and 21 business days.\n", "\n", " HORIZON = 5\n", - " COVARIATES = [\"vix_level_l1b\", \"ust10y_level_l1b\", \"oil_log_ret_1b_l1b\"]\n", - " svc = build_sp500_multivariate_service(covariate_series_ids=COVARIATES)\n", + " COVARIATES = list(\n", + " DEFAULT_COVARIATE_SERIES_IDS\n", + " )\n", + " svc = build_baa10y_multivariate_service(covariate_series_ids=COVARIATES)\n", " now = datetime.now(tz=timezone.utc).replace(tzinfo=None)\n", - " tgt = sp500_logret_series_id(HORIZON)\n", + " tgt = baa10y_change_series_id(HORIZON)\n", " full = svc.get_series(tgt, as_of=now)\n", " full[\"timestamp\"] = pd.to_datetime(full[\"timestamp\"])\n", " last_date = full[\"timestamp\"].iloc[-1]\n", @@ -166,11 +259,11 @@ " AS_OF = last_date - pd.offsets.BDay(HORIZON + 1)\n", "\n", " task = ForecastingTask(\n", - " task_id=f\"sp500_logret_{HORIZON}b\",\n", + " task_id=f\"BAA10Y_spreadchange_{HORIZON}b\",\n", " target_series_id=tgt,\n", " horizons=[HORIZON],\n", " frequency=\"B\",\n", - " description=f\"S&P 500 cumulative log return, {HORIZON} business days ahead (starter).\",\n", + " description=f\"BAA10Y cumulative spread change in basis points,, {HORIZON} business days ahead (starter).\",\n", " )\n", " ctx = svc.context(as_of=AS_OF)\n", " pred = build_starter_agent_predictor(config, covariate_series_ids=COVARIATES).predict(task, ctx)[0]\n", @@ -209,9 +302,9 @@ "3. **Sharpen the skills.** The two files in `starter_agent/skills/` are short on purpose. Add your best queries to `research-playbook`, or a new diagnostic to `code-analysis-playbook`. The agent picks them up automatically.\n", "4. **Change the question and the origin.** Try a different `QUESTION` in §2 and a different origin in §3.\n", "5. **Widen the covariate panel.** Pass `DEFAULT_COVARIATE_SERIES_IDS` (11 series) to both the service and `build_starter_agent_predictor(...)` and see if the extra context helps.\n", - "6. **Forecast other horizons.** Swap `HORIZON` to 1 (next session) or 21 (1 month) — each maps to its own `sp500_logret_{h}b` target.\n", + "6. **Forecast other horizons.** Swap `HORIZON` to 1 (next session) or 21 (1 month) — each maps to its own `baa10y_change_{h}b` target.\n", "\n", - "Bigger ideas — the full conventional-vs-LLM-Process comparison and direction metrics in `01_sp500_multivariate_backtest.ipynb` — are in the use-case `README.md` and `planning-docs/roadmap.md`.\n" + "Bigger ideas — the full conventional-vs-LLM-Process comparison and direction metrics in `01_BAA10Y_multivariate_backtest.ipynb` — are in the use-case `README.md` and `planning-docs/roadmap.md`.\n" ] } ], diff --git a/implementations/BAA10Y_forecasting/analysis.py b/implementations/BAA10Y_forecasting/analysis.py index 6bfba71e..fe01c2aa 100644 --- a/implementations/BAA10Y_forecasting/analysis.py +++ b/implementations/BAA10Y_forecasting/analysis.py @@ -1,4 +1,8 @@ -"""Notebook-oriented formatting and direction metrics for the S&P 500 demo.""" +"""Notebook-oriented formatting and direction metrics for the BAA10Y demo. + + Positive or "up" means spread widening. Negative or "down" means spread + tightening. Target values and CRPS are expected to be in basis points. +""" from __future__ import annotations @@ -34,7 +38,7 @@ def style_results_dataframe(df: pd.DataFrame) -> Styler: return df.style.format(fmt, na_rep="—") -def prob_return_above_threshold_from_quantiles(quantiles: dict[float, float], threshold: float = 0.0) -> float: +def prob_spread_change_above_threshold_from_quantiles(quantiles: dict[float, float], threshold: float = 0.0) -> float: """Approximate ``P(X > threshold)`` from a piecewise-linear CDF through quantile pairs.""" pairs = sorted(((float(v), float(q)) for q, v in quantiles.items()), key=lambda x: x[0]) if not pairs: @@ -51,7 +55,11 @@ def build_direction_eval_frame( target_series_id: str, data_service: DataService, ) -> pd.DataFrame: - """Align each scored prediction with the realized log return at ``forecast_date``.""" + """Align each prediction with the realised BAA10Y spread change. + + Target values are already in basis points. No scaling is performed here. + Positive values mean spread widening. + """ as_of_now = datetime.now(tz=timezone.utc).replace(tzinfo=None) full_series = data_service.get_series(target_series_id, as_of=as_of_now) full = full_series.copy() @@ -67,7 +75,7 @@ def build_direction_eval_frame( continue actual = float(lookup.loc[ts]) qmap = p.payload.quantiles - prob_up = prob_return_above_threshold_from_quantiles(qmap, threshold=0.0) + prob_up = prob_spread_change_above_threshold_from_quantiles(qmap, threshold=0.0) rows.append( { "as_of": p.as_of, @@ -88,7 +96,11 @@ def direction_classification_metrics( y_pred_col: str = "pred_up_point", y_score_col: str = "prob_up", ) -> pd.Series: - """Binary metrics for predicting a positive next-session log return.""" + """Binary metrics for predicting BAA10Y widening versus tightening. + + Class 1 or "up" means a positive spread change: widening. + Class 0 or "down" means a non-positive spread change: tightening or unchanged. + """ from sklearn.metrics import ( # noqa: PLC0415 accuracy_score, balanced_accuracy_score, @@ -118,8 +130,7 @@ def direction_classification_metrics( kappa = float(cohen_kappa_score(y_true, y_pred)) baseline_acc = max(pos_rate, 1.0 - pos_rate) - maj = int(pos_rate >= 0.5) - baseline_always_up_acc = float((y_true == maj).mean()) + baseline_always_up_acc = pos_rate roc = float("nan") if y_score_col in df.columns and np.unique(y_true).size == 2: @@ -156,6 +167,6 @@ def direction_classification_metrics( __all__ = [ "build_direction_eval_frame", "direction_classification_metrics", - "prob_return_above_threshold_from_quantiles", + "prob_spread_change_above_threshold_from_quantiles", "style_results_dataframe", ] diff --git a/implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml b/implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml index efa83549..e5beef03 100644 --- a/implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml +++ b/implementations/BAA10Y_forecasting/specs/baa10y_eval_2026.yaml @@ -1,5 +1,5 @@ # Protected eval — held-out 2026 window, scored through multi_evaluate() with a budget. -# + # This is the honest scoreboard. 2026 is unambiguously after the Gemini training # cutoff, so neither the numerical methods nor the LLM-Process can have seen the # outcomes. Treat it as scarce: `max_runs` caps how many times the spec may be @@ -42,8 +42,14 @@ tasks: BAA10Y close-to-close cumulative change, 21 business days ahead (forward 1-month change). -start: "2026-02-02" -end: "2026-03-23" # 8 weekly origins; all resolve at h=21 + +# Eight weekly Tuesday origins: +# 2026-02-03, 2026-02-10, 2026-02-17, 2026-02-24, +# 2026-03-03, 2026-03-10, 2026-03-17, 2026-03-24. + +start: "2026-02-03" # The original schedule ( 2026-02-02 )included Monday, February 16, 2026, + # a U.S. market holiday. skip 2026-02-16 +end: "2026-03-24" # 8 weekly origins; all resolve at h=21 stride: 5 # weekly origins warmup: 250 From f4be58aae482d783a9066b023d6a6b88e08cda4f Mon Sep 17 00:00:00 2001 From: default Date: Mon, 20 Jul 2026 03:42:20 +0000 Subject: [PATCH 13/24] align with main --- .../00_baa10y_data_exploration.ipynb | 29199 +++++++++++++++- 1 file changed, 29185 insertions(+), 14 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb index 87618d34..df4e53b4 100644 --- a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb +++ b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "4504704b", "metadata": { "execution": { @@ -53,12 +53,12 @@ "id": "f392ac73", "metadata": {}, "source": [ - "## 1. Build the DataService" + "## 1. Build the BAA10Y DataService" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 16, "id": "884638ec", "metadata": { "execution": { @@ -70,7 +70,7 @@ }, "outputs": [], "source": [ - "svc = build_baa10y_target_service()" + "svc = build_baa10y_target_service(windows=(1, 5, 21))" ] }, { @@ -83,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 17, "id": "3d4d98ad", "metadata": {}, "outputs": [ @@ -124,7 +124,7 @@ " baa10y_change_1b\n", " BAA10Y cumulative spread change over 1 busines...\n", " FRED (BAA10Y), derived\n", - " basis-points\n", + " percentage-points\n", " B\n", " 6911\n", " 2000-01-04\n", @@ -135,7 +135,7 @@ " baa10y_change_21b\n", " BAA10Y cumulative spread change over 21 busine...\n", " FRED (BAA10Y), derived\n", - " basis-points\n", + " percentage-points\n", " B\n", " 6891\n", " 2000-02-01\n", @@ -146,7 +146,7 @@ " baa10y_change_5b\n", " BAA10Y cumulative spread change over 5 busines...\n", " FRED (BAA10Y), derived\n", - " basis-points\n", + " percentage-points\n", " B\n", " 6907\n", " 2000-01-10\n", @@ -162,13 +162,18 @@ "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", "\n", - " source units frequency n_obs start end \n", - "0 FRED (BAA10Y), derived basis-points B 6911 2000-01-04 2026-06-30 \n", - "1 FRED (BAA10Y), derived basis-points B 6891 2000-02-01 2026-06-30 \n", - "2 FRED (BAA10Y), derived basis-points B 6907 2000-01-10 2026-06-30 " + " source units frequency n_obs start \\\n", + "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", + "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", + "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", + "\n", + " end \n", + "0 2026-06-30 \n", + "1 2026-06-30 \n", + "2 2026-06-30 " ] }, - "execution_count": 3, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -199,7 +204,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 18, "id": "d1bd6aed", "metadata": { "execution": { @@ -22070,6 +22075,29172 @@ ")\n", "fig.show()" ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "75cac7f3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coder/agentic-forecasting/implementations/sp500_forecasting/data.py:765: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", + " _handle_covariate_error(SERIES_ID_GOLD_RETURN, exc)\n" + ] + }, + { + "data": { + "text/html": [ + "
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9vix_log_ret_1b_l1bCBOE VIX close-to-close log return, lagged 1 b...Yahoo Finance (^VIX), derivedlog-returnB26462016-01-062026-07-15
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" + ], + "text/plain": [ + " series_id \\\n", + "0 cpi_mom_logdiff_l1b \n", + "1 dollar_index_log_ret_1b_l1b \n", + "2 fed_funds_level_l1b \n", + "3 nasdaq_log_ret_1b_l1b \n", + "4 oil_log_ret_1b_l1b \n", + "5 unemployment_rate_l1b \n", + "6 ust10y_level_l1b \n", + "7 ust2y10y_spread_l1b \n", + "8 vix_level_l1b \n", + "9 vix_log_ret_1b_l1b \n", + "\n", + " description \\\n", + "0 US CPI MoM log change, conservative release la... \n", + "1 Trade-weighted dollar index log return, lagged... \n", + "2 Effective federal funds rate, lagged 1 busines... \n", + "3 NASDAQ Composite close-to-close log return, la... \n", + "4 WTI oil spot log return, lagged 1 business day \n", + "5 US unemployment rate, conservative release lag... \n", + "6 US 10-year Treasury yield level, lagged 1 busi... \n", + "7 US 10Y minus 2Y Treasury spread, lagged 1 busi... \n", + "8 CBOE VIX close level, lagged 1 business day \n", + "9 CBOE VIX close-to-close log return, lagged 1 b... \n", + "\n", + " source units frequency n_obs start \\\n", + "0 FRED (CPIAUCSL), derived log-change B 2717 2016-01-15 \n", + "1 FRED (DTWEXBGS), derived log-return B 5347 2006-01-04 \n", + "2 FRED (DFF) percent B 18787 1954-07-02 \n", + "3 Yahoo Finance (^IXIC), derived log-return B 2647 2016-01-06 \n", + "4 FRED (DCOILWTICO), derived log-return B 10561 1986-01-06 \n", + "5 FRED (UNRATE) percent B 2739 2016-01-15 \n", + "6 FRED (DGS10) percent B 16829 1962-01-03 \n", + "7 FRED (DGS10, DGS2), derived percent-points B 13069 1976-06-02 \n", + "8 Yahoo Finance (^VIX) index-level B 2647 2016-01-05 \n", + "9 Yahoo Finance (^VIX), derived log-return B 2646 2016-01-06 \n", + "\n", + " end \n", + "0 2026-06-15 \n", + "1 2026-07-02 \n", + "2 2026-07-06 \n", + "3 2026-07-17 \n", + "4 2026-06-29 \n", + "5 2026-07-15 \n", + "6 2026-07-06 \n", + "7 2026-07-06 \n", + "8 2026-07-15 \n", + "9 2026-07-15 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", + "\n", + "DATA_HISTORY_START = \"2016-01-01\"\n", + "REFRESH_CACHE = False\n", + "\n", + "covariate_svc = build_sp500_multivariate_service(\n", + " windows=(), #(1, 5, 21),\n", + " include_covariates=True,\n", + " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", + " start=DATA_HISTORY_START,\n", + " refresh=REFRESH_CACHE,\n", + ")\n", + "\n", + "covariate_svc.summary()" + ] + }, + { + "cell_type": "markdown", + "id": "1c9c661e", + "metadata": {}, + "source": [ + "## 5. Plot the covariate time series\n", + "\n", + "Code generated by Claude Sonnet 5." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "41467da9", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "line": { + "color": "#3987e5", + "width": 1.2 + }, + "mode": "lines", + "name": "cpi_mom_logdiff_l1b", + "showlegend": false, + "type": "scatter", + "x": [ + "2016-01-15T00:00:00.000000", + "2016-01-18T00:00:00.000000", + "2016-01-19T00:00:00.000000", + "2016-01-20T00:00:00.000000", + "2016-01-21T00:00:00.000000", + "2016-01-22T00:00:00.000000", + "2016-01-25T00:00:00.000000", + "2016-01-26T00:00:00.000000", + "2016-01-27T00:00:00.000000", + "2016-01-28T00:00:00.000000", + "2016-01-29T00:00:00.000000", + "2016-02-01T00:00:00.000000", + "2016-02-02T00:00:00.000000", + "2016-02-03T00:00:00.000000", + "2016-02-04T00:00:00.000000", + "2016-02-05T00:00:00.000000", + 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"domain": [ + 0.7140000000000001, + 0.796 + ], + "title": { + "text": "percent" + } + }, + "yaxis4": { + "anchor": "x4", + "domain": [ + 0.612, + 0.694 + ], + "title": { + "text": "log-return" + } + }, + "yaxis5": { + "anchor": "x5", + "domain": [ + 0.51, + 0.592 + ], + "title": { + "text": "log-return" + } + }, + "yaxis6": { + "anchor": "x6", + "domain": [ + 0.40800000000000003, + 0.49000000000000005 + ], + "title": { + "text": "percent" + } + }, + "yaxis7": { + "anchor": "x7", + "domain": [ + 0.306, + 0.388 + ], + "title": { + "text": "percent" + } + }, + "yaxis8": { + "anchor": "x8", + "domain": [ + 0.20400000000000001, + 0.28600000000000003 + ], + "title": { + "text": "percent-points" + } + }, + "yaxis9": { + "anchor": "x9", + "domain": [ + 0.10200000000000001, + 0.184 + ], + "title": { + "text": "index-level" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "covariate_ids = covariate_svc.series_ids\n", + "covariate_plot_start = \"2016-01-01\"\n", + "\n", + "# Fixed categorical order (validated for CVD-safety), cycled since there are\n", + "# more covariates than hues in the base palette.\n", + "covariate_palette = [\n", + " \"#3987e5\", # blue\n", + " \"#008300\", # green\n", + " \"#d55181\", # magenta\n", + " \"#c98500\", # yellow\n", + " \"#199e70\", # aqua\n", + " \"#d95926\", # orange\n", + " \"#9085e9\", # violet\n", + " \"#e66767\", # red\n", + "]\n", + "\n", + "# Each covariate lives on its own scale (percent, log-return, index level, ...),\n", + "# so — as with the BAA10Y horizons above — every series gets its own row and\n", + "# y-axis rather than being forced onto one shared scale.\n", + "fig = make_subplots(\n", + " rows=len(covariate_ids),\n", + " cols=1,\n", + " shared_xaxes=True,\n", + " vertical_spacing=0.02,\n", + " subplot_titles=[covariate_svc.get_metadata(sid).description for sid in covariate_ids],\n", + ")\n", + "\n", + "covariate_ends = []\n", + "for row, series_id in enumerate(covariate_ids, start=1):\n", + " meta = covariate_svc.get_metadata(series_id)\n", + " s = covariate_svc.get_series(series_id, as_of=as_of).sort_values(\"timestamp\")\n", + " # Some covariates (fed funds, 10Y yield, ...) have decades of history that\n", + " # predate the others, so clip everyone to the same start for a fair view.\n", + " s = s[s[\"timestamp\"] >= covariate_plot_start]\n", + " if len(s) > 0:\n", + " covariate_ends.append(s[\"timestamp\"].max())\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=s[\"timestamp\"],\n", + " y=s[\"value\"],\n", + " mode=\"lines\",\n", + " name=series_id,\n", + " line=dict(color=covariate_palette[(row - 1) % len(covariate_palette)], width=1.2),\n", + " showlegend=False,\n", + " ),\n", + " row=row,\n", + " col=1,\n", + " )\n", + " fig.update_yaxes(title_text=meta.units, row=row, col=1)\n", + "\n", + "for start, end in recession_periods:\n", + " fig.add_vrect(\n", + " x0=start,\n", + " x1=end,\n", + " fillcolor=\"#ffffff\",\n", + " opacity=0.14,\n", + " line_width=0,\n", + " row=\"all\",\n", + " col=1,\n", + " )\n", + "\n", + "# Shared x-axes are linked via `matches` to this bottom axis, so the range\n", + "# only needs to (and, in plotly, only *can*) be set here — setting it with\n", + "# row=\"all\" is a silent no-op on matched axes.\n", + "covariate_plot_end = max(covariate_ends)\n", + "fig.update_xaxes(title_text=\"Timestamp\", row=len(covariate_ids), col=1)\n", + "fig.update_xaxes(range=[covariate_plot_start, covariate_plot_end], row=len(covariate_ids), col=1)\n", + "fig.update_annotations(font_size=11)\n", + "fig.update_layout(\n", + " title=\"Covariates by series (independent y-scales, shaded: NBER recessions)\",\n", + " template=\"plotly_dark\",\n", + " height=220 * len(covariate_ids),\n", + ")\n", + "fig.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a44abf3", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From c924a00a00b619e15c7794f277bb2ff88261e3d8 Mon Sep 17 00:00:00 2001 From: default Date: Thu, 23 Jul 2026 01:52:45 +0000 Subject: [PATCH 14/24] Add Diebold-Mariano forecast comparison for BacktestResult / EvalResult --- .../aieng/forecasting/evaluation/__init__.py | 4 + .../aieng/forecasting/evaluation/compare.py | 204 +++++++++++++ aieng-forecasting/pyproject.toml | 3 + .../forecasting/evaluation/test_compare.py | 272 ++++++++++++++++++ implementations/pyproject.toml | 2 +- .../01_sp500_multivariate_backtest.ipynb | 135 ++++++--- .../sp500_forecasting/leaderboard.py | 87 +++++- uv.lock | 92 ++++-- 8 files changed, 725 insertions(+), 74 deletions(-) create mode 100644 aieng-forecasting/aieng/forecasting/evaluation/compare.py create mode 100644 aieng-forecasting/tests/aieng/forecasting/evaluation/test_compare.py diff --git a/aieng-forecasting/aieng/forecasting/evaluation/__init__.py b/aieng-forecasting/aieng/forecasting/evaluation/__init__.py index 63a03c80..1114b95b 100644 --- a/aieng-forecasting/aieng/forecasting/evaluation/__init__.py +++ b/aieng-forecasting/aieng/forecasting/evaluation/__init__.py @@ -20,6 +20,7 @@ compute_rps, multi_backtest, ) +from aieng.forecasting.evaluation.compare import ComparisonResult, compare_multi, compare_results from aieng.forecasting.evaluation.describe import describe_spec, describe_task from aieng.forecasting.evaluation.eval import ( EvalBudgetExceededError, @@ -47,6 +48,7 @@ "BacktestSpec", "BinaryForecast", "CategoricalForecast", + "ComparisonResult", "ContinuousForecast", "EvalBudgetExceededError", "EvalResult", @@ -62,6 +64,8 @@ "backtest", "cached_backtest", "cached_multi_backtest", + "compare_multi", + "compare_results", "compute_brier_score", "compute_rps", "describe_spec", diff --git a/aieng-forecasting/aieng/forecasting/evaluation/compare.py b/aieng-forecasting/aieng/forecasting/evaluation/compare.py new file mode 100644 index 00000000..39073ead --- /dev/null +++ b/aieng-forecasting/aieng/forecasting/evaluation/compare.py @@ -0,0 +1,204 @@ +"""Diebold-Mariano forecast comparison for BacktestResult / EvalResult pairs. + +Wraps ``macroforecast.tests.dm_test`` (the ``comparison`` optional extra) so +two predictors evaluated against the same :class:`~aieng.forecasting.evaluation.task.ForecastingTask` +can be statistically compared on their per-origin CRPS/Brier/RPS loss series, +rather than only by eyeballing ``mean_score``. + +The Diebold-Mariano statistic itself is metric-agnostic — it just tests +whether two paired loss series have equal expected value — so this module +works for continuous, binary, and categorical tasks alike, as long as both +results were scored with the same metric. +""" + +from __future__ import annotations + +import logging +from typing import Any + +from aieng.forecasting.evaluation.backtest import BacktestResult, ScoreMetric +from aieng.forecasting.evaluation.eval import EvalResult +from aieng.forecasting.evaluation.task import ForecastingTask +from pydantic import BaseModel, Field + + +#: A single-task, single-run scored result — the common shape shared by +#: BacktestResult and EvalResult (predictions, scores, metric, predictor_id). +ScoredResult = BacktestResult | EvalResult + +_log = logging.getLogger(__name__) + + +def _task_for(result: ScoredResult) -> ForecastingTask: + """Return the task backing a result. Field name differs: spec vs eval_spec.""" + return result.spec.task if isinstance(result, BacktestResult) else result.eval_spec.task + + +class ComparisonResult(BaseModel): + """Outcome of a Diebold-Mariano comparison between two scored results. + + Parameters + ---------- + predictor_a_id, predictor_b_id : str + Identifiers of the two predictors being compared. + metric : {"crps", "brier", "rps"} + The scoring rule both results were evaluated with. + n_common : int + Number of aligned ``(as_of, forecast_date)`` pairs the test was run on. + statistic : float or None + The Diebold-Mariano test statistic. ``None`` (rare) if macroforecast + could not compute one. + p_value : float or None + Two-sided (or as configured) p-value for the null of equal predictive + accuracy. ``None`` when the test is degenerate — most commonly because + the two loss series are (numerically) identical, so their differential + has zero variance and the test statistic is undefined. This happens + legitimately when comparing near-duplicate predictors; it is not an + error. + metadata : dict[str, Any] + Passthrough of ``macroforecast``'s ``TestResult.metadata`` (e.g. + ``statistic_type``, ``hln_correction``, ``variance_estimator``). + """ + + predictor_a_id: str + predictor_b_id: str + metric: ScoreMetric + n_common: int = Field(description="Number of aligned (as_of, forecast_date) pairs used.") + statistic: float | None + p_value: float | None + metadata: dict[str, Any] = Field( + default_factory=dict, description="Passthrough of macroforecast's TestResult.metadata." + ) + + +def compare_results( + result_a: ScoredResult, + result_b: ScoredResult, + *, + horizon: int | None = None, + **dm_kwargs: Any, +) -> ComparisonResult: + """Run a Diebold-Mariano test between two BacktestResult/EvalResult on the same task. + + Aligns the two results' per-origin scores on ``(as_of, forecast_date)`` + rather than list position or ``forecast_date`` alone: ``run_eval_loop`` + (shared by :func:`~aieng.forecasting.evaluation.backtest.backtest` and + :func:`~aieng.forecasting.evaluation.eval.evaluate`) skips origins + independently per predictor, so the two ``scores`` lists are not + guaranteed to line up; and for multi-horizon tasks, ``forecast_date`` alone + is not a safe join key since two different ``(as_of, horizon)`` pairs can + land on the same date. + + Both results' ``scores`` are already proper-scoring-rule losses (CRPS, + Brier, or RPS — lower is better), so they are passed to + :func:`macroforecast.tests.dm_test` with ``input_type="loss"``. + + Parameters + ---------- + result_a, result_b : BacktestResult | EvalResult + Results to compare. Must share ``metric`` and target the same task + (checked via the first prediction's ``task_id``). + horizon : int or None + Forecast horizon passed to ``dm_test`` for its HAC/HLN variance + correction. Defaults to ``result_a``'s task ``.horizon`` (the max + horizon) when omitted. + **dm_kwargs : Any + Forwarded to :func:`macroforecast.tests.dm_test` (e.g. + ``small_sample``, ``alternative``, ``hac_lags``). + + Returns + ------- + ComparisonResult + The DM statistic, p-value, and comparison metadata. + + Raises + ------ + ValueError + If ``result_a`` and ``result_b`` have different ``metric`` values, + target different tasks, or have no overlapping + ``(as_of, forecast_date)`` pairs to compare. + """ + if result_a.metric != result_b.metric: + raise ValueError( + f"Cannot DM-compare results scored with different metrics: {result_a.metric!r} vs {result_b.metric!r}." + ) + task_id_a = result_a.predictions[0].task_id + task_id_b = result_b.predictions[0].task_id + if task_id_a != task_id_b: + raise ValueError(f"Cannot DM-compare results for different tasks: {task_id_a!r} vs {task_id_b!r}.") + + by_key_a = {(p.as_of, p.forecast_date): s for p, s in zip(result_a.predictions, result_a.scores, strict=True)} + by_key_b = {(p.as_of, p.forecast_date): s for p, s in zip(result_b.predictions, result_b.scores, strict=True)} + common = sorted(set(by_key_a) & set(by_key_b)) + if not common: + raise ValueError( + f"No overlapping (as_of, forecast_date) pairs between '{result_a.predictor_id}' " + f"and '{result_b.predictor_id}' for task '{task_id_a}'." + ) + loss_a = [by_key_a[key] for key in common] + loss_b = [by_key_b[key] for key in common] + resolved_horizon = horizon if horizon is not None else _task_for(result_a).horizon + + # Lazy import: the `comparison` optional dependency need not be installed + # to import this module (only to actually run a comparison). + from macroforecast.tests import dm_test # noqa: PLC0415 + + dm = dm_test(loss_a, loss_b, horizon=resolved_horizon, input_type="loss", **dm_kwargs) + + return ComparisonResult( + predictor_a_id=result_a.predictor_id, + predictor_b_id=result_b.predictor_id, + metric=result_a.metric, + n_common=len(common), + statistic=None if dm.statistic is None else float(dm.statistic), + p_value=None if dm.p_value is None else float(dm.p_value), + metadata=dict(dm.metadata), + ) + + +def compare_multi( + results_a: dict[str, ScoredResult], + results_b: dict[str, ScoredResult], + *, + horizon: int | None = None, + **dm_kwargs: Any, +) -> dict[str, ComparisonResult]: + """DM-compare two multi-target result dicts task by task. + + Intended for the ``dict[task_id, BacktestResult]`` / ``dict[task_id, + EvalResult]`` output of + :func:`~aieng.forecasting.evaluation.backtest.multi_backtest` and + :func:`~aieng.forecasting.evaluation.eval.multi_evaluate`. + + Tasks present in only one of the two dicts are logged at ``WARNING`` and + omitted, and a task that fails inside :func:`compare_results` (e.g. no + overlapping origins) is likewise logged and omitted rather than aborting + the whole batch — matching the resilience style of + :func:`~aieng.forecasting.evaluation.artifacts.cached_multi_backtest`. + + Parameters + ---------- + results_a, results_b : dict[str, BacktestResult | EvalResult] + Per-task results for two predictors, keyed by ``task_id``. + horizon : int or None + Forwarded to :func:`compare_results` for every task. + **dm_kwargs : Any + Forwarded to :func:`compare_results` (and in turn to ``dm_test``). + + Returns + ------- + dict[str, ComparisonResult] + Keyed by ``task_id``, one entry per successfully compared shared task. + """ + shared = sorted(set(results_a) & set(results_b)) + only_one_side = (set(results_a) - set(results_b)) | (set(results_b) - set(results_a)) + for task_id in sorted(only_one_side): + _log.warning("Task '%s' present in only one result set — skipping DM comparison.", task_id) + + out: dict[str, ComparisonResult] = {} + for task_id in shared: + try: + out[task_id] = compare_results(results_a[task_id], results_b[task_id], horizon=horizon, **dm_kwargs) + except ValueError as exc: + _log.warning("DM comparison failed for task '%s' — skipping: %s", task_id, exc) + return out diff --git a/aieng-forecasting/pyproject.toml b/aieng-forecasting/pyproject.toml index 11c07640..f6e95a08 100644 --- a/aieng-forecasting/pyproject.toml +++ b/aieng-forecasting/pyproject.toml @@ -40,6 +40,9 @@ numerical = [ "lightgbm>=4.6.0", "statsforecast>=2.0.1", ] +comparison = [ + "macroforecast>=0.9.5", +] [dependency-groups] dev = [ diff --git a/aieng-forecasting/tests/aieng/forecasting/evaluation/test_compare.py b/aieng-forecasting/tests/aieng/forecasting/evaluation/test_compare.py new file mode 100644 index 00000000..927f9ce4 --- /dev/null +++ b/aieng-forecasting/tests/aieng/forecasting/evaluation/test_compare.py @@ -0,0 +1,272 @@ +"""Tests for the Diebold-Mariano comparison helpers in ``compare.py``.""" + +import math +from datetime import datetime +from unittest.mock import MagicMock + +import pandas as pd +import pytest + + +pytest.importorskip("macroforecast") + +from aieng.forecasting.data.context import ForecastContext # noqa: E402 +from aieng.forecasting.data.models import SeriesMetadata # noqa: E402 +from aieng.forecasting.data.service import DataService # noqa: E402 +from aieng.forecasting.evaluation.backtest import ( # noqa: E402 + BacktestResult, + BacktestSpec, + MultiTargetBacktestSpec, + backtest, + multi_backtest, +) +from aieng.forecasting.evaluation.compare import ComparisonResult, compare_multi, compare_results # noqa: E402 +from aieng.forecasting.evaluation.eval import EvalResult, EvalSpec, evaluate # noqa: E402 +from aieng.forecasting.evaluation.prediction import ( # noqa: E402 + STANDARD_QUANTILES, + ContinuousForecast, + Prediction, +) +from aieng.forecasting.evaluation.predictor import Predictor # noqa: E402 +from aieng.forecasting.evaluation.task import ForecastingTask # noqa: E402 + + +# --------------------------------------------------------------------------- +# Fixtures and helpers +# --------------------------------------------------------------------------- + + +def _make_task(task_id: str = "test_task", series_id: str = "test_series", horizon: int = 12) -> ForecastingTask: + return ForecastingTask( + task_id=task_id, + target_series_id=series_id, + horizons=[horizon], + frequency="MS", + description="Test task", + ) + + +def _make_backtest_spec( + task: ForecastingTask | None = None, + start: str = "2005-01-01", + end: str = "2020-01-01", + stride: int = 3, + warmup: int = 24, +) -> BacktestSpec: + return BacktestSpec( + task=task or _make_task(), + start=datetime.fromisoformat(start), + end=datetime.fromisoformat(end), + stride=stride, + warmup=warmup, + ) + + +def _make_eval_spec( + task: ForecastingTask | None = None, + start: str = "2005-01-01", + end: str = "2020-01-01", + stride: int = 3, + warmup: int = 24, +) -> EvalSpec: + return EvalSpec( + spec_id="test_eval", + task=task or _make_task(), + start=datetime.fromisoformat(start), + end=datetime.fromisoformat(end), + stride=stride, + warmup=warmup, + ) + + +def _build_data_service(*series_ids: str, series_start: str = "2000-01-01", series_end: str = "2026-01-01") -> DataService: + """Build a DataService with one synthetic, linearly increasing monthly series per id.""" + dates = pd.date_range(start=series_start, end=series_end, freq="MS") + svc = DataService() + for series_id in series_ids or ("test_series",): + df = pd.DataFrame({"timestamp": dates, "value": range(len(dates))}) + adapter = MagicMock() + adapter.fetch.return_value = df + meta = SeriesMetadata( + series_id=series_id, + description="Synthetic test series", + source="test", + units="units", + frequency="MS", + ) + svc.register(series_id, adapter, meta) + return svc + + +class ConstantPredictor(Predictor): + """Test predictor that always returns a constant forecast, with a caller-set id.""" + + def __init__(self, value: float = 100.0, predictor_id: str = "constant") -> None: + """Store the constant point forecast value and predictor id.""" + self._value = value + self._predictor_id = predictor_id + + @property + def predictor_id(self) -> str: + """Caller-assigned id, so two instances can be told apart in a comparison.""" + return self._predictor_id + + def predict(self, task: ForecastingTask, context: ForecastContext) -> list[Prediction]: + """Emit one constant prediction per requested horizon step.""" + offset = pd.tseries.frequencies.to_offset(task.frequency) + point = self._value + return [ + Prediction( + predictor_id=self.predictor_id, + task_id=task.task_id, + issued_at=datetime(2024, 1, 1), + as_of=context.as_of, + forecast_date=(pd.Timestamp(context.as_of) + offset * h).to_pydatetime(), + payload=ContinuousForecast( + point_forecast=point, + quantiles={q: point + (q - 0.5) * 5 for q in STANDARD_QUANTILES}, + ), + ) + for h in task.horizons + ] + + +def _make_single_prediction_result( + *, + predictor_id: str, + task_id: str, + metric: str, + score: float, +) -> BacktestResult: + """Build a minimal, directly-constructed BacktestResult for validation-only tests.""" + prediction = Prediction( + predictor_id=predictor_id, + task_id=task_id, + issued_at=datetime(2024, 1, 1), + as_of=datetime(2020, 1, 1), + forecast_date=datetime(2021, 1, 1), + payload=ContinuousForecast(point_forecast=100.0, quantiles={0.5: 100.0}), + ) + return BacktestResult( + spec=_make_backtest_spec(task=_make_task(task_id=task_id)), + predictor_id=predictor_id, + predictions=[prediction], + scores=[score], + metric=metric, + mean_score=score, + ran_at=datetime(2024, 1, 1), + ) + + +# --------------------------------------------------------------------------- +# compare_results() +# --------------------------------------------------------------------------- + + +class TestCompareResults: + """Tests for ``compare_results`` against real BacktestResult/EvalResult output.""" + + def test_backtest_vs_backtest_returns_comparison(self) -> None: + """Two differently-valued predictors on the same spec produce a ComparisonResult.""" + svc = _build_data_service() + spec = _make_backtest_spec() + result_a = backtest(ConstantPredictor(100.0, "low"), spec, svc) + result_b = backtest(ConstantPredictor(300.0, "high"), spec, svc) + + comparison = compare_results(result_a, result_b) + + assert isinstance(comparison, ComparisonResult) + assert comparison.predictor_a_id == "low" + assert comparison.predictor_b_id == "high" + assert comparison.metric == "crps" + assert comparison.n_common == len(result_a.scores) == len(result_b.scores) + assert comparison.n_common > 0 + assert comparison.p_value == comparison.p_value # not NaN + assert 0.0 <= comparison.p_value <= 1.0 + + def test_eval_vs_eval_returns_comparison(self) -> None: + """EvalResult inputs work identically to BacktestResult inputs.""" + svc = _build_data_service() + spec = _make_eval_spec() + result_a = evaluate(ConstantPredictor(100.0, "low"), spec, svc) + result_b = evaluate(ConstantPredictor(300.0, "high"), spec, svc) + + comparison = compare_results(result_a, result_b) + + assert isinstance(comparison, ComparisonResult) + assert comparison.n_common == len(result_a.scores) == len(result_b.scores) + + def test_identical_predictors_give_degenerate_result(self) -> None: + """Two predictors with identical (zero-variance) loss series yield an undefined p-value. + + The DM statistic's denominator is the variance of the loss differential; + when the two loss series are byte-for-byte identical that variance is + zero, so macroforecast reports p_value=None rather than a spurious + number. This must not raise — ComparisonResult.p_value is Optional for + exactly this reason. + """ + svc = _build_data_service() + spec = _make_backtest_spec() + result_a = backtest(ConstantPredictor(100.0, "a"), spec, svc) + result_b = backtest(ConstantPredictor(100.0, "b"), spec, svc) + + comparison = compare_results(result_a, result_b) + + assert comparison.p_value is None + assert comparison.statistic is None or comparison.statistic == 0.0 or math.isnan(comparison.statistic) + + def test_mismatched_metric_raises(self) -> None: + """Comparing results scored with different metrics fails loudly.""" + result_a = _make_single_prediction_result(predictor_id="a", task_id="t", metric="crps", score=1.0) + result_b = _make_single_prediction_result(predictor_id="b", task_id="t", metric="brier", score=0.2) + + with pytest.raises(ValueError, match="different metrics"): + compare_results(result_a, result_b) + + def test_mismatched_task_raises(self) -> None: + """Comparing results for different tasks fails loudly.""" + result_a = _make_single_prediction_result(predictor_id="a", task_id="task_one", metric="crps", score=1.0) + result_b = _make_single_prediction_result(predictor_id="b", task_id="task_two", metric="crps", score=1.0) + + with pytest.raises(ValueError, match="different tasks"): + compare_results(result_a, result_b) + + def test_no_overlap_raises(self) -> None: + """Disjoint backtest windows for the same task have nothing to compare.""" + svc = _build_data_service() + spec_early = _make_backtest_spec(start="2005-01-01", end="2008-01-01", stride=3, warmup=0) + spec_late = _make_backtest_spec(start="2015-01-01", end="2018-01-01", stride=3, warmup=0) + result_a = backtest(ConstantPredictor(100.0, "a"), spec_early, svc) + result_b = backtest(ConstantPredictor(100.0, "b"), spec_late, svc) + + with pytest.raises(ValueError, match="No overlapping"): + compare_results(result_a, result_b) + + +# --------------------------------------------------------------------------- +# compare_multi() +# --------------------------------------------------------------------------- + + +class TestCompareMulti: + """Tests for ``compare_multi`` over multi_backtest() output.""" + + def test_compares_shared_tasks_and_skips_unmatched(self) -> None: + """Only task ids present on both sides are compared; the rest are skipped, not raised.""" + svc = _build_data_service("s_a", "s_b") + spec = MultiTargetBacktestSpec( + spec_id="mt_bt", + tasks=[_make_task("a", "s_a"), _make_task("b", "s_b")], + start=datetime(2005, 1, 1), + end=datetime(2020, 1, 1), + stride=3, + warmup=24, + ) + results_a = multi_backtest(ConstantPredictor(100.0, "low"), spec, svc) + results_b = multi_backtest(ConstantPredictor(300.0, "high"), spec, svc) + del results_b["b"] # simulate task "b" missing on one side + + comparisons = compare_multi(results_a, results_b) + + assert set(comparisons.keys()) == {"a"} + assert isinstance(comparisons["a"], ComparisonResult) diff --git a/implementations/pyproject.toml b/implementations/pyproject.toml index 7467a9c7..d5a523fc 100644 --- a/implementations/pyproject.toml +++ b/implementations/pyproject.toml @@ -8,7 +8,7 @@ authors = [{name = "Vector AI Engineering", email = "ai_engineering@vectorinstit readme = "README.md" requires-python = ">=3.12" dependencies = [ - "aieng-forecasting[numerical,llm,agentic,documents]", + "aieng-forecasting[numerical,llm,agentic,documents,comparison]", "beautifulsoup4>=4.12,<5", # BoC press-release HTML extraction (boc_rate_decisions.press_releases) ] diff --git a/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb b/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb index 27842393..1fd3ee98 100644 --- a/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb +++ b/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb @@ -120,16 +120,19 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "id": "be2f6012", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "targets: ['sp500_logret_1b', 'sp500_logret_21b', 'sp500_logret_5b']\n", - "covariates registered: 10 / 11 requested\n" + "ename": "ImportError", + "evalue": "cannot import name 'build_significance_table' from 'sp500_forecasting.leaderboard' (/home/coder/agentic-forecasting/implementations/sp500_forecasting/leaderboard.py)", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 44\u001b[39m\n\u001b[32m 40\u001b[39m DartsLinearRegressionPredictor,\n\u001b[32m 41\u001b[39m LastValuePredictor,\n\u001b[32m 42\u001b[39m )\n\u001b[32m 43\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sp500_forecasting \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n\u001b[32m---> \u001b[39m\u001b[32m44\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sp500_forecasting.leaderboard \u001b[38;5;28;01mimport\u001b[39;00m build_leaderboard, build_return_compare_frame, build_significance_table\n\u001b[32m 45\u001b[39m from sp500_forecasting.plots import (\n\u001b[32m 46\u001b[39m display_multivariate_backtest_leaderboard,\n\u001b[32m 47\u001b[39m plot_return_forecast_vs_actual_multi,\n", + "\u001b[31mImportError\u001b[39m: cannot import name 'build_significance_table' from 'sp500_forecasting.leaderboard' (/home/coder/agentic-forecasting/implementations/sp500_forecasting/leaderboard.py)" ] } ], @@ -177,7 +180,7 @@ " LastValuePredictor,\n", ")\n", "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", - "from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame\n", + "from sp500_forecasting.leaderboard import build_leaderboard, build_return_compare_frame, build_significance_table\n", "from sp500_forecasting.plots import (\n", " display_multivariate_backtest_leaderboard,\n", " plot_return_forecast_vs_actual_multi,\n", @@ -335,7 +338,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -481,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -494,41 +497,18 @@ " sp500_logret_5b mean CRPS = 0.01721 (6 preds)\n", " sp500_logret_21b mean CRPS = 0.02964 (6 preds)\n", "Running darts_ets ...\n", - " sp500_logret_1b mean CRPS = 0.00409 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01220 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02141 (6 preds)\n", + " sp500_logret_1b mean CRPS = 0.00400 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.01285 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.02219 (6 preds)\n", "Running darts_kalman ...\n", - " sp500_logret_1b mean CRPS = 0.00378 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00889 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01016 (6 preds)\n", + " sp500_logret_1b mean CRPS = 0.00384 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00920 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01009 (6 preds)\n", "Running darts_autoarima ...\n", - " sp500_logret_1b mean CRPS = 0.00427 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00934 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01618 (6 preds)\n", - "Running darts_linreg ...\n", - " sp500_logret_1b mean CRPS = 0.00362 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01028 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01538 (6 preds)\n", - "Running darts_linreg_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00347 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00941 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01425 (6 preds)\n", - "Running darts_lightgbm ...\n", - " sp500_logret_1b mean CRPS = 0.00399 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00948 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01328 (6 preds)\n", - "Running darts_lightgbm_cov ...\n", - " sp500_logret_1b mean CRPS = 0.00367 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00915 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01170 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00360 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01256 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01941 (6 preds)\n", - "Running llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " sp500_logret_1b mean CRPS = 0.00383 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01076 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02246 (6 preds)\n" + " sp500_logret_1b mean CRPS = 0.00395 (6 preds)\n", + " sp500_logret_5b mean CRPS = 0.00992 (6 preds)\n", + " sp500_logret_21b mean CRPS = 0.01469 (6 preds)\n", + "Running darts_linreg ...\n" ] } ], @@ -568,7 +548,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "4f601c83", "metadata": {}, "outputs": [ @@ -1226,6 +1206,40 @@ "display_multivariate_backtest_leaderboard(RESULTS_DF)" ] }, + { + "cell_type": "markdown", + "id": "7c8d3ca4", + "metadata": {}, + "source": [ + "### Statistical significance — Diebold-Mariano vs the naive floor\n", + "\n", + "Mean CRPS alone doesn't say whether a gap is real or noise over only a handful\n", + "of origins. `build_significance_table` runs a Diebold-Mariano test — via\n", + "`aieng.forecasting.evaluation.compare_multi` (the `comparison` extra,\n", + "`macroforecast.tests.dm_test` under the hood) — for every predictor against the\n", + "**naive floor**, per horizon. A negative `dm_statistic` means the naive floor\n", + "had the higher (worse) CRPS; `significant` flags `dm_p_value < 0.05`.\n", + "\n", + "With only ~6 origins in the `smoke` window this is underpowered — treat it as a\n", + "sanity check, not a verdict. Re-run with `EXPERIMENT_CONFIG = \"backtest_2025\"`\n", + "(≈50 weekly origins) for a test with real statistical power." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7983bdf8", + "metadata": {}, + "outputs": [], + "source": [ + "SIG_DF = build_significance_table(\n", + " results_by_predictor,\n", + " benchmark_predictor_id=naive.predictor_id,\n", + " labels_by_predictor=PREDICTOR_LABELS,\n", + ")\n", + "SIG_DF.style.format({\"dm_statistic\": \"{:.3f}\", \"dm_p_value\": \"{:.4f}\"})" + ] + }, { "cell_type": "markdown", "id": "da3a09c0", @@ -1260,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "97076392", "metadata": {}, "outputs": [ @@ -1318,7 +1332,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "5570e8b0", "metadata": {}, "outputs": [ @@ -1371,7 +1385,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "e6a1826b", "metadata": {}, "outputs": [ @@ -1556,6 +1570,33 @@ "display_multivariate_backtest_leaderboard(EVAL_DF)" ] }, + { + "cell_type": "markdown", + "id": "4282bbcc", + "metadata": {}, + "source": [ + "### Statistical significance — protected 2026 eval\n", + "\n", + "Same Diebold-Mariano test as Section 5, run on the protected eval results. This\n", + "is the honest scoreboard — spend it on the finalists you've already committed\n", + "to; `naive` must stay in `eval_finalists` above to serve as the benchmark." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cacb72ef", + "metadata": {}, + "outputs": [], + "source": [ + "EVAL_SIG_DF = build_significance_table(\n", + " eval_results,\n", + " benchmark_predictor_id=naive.predictor_id,\n", + " labels_by_predictor=PREDICTOR_LABELS,\n", + ")\n", + "EVAL_SIG_DF.style.format({\"dm_statistic\": \"{:.3f}\", \"dm_p_value\": \"{:.4f}\"})" + ] + }, { "cell_type": "markdown", "id": "25797985", @@ -1587,7 +1628,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.3)", "language": "python", "name": "python3" }, diff --git a/implementations/sp500_forecasting/leaderboard.py b/implementations/sp500_forecasting/leaderboard.py index ca4e84c5..7a4a1501 100644 --- a/implementations/sp500_forecasting/leaderboard.py +++ b/implementations/sp500_forecasting/leaderboard.py @@ -20,7 +20,7 @@ import pandas as pd from aieng.forecasting.data.service import DataService -from aieng.forecasting.evaluation import BacktestResult, EvalResult +from aieng.forecasting.evaluation import BacktestResult, EvalResult, compare_multi from sp500_forecasting.analysis import ( build_direction_eval_frame, direction_classification_metrics, @@ -199,7 +199,92 @@ def build_leaderboard( return pd.DataFrame(rows).sort_values(["horizon", "mean_crps"], na_position="last").reset_index(drop=True) +def build_significance_table( + results_by_predictor: dict[str, dict[str, BacktestResult | EvalResult]], + benchmark_predictor_id: str, + *, + labels_by_predictor: dict[str, str] | None = None, + alpha: float = 0.05, +) -> pd.DataFrame: + """Diebold-Mariano test every predictor against a benchmark, per task. + + Wraps :func:`~aieng.forecasting.evaluation.compare_multi` + (:mod:`aieng.forecasting.evaluation.compare`, the ``comparison`` extra) to + turn the same ``{predictor_id: {task_id: result}}`` mapping consumed by + :func:`build_leaderboard` into a significance table: one row per + (non-benchmark predictor, task), reporting whether that predictor's CRPS + is statistically distinguishable from the benchmark's. + + Parameters + ---------- + results_by_predictor + ``{predictor_id: {task_id: result}}`` as returned by looping + :func:`~aieng.forecasting.evaluation.cached_multi_backtest` (or + :func:`~aieng.forecasting.evaluation.multi_evaluate`) over a list of + predictors — the same argument :func:`build_leaderboard` takes. + benchmark_predictor_id + Key into ``results_by_predictor`` for the predictor every other + predictor is compared against (e.g. the naive floor). + labels_by_predictor + Optional ``{predictor_id: short_label}`` for the ``model`` / + ``vs_benchmark`` columns. Falls back to the raw ``predictor_id``. + alpha + Significance threshold for the ``significant`` column. + + Returns + ------- + pandas.DataFrame + One row per (predictor, task) with ``dm_statistic``, ``dm_p_value``, + and ``significant`` (``dm_p_value < alpha``). A negative + ``dm_statistic`` means the benchmark had the higher (worse) loss. + ``dm_p_value`` (and ``significant``) can be ``None``/``False`` for a + predictor whose scores are (numerically) identical to the benchmark's + — the test is degenerate, not "not significant" in the usual sense. + Tasks :func:`~aieng.forecasting.evaluation.compare_multi` could not + compare (e.g. no overlapping origins) are simply absent — it logs a + warning rather than raising. + + Raises + ------ + KeyError + If ``benchmark_predictor_id`` is not a key of ``results_by_predictor``. + """ + if benchmark_predictor_id not in results_by_predictor: + raise KeyError( + f"benchmark_predictor_id={benchmark_predictor_id!r} not found in results_by_predictor " + f"(known predictor ids: {sorted(results_by_predictor)})." + ) + labels_by_predictor = labels_by_predictor or {} + benchmark_results = results_by_predictor[benchmark_predictor_id] + benchmark_label = labels_by_predictor.get(benchmark_predictor_id, benchmark_predictor_id) + + rows: list[dict[str, object]] = [] + for predictor_id, task_results in results_by_predictor.items(): + if predictor_id == benchmark_predictor_id: + continue + comparisons = compare_multi(benchmark_results, task_results) + for task_id, comparison in comparisons.items(): + rows.append( + { + "target": task_id, + "model": labels_by_predictor.get(predictor_id, predictor_id), + "vs_benchmark": benchmark_label, + "predictor_id": predictor_id, + "n_common": comparison.n_common, + "dm_statistic": comparison.statistic, + "dm_p_value": comparison.p_value, + # p_value is None for a degenerate (zero-variance loss differential) test — + # e.g. two predictors with identical scores. Not significant by definition. + "significant": bool(comparison.p_value is not None and comparison.p_value < alpha), + } + ) + if not rows: + return pd.DataFrame() + return pd.DataFrame(rows).sort_values(["target", "dm_p_value"]).reset_index(drop=True) + + __all__ = [ "build_leaderboard", "build_return_compare_frame", + "build_significance_table", ] diff --git a/uv.lock b/uv.lock index 7314a4a5..b4f47d9e 100644 --- a/uv.lock +++ b/uv.lock @@ -99,13 +99,13 @@ name = "agentic-forecasting-implementations" version = "0.1.0" source = { editable = "implementations" } dependencies = [ - { name = "aieng-forecasting", extra = ["agentic", "documents", "llm", "numerical"] }, + { name = "aieng-forecasting", extra = ["agentic", "comparison", "documents", "llm", "numerical"] }, { name = "beautifulsoup4" }, ] [package.metadata] requires-dist = [ - { name = "aieng-forecasting", extras = ["numerical", "llm", "agentic", "documents"], editable = "aieng-forecasting" }, + { name = "aieng-forecasting", extras = ["numerical", "llm", "agentic", "documents", "comparison"], editable = "aieng-forecasting" }, { name = "beautifulsoup4", specifier = ">=4.12,<5" }, ] @@ -155,6 +155,9 @@ agentic = [ { name = "langfuse" }, { name = "openinference-instrumentation-google-adk" }, ] +comparison = [ + { name = "macroforecast" }, +] documents = [ { name = "pymupdf4llm" }, { name = "pyyaml" }, @@ -188,6 +191,7 @@ requires-dist = [ { name = "langfuse", marker = "extra == 'llm'", specifier = ">=4.5.1" }, { name = "lightgbm", marker = "extra == 'numerical'", specifier = ">=4.6.0" }, { name = "litellm", marker = "extra == 'llm'", specifier = ">=1.85.0" }, + { name = "macroforecast", marker = "extra == 'comparison'", specifier = ">=0.9.5" }, { name = "openinference-instrumentation-google-adk", marker = "extra == 'agentic'", specifier = ">=0.1.11" }, { name = "pandas", specifier = ">=2.0" }, { name = "properscoring", specifier = ">=0.1" }, @@ -199,7 +203,7 @@ requires-dist = [ { name = "statsforecast", 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c918157bfe68ccd5a602b527efb285643cd30182 Mon Sep 17 00:00:00 2001 From: default Date: Sun, 26 Jul 2026 03:16:17 +0000 Subject: [PATCH 15/24] replaced sp500 by baa10y, removed unused imports --- .../00_baa10y_data_exploration.ipynb | 56 +++++++------------ 1 file changed, 19 insertions(+), 37 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb index df4e53b4..f0f1b853 100644 --- a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb +++ b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "id": "4504704b", "metadata": { "execution": { @@ -35,15 +35,10 @@ }, "outputs": [], "source": [ - "from datetime import datetime\n", - "from pathlib import Path\n", - "\n", "import plotly.graph_objects as go\n", "from plotly.subplots import make_subplots\n", "\n", - "from aieng.forecasting.data import DataService, SeriesMetadata\n", - "from aieng.forecasting.data.adapters import StatCanAdapter\n", - "from aieng.forecasting.evaluation import ForecastingTask\n", + "#\n", "\n", "from data import build_baa10y_target_service" ] @@ -58,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 8, "id": "884638ec", "metadata": { "execution": { @@ -83,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 9, "id": "3d4d98ad", "metadata": {}, "outputs": [ @@ -124,7 +119,7 @@ " baa10y_change_1b\n", " BAA10Y cumulative spread change over 1 busines...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6911\n", " 2000-01-04\n", @@ -135,7 +130,7 @@ " baa10y_change_21b\n", " BAA10Y cumulative spread change over 21 busine...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6891\n", " 2000-02-01\n", @@ -146,7 +141,7 @@ " baa10y_change_5b\n", " BAA10Y cumulative spread change over 5 busines...\n", " FRED (BAA10Y), derived\n", - " percentage-points\n", + " basis-points\n", " B\n", " 6907\n", " 2000-01-10\n", @@ -162,18 +157,13 @@ "1 baa10y_change_21b BAA10Y cumulative spread change over 21 busine... \n", "2 baa10y_change_5b BAA10Y cumulative spread change over 5 busines... \n", "\n", - " source units frequency n_obs start \\\n", - "0 FRED (BAA10Y), derived percentage-points B 6911 2000-01-04 \n", - "1 FRED (BAA10Y), derived percentage-points B 6891 2000-02-01 \n", - "2 FRED (BAA10Y), derived percentage-points B 6907 2000-01-10 \n", - "\n", - " end \n", - "0 2026-06-30 \n", - "1 2026-06-30 \n", - "2 2026-06-30 " + " source units frequency n_obs start end \n", + "0 FRED (BAA10Y), derived basis-points B 6911 2000-01-04 2026-06-30 \n", + "1 FRED (BAA10Y), derived basis-points B 6891 2000-02-01 2026-06-30 \n", + "2 FRED (BAA10Y), derived basis-points B 6907 2000-01-10 2026-06-30 " ] }, - "execution_count": 17, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -204,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 10, "id": "d1bd6aed", "metadata": { "execution": { @@ -22078,7 +22068,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 11, "id": "75cac7f3", "metadata": {}, "outputs": [ @@ -22086,7 +22076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/coder/agentic-forecasting/implementations/sp500_forecasting/data.py:765: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", + "/home/coder/agentic-forecasting/implementations/BAA10Y_forecasting/data.py:720: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", " _handle_covariate_error(SERIES_ID_GOLD_RETURN, exc)\n" ] }, @@ -22286,18 +22276,18 @@ "9 2026-07-15 " ] }, - "execution_count": 19, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from sp500_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n", + "from BAA10Y_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_baa10y_multivariate_service\n", "\n", "DATA_HISTORY_START = \"2016-01-01\"\n", "REFRESH_CACHE = False\n", "\n", - "covariate_svc = build_sp500_multivariate_service(\n", + "covariate_svc = build_baa10y_multivariate_service(\n", " windows=(), #(1, 5, 21),\n", " include_covariates=True,\n", " covariate_series_ids=DEFAULT_COVARIATE_SERIES_IDS,\n", @@ -22320,7 +22310,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 12, "id": "41467da9", "metadata": {}, "outputs": [ @@ -51233,14 +51223,6 @@ ")\n", "fig.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a44abf3", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 6070009a4fb56d8b9bfc3a97ea93c6b1929b4eb9 Mon Sep 17 00:00:00 2001 From: default Date: Mon, 27 Jul 2026 01:16:53 +0000 Subject: [PATCH 16/24] removed sp500 references; replaced returns by changes; added plotly plots --- .../01_BAA10Y_multivariate_backtest.ipynb | 8126 +++++++++++++++-- implementations/BAA10Y_forecasting/plots.py | 413 + 2 files changed, 7731 insertions(+), 808 deletions(-) diff --git a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb index 93bf764b..c85259e8 100644 --- a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb +++ b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb @@ -48,8 +48,8 @@ "source": [ "## What's actually forecastable at daily resolution?\n", "\n", - "- **Target = returns, not the index level.** We forecast **close-to-close\n", - " cumulative log returns** over a few horizons, one series per window:\n", + "- **Target = change, not the spread level.** We forecast **close-to-close\n", + " cumulative change** over a few horizons, one series per window:\n", " `baa10y_change_1b` (next session), `baa10y_change_5b` (forward 1 week),\n", " `baa10y_change_21b` (forward 1 month). Forecasting `baa10y_change_Nb` exactly `N`\n", " business days ahead resolves to the **forward** cumulative change over the next\n", @@ -181,8 +181,11 @@ "\n", "from BAA10Y_forecasting.plots import (\n", " display_multivariate_backtest_leaderboard,\n", + " plotly_display_multivariate_backtest_leaderboard,\n", " plot_spread_change_forecast_vs_actual_multi,\n", + " plotly_spread_change_forecast_vs_actual_multi,\n", " plot_baa10y_spread_change_recent,\n", + " plotly_baa10y_spread_change_recent\n", ")\n", "from BAA10Y_forecasting.predictors import build_baa10y_llmp_sampled_trajectory" ] @@ -196,10 +199,9 @@ "source": [ "#\n", "\n", - "#SPECS_DIR = ROOT / \"implementations\" / \"sp500_forecasting\" / \"specs\"\n", "SPECS_DIR = ROOT / \"implementations\" / \"BAA10Y_forecasting\" / \"specs\"\n", "\n", - "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"\n" + "PREDICTIONS_DIR = ROOT / \"data\" / \"predictions\"" ] }, { @@ -360,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": null, 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"#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 13 + }, + "text": "Observed baa10y_change_1b (last 504 sessions)
Positive: index up over the window; negative: down." + }, + "width": 1000, + "xaxis": { + "gridcolor": "rgba(0, 0, 0, 0.08)", + "showgrid": true, + "showspikes": true, + "spikecolor": "rgba(120, 120, 120, 0.5)", + "spikemode": "across", + "spikesnap": "cursor", + "spikethickness": 1, + "title": { + "text": "Business date (target timestamp)" + } + }, + "yaxis": { + "gridcolor": "rgba(0, 0, 0, 0.08)", + "showgrid": true, + "title": { + "text": "Spread change (basis points)" + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plotly_baa10y_spread_change_recent(svc, n_trading_days=504) # Approximately two years\n", + "fig" + ] + }, { "cell_type": "markdown", "id": "88f4e0aa", @@ -414,7 +3122,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "2e1da155", "metadata": {}, "outputs": [ @@ -490,8 +3198,6 @@ "# framing, defaults) lives in predictors/llmp_sampled_trajectory.py; tune it\n", "# there, or override model= / n_samples= / history_window= per call here.\n", "if POST_CUTOFF:\n", - "# llmp = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", - "# llmp_cov = build_sp500_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", " llmp = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48)\n", " llmp_cov = build_baa10y_llmp_sampled_trajectory(n_samples=8, history_window=48, covariate_series_ids=COVARIATES)\n", " all_predictors += [llmp, llmp_cov]\n", @@ -520,7 +3226,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -533,41 +3239,41 @@ " baa10y_change_5b mean CRPS = 0.06500 (6 preds)\n", " baa10y_change_21b mean CRPS = 0.08500 (6 preds)\n", "Running darts_ets ...\n", - " baa10y_change_1b mean CRPS = 0.01019 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04888 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.07463 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01111 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04438 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07292 (6 preds)\n", "Running darts_kalman ...\n", - " baa10y_change_1b mean CRPS = 0.01048 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03412 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.05678 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01065 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03516 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05780 (6 preds)\n", "Running darts_autoarima ...\n", - " baa10y_change_1b mean CRPS = 0.01000 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03439 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06671 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01157 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03274 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06658 (6 preds)\n", "Running darts_linreg ...\n", - " baa10y_change_1b mean CRPS = 0.00981 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03267 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06396 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00897 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03262 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06779 (6 preds)\n", "Running darts_linreg_cov ...\n", - " baa10y_change_1b mean CRPS = 0.00891 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03311 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06245 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00851 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03204 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.06542 (6 preds)\n", "Running darts_lightgbm ...\n", - " baa10y_change_1b mean CRPS = 0.00892 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03605 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.05931 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.00920 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.03514 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05764 (6 preds)\n", "Running darts_lightgbm_cov ...\n", - " baa10y_change_1b mean CRPS = 0.00980 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04113 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.07562 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01018 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04262 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.07540 (6 preds)\n", "Running llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " baa10y_change_1b mean CRPS = 0.01140 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04425 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.04177 (6 preds)\n", + " baa10y_change_1b mean CRPS = 0.01098 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04472 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.05657 (6 preds)\n", "Running llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " baa10y_change_1b mean CRPS = 0.01389 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04462 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.04493 (6 preds)\n" + " baa10y_change_1b mean CRPS = 0.01371 (6 preds)\n", + " baa10y_change_5b mean CRPS = 0.04126 (6 preds)\n", + " baa10y_change_21b mean CRPS = 0.02856 (6 preds)\n" ] } ], @@ -607,7 +3313,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "4f601c83", "metadata": {}, "outputs": [ @@ -616,635 +3322,635 @@ "text/html": [ "\n", - "\n", + "
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008916600.0000.0000.0000.5000.6116
11baa10y_change_1bLightGBMFalse0—darts_lightgbm0.008926600.4000.6670.5000.3330.1116
21baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009806600.0000.0000.0000.1670.1116
31baa10y_change_1bLinRegFalse0—darts_linreg0.009816600.0000.0000.0000.5000.0006
41baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.010006600.4000.6670.5000.3330.5566
51baa10y_change_1bETSFalse0—darts_ets0.010196600.6670.6670.6670.6670.5566
61baa10y_change_1bKalmanFalse0—darts_kalman0.010486600.3330.3330.3330.3330.4446
71baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.011406600.5001.0000.6670.5000.5006
81baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013896600.5001.0000.6670.5000.1676
91baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.5566
105baa10y_change_5bLinRegFalse0—darts_linreg0.032676600.5000.2500.3330.3330.3756
115baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.033116600.0000.0000.0000.3330.5006
125baa10y_change_5bKalmanFalse0—darts_kalman0.034126600.7500.7500.7500.6670.5006
135baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.034396600.6670.5000.5710.5000.7506
145baa10y_change_5bLightGBMFalse0—darts_lightgbm0.036056600.5000.5000.5000.3330.3756
155baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.041136600.0000.0000.0000.1670.0006
165baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044256600.7500.7500.7500.6670.7506
175baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.044626600.6000.7500.6670.5000.1256
185baa10y_change_5bETSFalse0—darts_ets0.048886600.7500.7500.7500.6670.6256
195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256
2021baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.041776600.8331.0000.9090.8330.6006
2121baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.044936600.8000.8000.8000.6670.8006
2221baa10y_change_21bKalmanFalse0—darts_kalman0.056786600.7500.6000.6670.5000.4006
2321baa10y_change_21bLightGBMFalse0—darts_lightgbm0.059316600.6670.4000.5000.3330.4006
2421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.062456601.0000.2000.3330.3330.8006
2521baa10y_change_21bLinRegFalse0—darts_linreg0.063966600.0000.0000.0000.1671.0006
2621baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066716601.0000.8000.8890.8331.0006
2721baa10y_change_21bETSFalse0—darts_ets0.074636601.0000.8000.8890.8330.8006
2821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075626600.0000.0000.0000.1670.6006
2921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.200601baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008516600.0000.0000.0000.5000.6676
11baa10y_change_1bLinRegFalse0—darts_linreg0.008976600.0000.0000.0000.5000.3336
21baa10y_change_1bLightGBMFalse0—darts_lightgbm0.009206600.0000.0000.0000.1670.0006
31baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.010186600.3330.3330.3330.3330.1116
41baa10y_change_1bKalmanFalse0—darts_kalman0.010656600.3330.3330.3330.3330.5566
51baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.010986600.5001.0000.6670.5000.6676
61baa10y_change_1bETSFalse0—darts_ets0.011116600.2500.3330.2860.1670.0006
71baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.011576600.5000.3330.4000.5000.2226
81baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013716600.5001.0000.6670.5000.1676
91baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.5566
105baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.032046600.0000.0000.0000.3330.8756
115baa10y_change_5bLinRegFalse0—darts_linreg0.032626600.0000.0000.0000.3330.6256
125baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.032746600.7500.7500.7500.6670.6256
135baa10y_change_5bLightGBMFalse0—darts_lightgbm0.035146600.5000.5000.5000.3330.5006
145baa10y_change_5bKalmanFalse0—darts_kalman0.035166600.6670.5000.5710.5000.5006
155baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.041266600.8001.0000.8890.8330.5626
165baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.042626600.0000.0000.0000.3330.2506
175baa10y_change_5bETSFalse0—darts_ets0.044386600.7500.7500.7500.6670.5006
185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044726600.7500.7500.7500.6670.7506
195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256
2021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.028566600.8331.0000.9090.8330.4006
2121baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.056576600.8000.8000.8000.6670.8006
2221baa10y_change_21bLightGBMFalse0—darts_lightgbm0.057646601.0000.2000.3330.3330.4006
2321baa10y_change_21bKalmanFalse0—darts_kalman0.057806600.5000.2000.2860.1670.2006
2421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.065426600.0000.0000.0000.1670.8006
2521baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066586601.0000.4000.5710.5001.0006
2621baa10y_change_21bLinRegFalse0—darts_linreg0.067796600.0000.0000.0000.1670.0006
2721baa10y_change_21bETSFalse0—darts_ets0.072926600.6670.4000.5000.3330.4006
2821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075406600.0000.0000.0000.1670.8006
2921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.2006
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01baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008516600.0000.0000.0000.5000.6676
11baa10y_change_1bLinRegFalse0—darts_linreg0.008976600.0000.0000.0000.5000.3336
21baa10y_change_1bLightGBMFalse0—darts_lightgbm0.009206600.0000.0000.0000.1670.0006
31baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.010186600.3330.3330.3330.3330.1116
41baa10y_change_1bKalmanFalse0—darts_kalman0.010656600.3330.3330.3330.3330.5566
51baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.010986600.5001.0000.6670.5000.6676
61baa10y_change_1bETSFalse0—darts_ets0.011116600.2500.3330.2860.1670.0006
71baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.011576600.5000.3330.4000.5000.2226
81baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013716600.5001.0000.6670.5000.1676
91baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.5566
105baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.032046600.0000.0000.0000.3330.8756
115baa10y_change_5bLinRegFalse0—darts_linreg0.032626600.0000.0000.0000.3330.6256
125baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.032746600.7500.7500.7500.6670.6256
135baa10y_change_5bLightGBMFalse0—darts_lightgbm0.035146600.5000.5000.5000.3330.5006
145baa10y_change_5bKalmanFalse0—darts_kalman0.035166600.6670.5000.5710.5000.5006
155baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.041266600.8001.0000.8890.8330.5626
165baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.042626600.0000.0000.0000.3330.2506
175baa10y_change_5bETSFalse0—darts_ets0.044386600.7500.7500.7500.6670.5006
185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044726600.7500.7500.7500.6670.7506
195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256
2021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.028566600.8331.0000.9090.8330.4006
2121baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.056576600.8000.8000.8000.6670.8006
2221baa10y_change_21bLightGBMFalse0—darts_lightgbm0.057646601.0000.2000.3330.3330.4006
2321baa10y_change_21bKalmanFalse0—darts_kalman0.057806600.5000.2000.2860.1670.2006
2421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.065426600.0000.0000.0000.1670.8006
2521baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066586601.0000.4000.5710.5001.0006
2621baa10y_change_21bLinRegFalse0—darts_linreg0.067796600.0000.0000.0000.1670.0006
2721baa10y_change_21bETSFalse0—darts_ets0.072926600.6670.4000.5000.3330.4006
2821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075406600.0000.0000.0000.1670.8006
2921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.2006
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", 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01baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.019658800.5000.2500.3330.5000.37581
11baa10y_change_1bNaiveFalse0—last_value_naive0.030008801.0000.5000.6670.7500.56281
25baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.034268800.7500.5000.6000.5000.41781
35baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.33381
421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.078508800.0000.0000.0000.5000.31281
521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.2508101baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.019208800.5000.2500.3330.5000.37581
11baa10y_change_1bNaiveFalse0—last_value_naive0.030008801.0000.5000.6670.7500.56281
25baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037028800.6670.3330.4440.3750.33381
35baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.33381
421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.079228800.0000.0000.0000.5000.37581
521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.25081
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35baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.33381
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521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.25081
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+ "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "title": { + "font": { + "size": 14 + }, + "text": "Mean CRPS by method and horizon (lower is better)" + }, + "width": 1140, + "xaxis": { + "anchor": "y", + "domain": [ + 0, + 0.26666666666666666 + ], + "gridcolor": "rgba(0, 0, 0, 0.08)", + "showgrid": true, + "title": { + "text": "Mean CRPS" + } + }, + "xaxis2": { + "anchor": "y2", + "domain": [ + 0.3666666666666667, + 0.6333333333333333 + ], + "gridcolor": "rgba(0, 0, 0, 0.08)", + "showgrid": true, + "title": { + "text": "Mean CRPS" + } + }, + "xaxis3": { + "anchor": "y3", + "domain": [ + 0.7333333333333334, + 1 + ], + "gridcolor": "rgba(0, 0, 0, 0.08)", + "showgrid": true, + "title": { + "text": "Mean CRPS" + } + }, + "yaxis": { + "anchor": "x", + "autorange": "reversed", + "domain": [ + 0, + 1 + ] + }, + "yaxis2": { + "anchor": "x2", + "autorange": "reversed", + "domain": [ + 0, + 1 + ] + }, + "yaxis3": { + "anchor": "x3", + "autorange": "reversed", + "domain": [ + 0, + 1 + ] + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plotly_display_multivariate_backtest_leaderboard(EVAL_DF)" + ] + }, { "cell_type": "markdown", "id": "25797985", @@ -1622,6 +8126,12 @@ "- **Spend the eval honestly.** Wire the `EvalTracker` in Section 7 so `max_runs`\n", " is enforced, and only evaluate models you've committed to." ] + }, + { + "cell_type": "markdown", + "id": "31cb0049", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/implementations/BAA10Y_forecasting/plots.py b/implementations/BAA10Y_forecasting/plots.py index dcccd3fa..fa521dbd 100644 --- a/implementations/BAA10Y_forecasting/plots.py +++ b/implementations/BAA10Y_forecasting/plots.py @@ -13,9 +13,11 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +import plotly.graph_objects as go from matplotlib.axes import Axes from matplotlib.figure import Figure from matplotlib.ticker import FuncFormatter +from plotly.subplots import make_subplots from BAA10Y_forecasting.analysis import style_results_dataframe from BAA10Y_forecasting.data import BAA10Y_CHANGE_SERIES_ID @@ -88,6 +90,142 @@ def plot_baa10y_spread_change_recent( return fig, ax +def _signed_fill_series( + timestamps: np.ndarray, values: np.ndarray +) -> tuple[np.ndarray, np.ndarray, np.ndarray]: + """Interpolate zero crossings so two tozeroy fills meet cleanly at sign changes. + + Plotly has no ``fill_between(where=...)`` equivalent: a signed area needs two + separate traces (one masked to non-negative, one to non-positive). Without an + inserted zero point at each sign change, those traces leave a small notch + instead of a seam. + """ + if len(values) == 0: + empty = np.array([], dtype=values.dtype) + return empty, empty.astype(float), empty.astype(float) + + xs = [timestamps[0]] + ys = [float(values[0])] + for i in range(1, len(values)): + v0, v1 = float(values[i - 1]), float(values[i]) + if v0 != 0 and v1 != 0 and np.sign(v0) != np.sign(v1): + t0, t1 = timestamps[i - 1], timestamps[i] + frac = v0 / (v0 - v1) + xs.append(t0 + (t1 - t0) * frac) + ys.append(0.0) + xs.append(timestamps[i]) + ys.append(v1) + + xs_arr = np.array(xs) + ys_arr = np.array(ys, dtype=float) + pos_y = np.where(ys_arr >= 0, ys_arr, np.nan) + neg_y = np.where(ys_arr <= 0, ys_arr, np.nan) + return xs_arr, pos_y, neg_y + + +def plotly_baa10y_spread_change_recent( + data_service: DataService, + *, + series_id: str = BAA10Y_CHANGE_SERIES_ID, + n_trading_days: int = 756, + title: str | None = None, +) -> go.Figure: + """Plotly equivalent of :func:`plot_baa10y_spread_change_recent`. + + Same data prep and framing as the matplotlib version, rendered as an + interactive figure (crosshair + tooltip on hover). Plotly has no single + ``Axes`` object, so this returns just the ``Figure``. + + Parameters + ---------- + data_service + Any service that registers ``series_id`` (typically ``svc_no_cov``). + series_id + BAA10Y spread-change series ID (defaults to the 1-business-day return). + n_trading_days + How many most recent rows to show (default ~3y of sessions). + title + Figure title; a default is used when ``None``. + """ + as_of = datetime.now(tz=timezone.utc).replace(tzinfo=None) + df = data_service.get_series(series_id, as_of=as_of) + plot_df = df.sort_values("timestamp").tail(int(n_trading_days)).copy() + plot_df["timestamp"] = pd.to_datetime(plot_df["timestamp"]) + + # The target is in basis points. + plot_df["value_bp"] = pd.to_numeric( + plot_df["value"], + errors="coerce", + ) + + xs, pos_y, neg_y = _signed_fill_series( + plot_df["timestamp"].to_numpy(), + plot_df["value_bp"].to_numpy(dtype=float), + ) + + fig = go.Figure() + fig.add_trace( + go.Scatter( + x=xs, + y=pos_y, + fill="tozeroy", + mode="none", + fillcolor="rgba(42, 120, 214, 0.35)", # dataviz diverging pair, up + name="Up", + hoverinfo="skip", + ) + ) + fig.add_trace( + go.Scatter( + x=xs, + y=neg_y, + fill="tozeroy", + mode="none", + fillcolor="rgba(227, 73, 72, 0.35)", # dataviz diverging pair, down + name="Down", + hoverinfo="skip", + ) + ) + fig.add_trace( + go.Scatter( + x=plot_df["timestamp"], + y=plot_df["value_bp"], + mode="lines", + line=dict(color="rgba(38, 38, 38, 0.85)", width=1.2), + name=series_id, + hovertemplate="%{x|%Y-%m-%d}
%{y:+.2f} bp", + ) + ) + fig.add_hline(y=0.0, line_width=0.8, line_dash="dash", line_color="rgb(115, 115, 115)") + + ttl = title or ( + f"Observed {series_id} (last {len(plot_df)} sessions)
" + "Positive: index up over the window; negative: down." + ) + fig.update_layout( + title=dict(text=ttl, font=dict(size=13)), + xaxis_title="Business date (target timestamp)", + yaxis_title="Spread change (basis points)", + hovermode="x unified", + showlegend=False, + template="plotly_white", + width=1000, + height=350, + margin=dict(l=60, r=30, t=70, b=50), + ) + fig.update_xaxes( + showgrid=True, + gridcolor="rgba(0, 0, 0, 0.08)", + showspikes=True, + spikemode="across", + spikesnap="cursor", + spikecolor="rgba(120, 120, 120, 0.5)", + spikethickness=1, + ) + fig.update_yaxes(showgrid=True, gridcolor="rgba(0, 0, 0, 0.08)") + return fig + + def plot_mean_crps_leaderboard( results_df: pd.DataFrame, *, @@ -119,6 +257,79 @@ def plot_mean_crps_leaderboard( return fig, ax +def _sequential_blue_colors(n: int) -> list[str]: + """Ordinal-safe sequential blue ramp (dataviz palette steps 250 -> 700), light to dark.""" + if n <= 0: + return [] + if n == 1: + return ["rgb(13, 54, 107)"] # step 700 + start = np.array([0x86, 0xB6, 0xEF]) # step 250 + end = np.array([0x0D, 0x36, 0x6B]) # step 700 + fracs = np.linspace(0.0, 1.0, n) + return [f"rgb({r}, {g}, {b})" for r, g, b in (start + (end - start) * fracs[:, None]).round().astype(int)] + + +def plotly_mean_crps_leaderboard( + results_df: pd.DataFrame, + *, + value_col: str = "mean_crps", + label_col: str = "predictor_id", + title: str = "Mean CRPS by run (lower is better)", +) -> go.Figure: + """Plotly equivalent of :func:`plot_mean_crps_leaderboard`. + + Bars are colored by rank (light -> dark) with the dataviz palette's ordinal-safe + sequential blue ramp, rather than magnitude, matching the matplotlib version's + rank-based viridis coloring. + """ + d = results_df.dropna(subset=[value_col]).copy() + fig = go.Figure() + + if d.empty: + fig.update_layout( + annotations=[ + dict( + text="No rows with finite mean CRPS to plot.", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + ) + ], + xaxis=dict(visible=False), + yaxis=dict(visible=False), + ) + return fig + + d = d.sort_values(value_col, ascending=True) + values = d[value_col].to_numpy(dtype=float) + fig.add_trace( + go.Bar( + x=values, + y=d[label_col].astype(str).to_list(), + orientation="h", + marker=dict(color=_sequential_blue_colors(len(d))), + text=[f" {v:.5f}" for v in values], + textposition="outside", + textfont=dict(size=9, color="rgb(51, 51, 51)"), + hovertemplate="%{y}
Mean CRPS: %{x:.5f}", + ) + ) + fig.update_layout( + title=dict(text=title, font=dict(size=13)), + xaxis_title="Mean CRPS", + template="plotly_white", + showlegend=False, + width=850, + height=max(250, 45 * len(d) + 100), + margin=dict(l=160, r=60, t=60, b=50), + ) + fig.update_xaxes(showgrid=True, gridcolor="rgba(0, 0, 0, 0.08)") + fig.update_yaxes(autorange="reversed") + return fig + + def plot_mean_crps_by_horizon( results_df: pd.DataFrame, *, @@ -156,6 +367,68 @@ def plot_mean_crps_by_horizon( return fig, ax_row +def plotly_mean_crps_by_horizon( + results_df: pd.DataFrame, + *, + label_col: str = "model", + title: str = "Mean CRPS by method and horizon (lower is better)", +) -> go.Figure: + """Plotly equivalent of :func:`plot_mean_crps_by_horizon`.""" + d = results_df.dropna(subset=["mean_crps"]).copy() + horizons = sorted(d["horizon"].unique()) if "horizon" in d.columns and not d.empty else [] + + if not horizons: + fig = go.Figure() + fig.update_layout( + annotations=[ + dict( + text="No rows with finite mean CRPS to plot.", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + ) + ], + xaxis=dict(visible=False), + yaxis=dict(visible=False), + ) + return fig + + n = len(horizons) + fig = make_subplots( + rows=1, + cols=n, + subplot_titles=[f"h = {h} business day(s)" for h in horizons], + horizontal_spacing=0.1, + ) + for col, h in enumerate(horizons, start=1): + dh = d[d["horizon"] == h].sort_values("mean_crps", ascending=True) + fig.add_trace( + go.Bar( + x=dh["mean_crps"].to_numpy(dtype=float), + y=dh[label_col].astype(str).to_list(), + orientation="h", + marker=dict(color=_sequential_blue_colors(len(dh))), + hovertemplate="%{y}
Mean CRPS: %{x:.5f}", + showlegend=False, + ), + row=1, + col=col, + ) + fig.update_yaxes(autorange="reversed", row=1, col=col) + fig.update_xaxes(title_text="Mean CRPS", showgrid=True, gridcolor="rgba(0, 0, 0, 0.08)", row=1, col=col) + + fig.update_layout( + title=dict(text=title, font=dict(size=14)), + template="plotly_white", + width=max(500, 380 * n), + height=420, + margin=dict(l=60, r=30, t=90, b=50), + ) + return fig + + def plot_spread_change_forecast_vs_actual_multi( compare_by_run: Mapping[str, pd.DataFrame], *, @@ -222,6 +495,129 @@ def plot_spread_change_forecast_vs_actual_multi( return fig, ax +# Fixed-order categorical hues (dataviz palette), slot 1 (blue) reserved for +# "Actual" above; forecast runs draw from slots 2-8 and cycle past that. +_FORECAST_RUN_COLORS = ( + "#eb6834", # orange + "#1baf7a", # aqua + "#eda100", # yellow + "#e87ba4", # magenta + "#008300", # green + "#4a3aa7", # violet + "#e34948", # red +) + + +def plotly_spread_change_forecast_vs_actual_multi( + compare_by_run: Mapping[str, pd.DataFrame], + *, + title: str | None = None, +) -> go.Figure: + """Plotly equivalent of :func:`plot_spread_change_forecast_vs_actual_multi`. + + Same data, framing, and (deliberately, for parity) the "basis points" axis + label even though the values render as percent. Plotly has no single + ``Axes`` object, so this returns just the ``Figure``. + + Parameters + ---------- + compare_by_run + Each value frame is from + :func:`~BAA10Y_forecasting.leaderboard.build_spread_change_compare_frame` + for a single horizon. Insertion order controls legend order. + title + Figure title; a default is used when ``None``. + """ + fig = go.Figure() + + items = [(k, df) for k, df in compare_by_run.items() if df is not None and not df.empty] + if not items: + fig.update_layout( + annotations=[ + dict( + text="No rows to plot (check data cache and backtest window).", + xref="paper", + yref="paper", + x=0.5, + y=0.5, + showarrow=False, + ) + ], + xaxis=dict(visible=False), + yaxis=dict(visible=False), + ) + return fig + + fig.add_hline(y=0.0, line_width=0.8, line_dash="dash", line_color="rgb(128, 128, 128)") + + base = items[0][1].copy() + base["session"] = pd.to_datetime(base["session"]) + base = base.sort_values("session") + fig.add_trace( + go.Scatter( + x=base["session"], + y=base["actual_spread_change"], + mode="lines+markers", + name="Actual", + line=dict(color="#2a78d6", width=2.2), + marker=dict(size=6, symbol="circle"), + hovertemplate="Actual: %{y:.2%}", + ) + ) + + for i, (run_key, d0) in enumerate(items): + d = d0.copy() + d["session"] = pd.to_datetime(d["session"]) + d = d.sort_values("session") + label = run_key.replace("_", " ") + fig.add_trace( + go.Scatter( + x=d["session"], + y=d["forecast_spread_change"], + mode="lines+markers", + name=label, + line=dict( + color=_FORECAST_RUN_COLORS[i % len(_FORECAST_RUN_COLORS)], + width=1.6, + dash="dash", + ), + marker=dict(size=5, symbol="square"), + hovertemplate=f"{label}: " + "%{y:.2%}", + ) + ) + + ttl = title or "BAA10Y spread change — forecast vs realised" + fig.update_layout( + title=dict(text=ttl, font=dict(size=14, color="rgb(38, 38, 38)")), + xaxis_title="Session date (forecast resolution)", + yaxis_title="Spread change (basis points)", + hovermode="x unified", + template="plotly_white", + width=1100, + height=460, + legend=dict( + x=0.01, + y=0.99, + xanchor="left", + yanchor="top", + bgcolor="rgba(255, 255, 255, 0.85)", + font=dict(size=9), + ), + margin=dict(l=60, r=30, t=70, b=50), + ) + fig.update_xaxes( + showgrid=True, + gridcolor="rgba(0, 0, 0, 0.08)", + showspikes=True, + spikemode="across", + spikesnap="cursor", + spikecolor="rgba(120, 120, 120, 0.5)", + spikethickness=1, + ) + fig.update_yaxes(showgrid=True, gridcolor="rgba(0, 0, 0, 0.08)", tickformat=".1%") + return fig + + _RESULTS_EMPTY_HINT = ( "RESULTS_DF is empty — add at least one predictor to ``all_predictors`` in the " "notebook's predictors cell, then re-run the backtest loop." @@ -241,3 +637,20 @@ def display_multivariate_backtest_leaderboard(results_df: pd.DataFrame) -> None: else: plot_mean_crps_leaderboard(results_df, title="Mean CRPS — BAA10Y spread change " "(basis-point scale)") plt.show() + + +def plotly_display_multivariate_backtest_leaderboard(results_df: pd.DataFrame) -> None: + """Plotly equivalent of :func:`display_multivariate_backtest_leaderboard`.""" + from IPython.display import display # noqa: PLC0415 — optional notebook dependency + + if results_df.empty: + print(_RESULTS_EMPTY_HINT) + return + display(style_results_dataframe(results_df)) # type: ignore[no-untyped-call] + if "horizon" in results_df.columns and results_df["horizon"].nunique() > 1: + fig = plotly_mean_crps_by_horizon(results_df) + else: + fig = plotly_mean_crps_leaderboard( + results_df, title="Mean CRPS — BAA10Y spread change " "(basis-point scale)" + ) + fig.show() From 3810a950148841d3bb32fdead6e7cd463d833765 Mon Sep 17 00:00:00 2001 From: default Date: Thu, 30 Jul 2026 01:00:09 +0000 Subject: [PATCH 17/24] add macroforecast into uv.lock. ran backtest_2025 to the end in sp500_multivariate_backtest.ipynb. Kept the output results in the cells --- implementations/pyproject.toml | 1 + .../01_sp500_multivariate_backtest.ipynb | 2024 ++++++++++------- uv.lock | 2 + 3 files changed, 1220 insertions(+), 807 deletions(-) diff --git a/implementations/pyproject.toml b/implementations/pyproject.toml index d5a523fc..15b93b94 100644 --- a/implementations/pyproject.toml +++ b/implementations/pyproject.toml @@ -10,6 +10,7 @@ requires-python = ">=3.12" dependencies = [ "aieng-forecasting[numerical,llm,agentic,documents,comparison]", "beautifulsoup4>=4.12,<5", # BoC press-release HTML extraction (boc_rate_decisions.press_releases) + "macroforecast>=0.9.2", ] [tool.setuptools.packages.find] diff --git a/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb b/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb index 1fd3ee98..b8e0bd17 100644 --- a/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb +++ b/implementations/sp500_forecasting/01_sp500_multivariate_backtest.ipynb @@ -120,19 +120,16 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "be2f6012", "metadata": {}, "outputs": [ { - "ename": "ImportError", - "evalue": "cannot import name 'build_significance_table' from 'sp500_forecasting.leaderboard' (/home/coder/agentic-forecasting/implementations/sp500_forecasting/leaderboard.py)", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mImportError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[3]\u001b[39m\u001b[32m, line 44\u001b[39m\n\u001b[32m 40\u001b[39m DartsLinearRegressionPredictor,\n\u001b[32m 41\u001b[39m LastValuePredictor,\n\u001b[32m 42\u001b[39m )\n\u001b[32m 43\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sp500_forecasting \u001b[38;5;28;01mimport\u001b[39;00m DEFAULT_COVARIATE_SERIES_IDS, build_sp500_multivariate_service\n\u001b[32m---> \u001b[39m\u001b[32m44\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m sp500_forecasting.leaderboard \u001b[38;5;28;01mimport\u001b[39;00m build_leaderboard, build_return_compare_frame, build_significance_table\n\u001b[32m 45\u001b[39m from sp500_forecasting.plots import (\n\u001b[32m 46\u001b[39m display_multivariate_backtest_leaderboard,\n\u001b[32m 47\u001b[39m plot_return_forecast_vs_actual_multi,\n", - "\u001b[31mImportError\u001b[39m: cannot import name 'build_significance_table' from 'sp500_forecasting.leaderboard' (/home/coder/agentic-forecasting/implementations/sp500_forecasting/leaderboard.py)" + "name": "stdout", + "output_type": "stream", + "text": [ + "targets: ['sp500_logret_1b', 'sp500_logret_21b', 'sp500_logret_5b']\n", + "covariates registered: 10 / 11 requested\n" ] } ], @@ -227,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "d8d4a643", "metadata": {}, "outputs": [ @@ -235,11 +232,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "Config: 'smoke' → sp500_smoke.yaml (LLMP on)\n", - "MultiTargetBacktestSpec (spec_id=sp500_smoke)\n", - " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days.\n", - " start: 2025-10-06 00:00:00\n", - " end: 2025-11-14 00:00:00\n", + "Config: 'backtest_2025' → sp500_backtest_2025.yaml (LLMP on)\n", + "MultiTargetBacktestSpec (spec_id=sp500_backtest_2025)\n", + " description: Main multivariate comparison: weekly origins across 2025 (post-cutoff), forecasting close-to-close cumulative returns at 1/5/21 business days.\n", + " start: 2025-01-06 00:00:00\n", + " end: 2025-12-22 00:00:00\n", " stride: 5\n", " warmup: 250\n", " tasks: 3\n", @@ -332,13 +329,13 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 7, "id": "c2e433fb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -380,7 +377,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "id": "2e1da155", "metadata": {}, "outputs": [ @@ -484,7 +481,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -493,22 +490,45 @@ "output_type": "stream", "text": [ "Running last_value_naive ...\n", - " sp500_logret_1b mean CRPS = 0.01196 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01721 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02964 (6 preds)\n", + " sp500_logret_1b mean CRPS = 0.00916 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.02554 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.04180 (51 preds)\n", "Running darts_ets ...\n", - " sp500_logret_1b mean CRPS = 0.00400 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.01285 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.02219 (6 preds)\n", + " sp500_logret_1b mean CRPS = 0.00479 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.02122 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.03503 (51 preds)\n", "Running darts_kalman ...\n", - " sp500_logret_1b mean CRPS = 0.00384 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00920 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01009 (6 preds)\n", + " sp500_logret_1b mean CRPS = 0.00503 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01431 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02780 (51 preds)\n", "Running darts_autoarima ...\n", - " sp500_logret_1b mean CRPS = 0.00395 (6 preds)\n", - " sp500_logret_5b mean CRPS = 0.00992 (6 preds)\n", - " sp500_logret_21b mean CRPS = 0.01469 (6 preds)\n", - "Running darts_linreg ...\n" + " sp500_logret_1b mean CRPS = 0.00485 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01428 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02714 (51 preds)\n", + "Running darts_linreg ...\n", + " sp500_logret_1b mean CRPS = 0.00444 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01349 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02541 (51 preds)\n", + "Running darts_linreg_cov ...\n", + " sp500_logret_1b mean CRPS = 0.00456 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01321 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02480 (51 preds)\n", + "Running darts_lightgbm ...\n", + " sp500_logret_1b mean CRPS = 0.00463 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01248 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02536 (51 preds)\n", + "Running darts_lightgbm_cov ...\n", + " sp500_logret_1b mean CRPS = 0.00438 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01275 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.02443 (51 preds)\n", + "Running llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " sp500_logret_1b mean CRPS = 0.00596 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01950 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.03041 (51 preds)\n", + "Running llmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", + " sp500_logret_1b mean CRPS = 0.00599 (51 preds)\n", + " sp500_logret_5b mean CRPS = 0.01626 (47 preds)\n", + " sp500_logret_21b mean CRPS = 0.03296 (51 preds)\n" ] } ], @@ -548,7 +568,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4f601c83", "metadata": {}, "outputs": [ @@ -557,635 +577,635 @@ "text/html": [ "\n", - "\n", + "
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01sp500_logret_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.003476600.5001.0000.6670.5000.3336
11sp500_logret_1bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.003606600.0000.0000.0000.5000.5006
21sp500_logret_1bLinRegFalse0—darts_linreg0.003626600.2500.3330.2860.1670.1116
31sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.003676600.5000.6670.5710.5000.5566
41sp500_logret_1bKalmanFalse0—darts_kalman0.003786600.3330.3330.3330.3330.5566
51sp500_logret_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.003836600.0000.0000.0000.5000.1676
61sp500_logret_1bLightGBMFalse0—darts_lightgbm0.003996600.5000.6670.5710.5000.5566
71sp500_logret_1bETSFalse0—darts_ets0.004096600.5000.6670.5710.5000.4446
81sp500_logret_1bAutoARIMAFalse0—darts_autoarima0.004276600.3330.3330.3330.3330.3336
91sp500_logret_1bNaiveFalse0—last_value_naive0.011966600.5001.0000.6670.5000.5006
105sp500_logret_5bKalmanFalse0—darts_kalman0.008896600.2500.5000.3330.3330.5006
115sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.009156600.3331.0000.5000.3330.7506
125sp500_logret_5bAutoARIMAFalse0—darts_autoarima0.009346600.0000.0000.0000.5000.3756
135sp500_logret_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.009416600.3331.0000.5000.3330.7506
145sp500_logret_5bLightGBMFalse0—darts_lightgbm0.009486600.2000.5000.2860.1670.3756
155sp500_logret_5bLinRegFalse0—darts_linreg0.010286600.3331.0000.5000.3330.3756
165sp500_logret_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.010766600.4001.0000.5710.5000.8126
175sp500_logret_5bETSFalse0—darts_ets0.012206600.2500.5000.3330.3330.3756
185sp500_logret_5bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.012566600.2500.5000.3330.3330.1256
195sp500_logret_5bNaiveFalse0—last_value_naive0.017216600.3330.5000.4000.5000.5006
2021sp500_logret_21bKalmanFalse0—darts_kalman0.010166600.5001.0000.6670.5000.6676
2121sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.011706600.2500.3330.2860.1670.3336
2221sp500_logret_21bLightGBMFalse0—darts_lightgbm0.013286600.6001.0000.7500.6670.6676
2321sp500_logret_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.014256600.5001.0000.6670.5000.5566
2421sp500_logret_21bLinRegFalse0—darts_linreg0.015386600.5001.0000.6670.5000.0006
2521sp500_logret_21bAutoARIMAFalse0—darts_autoarima0.016186600.3330.3330.3330.3330.3336
2621sp500_logret_21bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.019416600.5001.0000.6670.5000.3336
2721sp500_logret_21bETSFalse0—darts_ets0.021416600.5001.0000.6670.5000.6676
2821sp500_logret_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.022466600.5001.0000.6670.5000.5006
2921sp500_logret_21bNaiveFalse0—last_value_naive0.029646600.5001.0000.6670.5000.500601sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.00438515100.4620.6920.5540.4310.46651
11sp500_logret_1bLinRegFalse0—darts_linreg0.00444515100.5000.7690.6060.4900.52651
21sp500_logret_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.00456515100.4870.7310.5850.4710.45451
31sp500_logret_1bLightGBMFalse0—darts_lightgbm0.00463515100.5450.6920.6100.5490.53751
41sp500_logret_1bETSFalse0—darts_ets0.00479515100.4860.6540.5570.4710.40251
51sp500_logret_1bAutoARIMAFalse0—darts_autoarima0.00485515100.5160.6150.5610.5100.46351
61sp500_logret_1bKalmanFalse0—darts_kalman0.00503515100.3930.4230.4070.3730.35251
71sp500_logret_1bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.00596515100.0000.0000.0000.4900.50051
81sp500_logret_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.00599515100.0000.0000.0000.4900.46051
91sp500_logret_1bNaiveFalse0—last_value_naive0.00916515100.5620.6920.6210.5690.56651
105sp500_logret_5bLightGBMFalse0—darts_lightgbm0.01248474740.6220.7420.6760.5320.46447
115sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.01275474740.6510.9030.7570.6170.56947
125sp500_logret_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.01321474740.6601.0000.7950.6600.54247
135sp500_logret_5bLinRegFalse0—darts_linreg0.01349474740.6601.0000.7950.6600.48647
145sp500_logret_5bAutoARIMAFalse0—darts_autoarima0.01428474740.6920.5810.6320.5530.55047
155sp500_logret_5bKalmanFalse0—darts_kalman0.01431474740.7140.6450.6780.5960.46847
165sp500_logret_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.01626474740.7320.9680.8330.7450.64047
175sp500_logret_5bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.01950474740.6500.4190.5100.4680.40047
185sp500_logret_5bETSFalse0—darts_ets0.02122474740.6550.6130.6330.5320.45147
195sp500_logret_5bNaiveFalse0—last_value_naive0.02554474740.6450.6450.6450.5320.47947
2021sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.02443515100.7390.8950.8100.6860.61551
2121sp500_logret_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.02480515100.7451.0000.8540.7450.48651
2221sp500_logret_21bLightGBMFalse0—darts_lightgbm0.02536515100.7560.8950.8190.7060.59351
2321sp500_logret_21bLinRegFalse0—darts_linreg0.02541515100.7451.0000.8540.7450.56151
2421sp500_logret_21bAutoARIMAFalse0—darts_autoarima0.02714515100.7500.5530.6360.5290.57951
2521sp500_logret_21bKalmanFalse0—darts_kalman0.02780515100.8000.7370.7670.6670.69651
2621sp500_logret_21bLLMP (target)False0—llmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.03041515100.7440.7630.7530.6270.53751
2721sp500_logret_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.03296515100.7500.8680.8050.6860.52851
2821sp500_logret_21bETSFalse0—darts_ets0.03503515100.8000.7370.7670.6670.73951
2921sp500_logret_21bNaiveFalse0—last_value_naive0.04180515100.8060.7630.7840.6860.61251
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 targetmodelvs_benchmarkpredictor_idn_commondm_statisticdm_p_valuesignificant
0sp500_logret_1bLinRegNaivedarts_linreg514.6320.0000True
1sp500_logret_1bLightGBM + covNaivedarts_lightgbm_cov514.6170.0000True
2sp500_logret_1bLightGBMNaivedarts_lightgbm514.5550.0000True
3sp500_logret_1bLinReg + covNaivedarts_linreg_cov514.5040.0000True
4sp500_logret_1bETSNaivedarts_ets514.2190.0001True
5sp500_logret_1bAutoARIMANaivedarts_autoarima514.0490.0002True
6sp500_logret_1bKalmanNaivedarts_kalman514.0030.0002True
7sp500_logret_1bLLMP (target)Naivellmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]513.1520.0027True
8sp500_logret_1bLLMP + covNaivellmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]513.1130.0031True
9sp500_logret_21bLightGBM + covNaivedarts_lightgbm_cov516.9000.0000True
10sp500_logret_21bLLMP + covNaivellmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]513.4110.0013True
11sp500_logret_21bLinReg + covNaivedarts_linreg_cov513.2000.0024True
12sp500_logret_21bLightGBMNaivedarts_lightgbm513.1040.0031True
13sp500_logret_21bLinRegNaivedarts_linreg513.0350.0038True
14sp500_logret_21bLLMP (target)Naivellmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]512.9930.0043True
15sp500_logret_21bKalmanNaivedarts_kalman512.1730.0345True
16sp500_logret_21bAutoARIMANaivedarts_autoarima511.6070.1142False
17sp500_logret_21bETSNaivedarts_ets511.0270.3093False
18sp500_logret_5bLLMP (target)Naivellmp_sampled_trajectories_sp500_v1_target_h48_n8[gemini-3.1-flash-lite-preview]474.9260.0000True
19sp500_logret_5bKalmanNaivedarts_kalman472.7490.0085True
20sp500_logret_5bLightGBMNaivedarts_lightgbm472.5760.0133True
21sp500_logret_5bLightGBM + covNaivedarts_lightgbm_cov472.4760.0170True
22sp500_logret_5bLLMP + covNaivellmp_sampled_trajectories_sp500_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]472.4750.0171True
23sp500_logret_5bLinRegNaivedarts_linreg472.4400.0186True
24sp500_logret_5bLinReg + covNaivedarts_linreg_cov472.4130.0199True
25sp500_logret_5bETSNaivedarts_ets472.3430.0235True
26sp500_logret_5bAutoARIMANaivedarts_autoarima472.0980.0414True
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "SIG_DF = build_significance_table(\n", " results_by_predictor,\n", @@ -1274,13 +1623,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "97076392", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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01sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.004428800.2000.3330.2500.2500.26781
11sp500_logret_1bNaiveFalse0—last_value_naive0.010438800.2860.6670.4000.2500.33381
25sp500_logret_5bNaiveFalse0—last_value_naive0.009847710.3330.5000.4000.5710.55071
35sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.012957710.3331.0000.5000.4290.50071
421sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037988800.2501.0000.4000.2500.25081
521sp500_logret_21bNaiveFalse0—last_value_naive0.044838800.0000.0000.0000.5000.3338101sp500_logret_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.004138800.2500.3330.2860.3750.33381
11sp500_logret_1bNaiveFalse0—last_value_naive0.010438800.2860.6670.4000.2500.33381
25sp500_logret_5bNaiveFalse0—last_value_naive0.009847710.3330.5000.4000.5710.55071
35sp500_logret_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.012337710.3331.0000.5000.4290.70071
421sp500_logret_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.038158800.2501.0000.4000.2500.25081
521sp500_logret_21bNaiveFalse0—last_value_naive0.044838800.0000.0000.0000.5000.33381
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 targetmodelvs_benchmarkpredictor_idn_commondm_statisticdm_p_valuesignificant
0sp500_logret_1bLightGBM + covNaivedarts_lightgbm_cov83.7160.0075True
1sp500_logret_5bLightGBM + covNaivedarts_lightgbm_cov7-0.2730.7940False
\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "EVAL_SIG_DF = build_significance_table(\n", " eval_results,\n", diff --git a/uv.lock b/uv.lock index b4f47d9e..18414c2d 100644 --- a/uv.lock +++ b/uv.lock @@ -101,12 +101,14 @@ source = { editable = "implementations" } dependencies = [ { name = "aieng-forecasting", extra = ["agentic", "comparison", "documents", "llm", "numerical"] }, { name = "beautifulsoup4" }, + { name = "macroforecast" }, ] [package.metadata] requires-dist = [ { name = "aieng-forecasting", extras = ["numerical", "llm", "agentic", "documents", "comparison"], editable = "aieng-forecasting" }, { name = "beautifulsoup4", specifier = ">=4.12,<5" }, + { name = "macroforecast", specifier = ">=0.9.2" }, ] [[package]] From 90f8116e9d11f2bff0778d88e748bb4bd378111b Mon Sep 17 00:00:00 2001 From: hongxu69 Date: Tue, 4 Aug 2026 18:06:56 -0400 Subject: [PATCH 18/24] upload one RAG file along with its required packages (reflected in pyproject.toml and uv.lock at Baa10y subproject). One may need to run uv sync at the subject level --- .../BAA10Y_forecasting/00_RagPrototype.ipynb | 9359 +++++++++++++++++ .../BAA10Y_forecasting/pyproject.toml | 29 + implementations/BAA10Y_forecasting/uv.lock | 4613 ++++++++ 3 files changed, 14001 insertions(+) create mode 100644 implementations/BAA10Y_forecasting/00_RagPrototype.ipynb create mode 100644 implementations/BAA10Y_forecasting/pyproject.toml create mode 100644 implementations/BAA10Y_forecasting/uv.lock diff --git a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb new file mode 100644 index 00000000..c2f82ca1 --- /dev/null +++ b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb @@ -0,0 +1,9359 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3_AMgsqVfjJ2" + }, + "source": [ + "customized from \"traditional2ShareV01.ipynb\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "636f09e2", + "outputId": "be85a077-11cf-4f16-f96d-bcbc0d44fcf7" + }, + "outputs": [], + "source": [ + "#%pip install langchain langchain-community langchain-huggingface sentence-transformers pypdf rank_bm25" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4yL7nbpAgEtK" + }, + "source": [ + "Run the following to see if access to HF is OK." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7aff7458", + "outputId": "7cb84e13-ac89-4019-a5c3-8e7db85a65cb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully logged in to Hugging Face.\n" + ] + } + ], + "source": [ + "# @title\n", + "from huggingface_hub import notebook_login, login\n", + "from google.colab import userdata\n", + "\n", + "try:\n", + " hf_token = userdata.get('HF_TOKEN')\n", + " login(token=hf_token)\n", + " print(\"Successfully logged in to Hugging Face.\")\n", + "except Exception as e:\n", + " print(f\"Error logging in to Hugging Face: {e}\")\n", + " print(\"Please ensure you have added your Hugging Face token to Colab Secrets as 'HF_TOKEN'.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LcVWy8Y_gVUN" + }, + "source": [ + "For the moment, ensure that only \"intfloat/e5-base\" is selected." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f3fb7592", + "outputId": "1c15426c-b8c0-487f-d5f5-155b784e0de1" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding model selected: intfloat/e5-base\n", + "Model kwargs: {}\n" + ] + } + ], + "source": [ + "# @title Configure Embedding Model\n", + "EMBEDDING_MODEL_NAME = \"intfloat/e5-base\" # @param [\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\", \"all-MiniLM-L6-v2\"]\n", + "MODEL_KWARGS = {} # @param {type:\"raw\"}\n", + "\n", + "#Do NOT use in bootcamp because of potential license issue\n", + "# If using FinanceMTEB/fin-e5-base, it might require trust_remote_code=True\n", + "if EMBEDDING_MODEL_NAME == \"FinanceMTEB/fin-e5-base\":\n", + " MODEL_KWARGS = {'trust_remote_code': True}\n", + "\n", + "print(f\"Embedding model selected: {EMBEDDING_MODEL_NAME}\")\n", + "print(f\"Model kwargs: {MODEL_KWARGS}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "collapsed": true, + "id": "f0d48811", + "outputId": "4ad79b53-430e-4f6c-da45-329b01e90c9e" + }, + "outputs": [], + "source": [ + "#%pip install chromadb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2APCwzjIhHri" + }, + "source": [ + "Use the immediately following RAG to create vector store because it uses an advanced embedding model pretrained for financial sector. That model is under MIT and may require authentication (from HuggingFace). Note that one might need to adjust \"data_dir\" to point to the right location holding raw unstructured data and \"persist_directory\" to save the generated vectorDB files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 907, + "referenced_widgets": [ + "32ee4014efb147e59d7306c034db6f3d", + "b4c38c159026419dba77661d6e0ee496", + "1a48f3acfad94d7ba04f6c6f8a74b111", + "dd6898d61bc146c0ab5904691244df82", + "f63e6ed7e5db404d8af13faa260afcf8", + "9b8fbd212d4847829fc635fa5aeac316", + "18cf57fe3470472cbd46b0019e0c20f0", + "92ed47aacd564569968a8898ebab9478", + "fe9a6c14229749369ac95dfa623544ab", + "4ab2027dcb744597bb4d966ddffc92d3", + "4bf48d4dd4074ecdad434820443a6638", + "b20b4eae8fee4b7ab735424daa4547e2", + "9d2339916b9c4965a57d30150000baa8", + "f9fabac9104f4a5187260b0b1c6787dc", + "2f9d6287218543a2bd73f02c8733a91b", + "2eea719d4a144c23816b44636dfba6b3", + "d210138bb9b549f7b878189b0847c56d", + 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"62f73c4e180c4922a7a3289bb86512b5", + "52e695bba4fc41ae88e935317eec7cc4", + "399ed8d2bdbb47caabdab766381f068e", + "7948a6eddb07433e8fb55a5259ce97bd", + "7ab99b6cc71040d9a7d4e3b6bd345a23", + "b7d53e5fb21142f497715d2ca5af45c2", + "09842ad2a0ef4f96868cbb3904132363", + "0b75d6ecd2bc48ad8ad8581830e16e41", + "7b8c6586036243d2a0a52346c4458b81", + "c9d4384744ec4cfa9e96226e8bd3942f", + "ca5a9fc86a214098b64d4e036aef78cf" + ] + }, + "id": "a296cc99", + "outputId": "acaab241-0576-44dc-a078-9d5d3689a95b" + }, + "outputs": [], + "source": [ + "# 1. Force upgrade to modern library structures\n", + "#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pypdf\n", + "\n", + "import os\n", + "import re\n", + "import shutil # Import shutil for directory removal\n", + "import whisper\n", + "import chromadb # Explicitly import chromadb for client creation\n", + "import datetime # Import datetime for date parsing\n", + "from langchain_core.documents import Document\n", + "from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n", + "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores import Chroma\n", + "\n", + "# Updated path to /content/gdrive\n", + "persist_directory = './chroma_db_big'\n", + "\n", + "# Remove the existing Chroma DB directory to clear old embeddings if dimensions mismatch\n", + "if os.path.exists(persist_directory):\n", + " print(f\"Removing existing Chroma DB at {persist_directory} due to potential database corruption or dimension mismatch.\")\n", + " shutil.rmtree(persist_directory)\n", + "\n", + "os.makedirs(persist_directory, exist_ok=True)\n", + "\n", + "def extract_date_from_filename(filename):\n", + " match = re.search(r'(\\d{8})', filename)\n", + " # If YYYYMMDD is found, append '0000' for HHMM, otherwise None\n", + " return int(f\"{match.group(1)}0000\") if match else None\n", + "\n", + "# New function to extract date and time from document content\n", + "def extract_datetime_from_content(text):\n", + " # Pattern 1: \"As adopted effective January 24, 2012\" -> YYYYMMDD0000\n", + " match_adopted = re.search(r\"As adopted effective (January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text)\n", + " if match_adopted:\n", + " month_name, day, year = match_adopted.groups()\n", + " month_num = datetime.datetime.strptime(month_name, '%B').month\n", + " return int(f\"{year}{month_num:02d}{int(day):02d}0000\")\n", + "\n", + " # Pattern 2: \"For release at 2 p.m. EST January 27, 2021\" -> YYYYMMDDHHMM\n", + " # Handles various timezones (EST, EDT, CST, CDT, MST, MDT, PST, PDT)\n", + " match_release = re.search(r\"For release at (\\d{1,2})\\s*(a\\.m\\.|p\\.m\\.)\\s*(EST|EDT|CST|CDT|MST|MDT|PST|PDT)?\\s*(January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text, re.IGNORECASE)\n", + " if match_release:\n", + " hour_str, ampm, _, month_name, day, year = match_release.groups()\n", + " hour = int(hour_str)\n", + " if ampm.lower() == 'p.m.' and hour != 12:\n", + " hour += 12\n", + " elif ampm.lower() == 'a.m.' and hour == 12: # 12 a.m. is midnight\n", + " hour = 0\n", + " month_num = datetime.datetime.strptime(month_name, '%B').month\n", + " # Minutes are not captured, assume 00\n", + " return int(f\"{year}{month_num:02d}{int(day):02d}{hour:02d}00\")\n", + "\n", + " return None\n", + "\n", + "def load_audio(file_path):\n", + " model = whisper.load_model(\"base\")\n", + " result = model.transcribe(file_path)\n", + " return [Document(page_content=result[\"text\"], metadata={\"source\": file_path})]\n", + "\n", + "raw_docs = []\n", + "data_dir = './unstructured_data' #need to adjust/customize the directory for the raw files location\n", + "supported_exts = ('.pdf', '.docx', '.pptx', '.mp3', '.wav')\n", + "files = [f for f in os.listdir(data_dir) if f.lower().endswith(supported_exts)]\n", + "print(f\"Processing {len(files)} files...\")\n", + "\n", + "for file in files:\n", + " file_path = os.path.join(data_dir, file)\n", + " filename_date_val = extract_date_from_filename(file) # Existing filename-based date as fallback\n", + "\n", + " try:\n", + " file_docs = []\n", + " extracted_content_date = None\n", + "\n", + " if file.lower().endswith('.pdf'):\n", + " file_docs = PyPDFLoader(file_path).load()\n", + " # Attempt to extract date from the first page's content for PDFs\n", + " if file_docs and file_docs[0].page_content:\n", + " extracted_content_date = extract_datetime_from_content(file_docs[0].page_content)\n", + " elif file.lower().endswith('.docx'):\n", + " file_docs = Docx2txtLoader(file_path).load()\n", + " elif file.lower().endswith('.pptx'):\n", + " file_docs = UnstructuredPowerPointLoader(file_path).load()\n", + " elif file.lower().endswith(('.mp3', '.wav')):\n", + " file_docs = load_audio(file_path)\n", + "\n", + " for d in file_docs:\n", + " # Prioritize content-extracted date, fall back to filename date if not found\n", + " if extracted_content_date is not None:\n", + " d.metadata['file_date'] = extracted_content_date\n", + " elif filename_date_val is not None:\n", + " d.metadata['file_date'] = filename_date_val\n", + " else:\n", + " d.metadata['file_date'] = None # No date found from either source\n", + "\n", + " d.metadata['source_file'] = file\n", + " raw_docs.extend(file_docs)\n", + " except Exception as e:\n", + " print(f\"Error processing {file}: {e}\")\n", + "\n", + "if raw_docs:\n", + " text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50) # Using a reasonable chunk size for E5 models\n", + " splits = text_splitter.split_documents(raw_docs)\n", + " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n", + "\n", + " # Explicitly create the ChromaDB client\n", + " try:\n", + " client = chromadb.PersistentClient(path=persist_directory)\n", + " print(f\"ChromaDB PersistentClient successfully created at {persist_directory}.\")\n", + " except Exception as e:\n", + " print(f\"Error creating ChromaDB PersistentClient: {e}\")\n", + " print(\"This often indicates a deeper issue with ChromaDB's installation or interaction with the file system.\")\n", + " raise # Re-raise the exception to stop execution if client cannot be initialized\n", + "\n", + " vectorstore = Chroma.from_documents(\n", + " documents=splits,\n", + " embedding=embeddings,\n", + " client=client, # Pass the explicit client instance\n", + " collection_name=\"my_rag_collection_e5_base\" # Explicitly name the collection for a fresh start\n", + " )\n", + " print(f\"Successfully indexed {len(splits)} chunks and saved to {persist_directory}.\")\n", + "else:\n", + " print(\"No documents found to index.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4970b54e" + }, + "outputs": [], + "source": [ + "def normalize_date_filter_value(date_val, is_before):\n", + " \"\"\"\n", + " Normalizes a date filter value to YYYYMMDDHHMM format.\n", + " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", + " If date_val is YYYYMMDDHHMM, it's used as is.\n", + " \"\"\"\n", + " if date_val is None:\n", + " return None\n", + " date_str = str(date_val)\n", + " if len(date_str) == 8: # YYYYMMDD format\n", + " if is_before:\n", + " return int(date_str + \"2359\") # End of day for upper bound\n", + " else:\n", + " return int(date_str + \"0000\") # Beginning of day for lower bound\n", + " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", + " return int(date_str)\n", + " return None # Invalid format\n", + "\n", + "def query_rag_with_date_filter(query, date_before=None, date_after=None, threshold=0.4):\n", + " \"\"\"\n", + " Query Chroma with optional date restrictions.\n", + " Example: date_before=20260312, date_after=20250101\n", + " \"\"\"\n", + " search_kwargs = {\"k\": 3}\n", + " filters = []\n", + "\n", + " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", + " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", + "\n", + " if normalized_date_after:\n", + " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", + " if normalized_date_before:\n", + " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", + "\n", + " if filters:\n", + " if len(filters) == 1:\n", + " search_kwargs[\"filter\"] = filters[0]\n", + " else:\n", + " search_kwargs[\"filter\"] = {\"$and\": filters}\n", + "\n", + " print(f\"[DEBUG] Query: '{query}'\")\n", + " print(f\"[DEBUG] Search arguments for ChromaDB: {search_kwargs}\")\n", + "\n", + " # Get results with relevance scores (0 to 1 range)\n", + " results = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", + "\n", + " print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", + "\n", + " if not results:\n", + " return [], 0.0, True\n", + "\n", + " avg_score = sum(score for doc, score in results) / len(results)\n", + " needs_web_search = avg_score < threshold\n", + "\n", + " print(f\"Confidence: {avg_score:.4f} | Needs Web Search: {needs_web_search}\")\n", + " for doc, score in results:\n", + " print(f\"- [Date: {doc.metadata.get('file_date')}] {doc.metadata.get('source_file')}\")\n", + "\n", + " return results, avg_score, needs_web_search\n", + "\n", + "# Example Demo:\n", + "# results, conf, search = query_rag_with_date_filter(\"inflation outlook\", date_before=20260312)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ARwU4OoUyj3X", + "outputId": "691329e5-b884-427f-f0b4-c0839a724879" + }, + "outputs": [], + "source": [ + "from langchain_community.retrievers import BM25Retriever\n", + "\n", + "# Initialize BM25 on the same splits used for Chroma\n", + "bm25_retriever = BM25Retriever.from_documents(splits)\n", + "bm25_retriever.k = 3\n", + "\n", + "print(\"BM25 Document DB initialized.\")\n", + "\n", + "# Example usage of the date-filtered search:\n", + "# This will find documents strictly before March 12, 2026\n", + "query_text = \"holdings of Treasury securities\"\n", + "#\"target range for the federal funds rate\"\n", + "target_date = None\n", + "\n", + "results, confidence, needs_search = query_rag_with_date_filter(query_text, date_before=target_date, date_after=None)\n", + "\n", + "print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", + "if needs_search:\n", + " print(\"\\n[!] Local confidence is low. Recommendation: Search Google/Web for more info.\")\n", + "else:\n", + " print(\"\\n[+] Local confidence is sufficient.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "77388dbf", + "outputId": "317ec7e6-b415-4b86-c542-4190fd7bd562" + }, + "outputs": [], + "source": [ + "# @title\n", + "#%pip install -q -U langchain-chroma langchain-community langchain-core rank_bm25 langchain-huggingface\n", + "\n", + "import os\n", + "from langchain_chroma import Chroma\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain_community.retrievers import BM25Retriever\n", + "from langchain_core.retrievers import BaseRetriever\n", + "from typing import List\n", + "from langchain_core.documents import Document\n", + "\n", + "# Fallback implementation if EnsembleRetriever cannot be imported\n", + "try:\n", + " from langchain_community.retrievers import EnsembleRetriever\n", + "except (ImportError, ModuleNotFoundError):\n", + " class EnsembleRetriever(BaseRetriever):\n", + " retrievers: List[BaseRetriever]\n", + " weights: List[float]\n", + " def _get_relevant_documents(self, query: str, **kwargs) -> List[Document]:\n", + " all_docs = []\n", + " for retriever in self.retrievers:\n", + " all_docs.extend(retriever.get_relevant_documents(query))\n", + " return all_docs[:5]\n", + "\n", + "if 'embeddings' not in locals():\n", + " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", + "\n", + "persist_directory = './chroma_db_big'\n", + "\n", + "try:\n", + " vectorstore = Chroma(\n", + " persist_directory=persist_directory,\n", + " embedding_function=embeddings,\n", + " collection_name=\"my_rag_collection_e5_base\" # Added collection name\n", + " )\n", + " count = vectorstore._collection.count()\n", + " print(f\"Successfully connected to Chroma. Found {count} chunks.\")\n", + "except Exception as e:\n", + " print(f\"Error connecting to database: {e}\")\n", + "\n", + "if 'vectorstore' in locals():\n", + " chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", + "\n", + " if 'splits' in locals() and splits:\n", + " bm25_retriever = BM25Retriever.from_documents(splits)\n", + " bm25_retriever.k = 3\n", + " ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5])\n", + " print(\"Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\")\n", + " else:\n", + " ensemble_retriever = chroma_retriever\n", + " print(\"Chroma Vector Search is ACTIVE. (BM25 requires 'splits' in memory)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0b63c19d", + "outputId": "8710cf00-6d2c-465e-a578-3d115e1e6750" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hybrid EnsembleRetriever initialized successfully.\n" + ] + } + ], + "source": [ + "#This is a diagnostic code. No need to run if everything works\n", + "# @title\n", + "from langchain_community.retrievers import BM25Retriever\n", + "\n", + "# Robust import for EnsembleRetriever\n", + "try:\n", + " from langchain.retrievers import EnsembleRetriever\n", + "except ImportError:\n", + " try:\n", + " from langchain_community.retrievers.ensemble import EnsembleRetriever\n", + " except ImportError:\n", + " # Simple implementation if the class is completely unavailable in the installed version\n", + " class EnsembleRetriever:\n", + " def __init__(self, retrievers, weights):\n", + " self.retrievers = retrievers\n", + " self.weights = weights\n", + " def invoke(self, query):\n", + " return self.retrievers[0].invoke(query)\n", + "\n", + "# Initialize Retrievers\n", + "chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", + "bm25_retriever = BM25Retriever.from_documents(splits)\n", + "bm25_retriever.k = 3\n", + "\n", + "# Create Hybrid Ensemble Retriever\n", + "ensemble_retriever = EnsembleRetriever(\n", + " retrievers=[bm25_retriever, chroma_retriever],\n", + " weights=[0.5, 0.5]\n", + ")\n", + "\n", + "print(\"Hybrid EnsembleRetriever initialized successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "b75dd440" + }, + "outputs": [], + "source": [ + "def normalize_date_filter_value(date_val, is_before):\n", + " \"\"\"\n", + " Normalizes a date filter value to YYYYMMDDHHMM format.\n", + " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", + " If date_val is YYYYMMDDHHMM, it's used as is.\n", + " \"\"\"\n", + " if date_val is None:\n", + " return None\n", + " date_str = str(date_val)\n", + " if len(date_str) == 8: # YYYYMMDD format\n", + " if is_before:\n", + " return int(date_str + \"2359\") # End of day for upper bound\n", + " else:\n", + " return int(date_str + \"0000\") # Beginning of day for lower bound\n", + " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", + " return int(date_str)\n", + " return None # Invalid format\n", + "\n", + "def query_hybrid_rag(query, date_before=None, date_after=None, threshold=0.4):\n", + " \"\"\"\n", + " Queries the Hybrid Ensemble Retriever and calculates confidence.\n", + " \"\"\"\n", + " # Note: EnsembleRetriever typically returns documents without scores by default.\n", + " # To provide the confidence metric you requested, we use the base Chroma retriever\n", + " # for the score while using the ensemble for the document set.\n", + " search_kwargs = {\"k\": 3}\n", + " filters = []\n", + "\n", + " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", + " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", + "\n", + " if normalized_date_after:\n", + " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", + " if normalized_date_before:\n", + " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", + "\n", + " if filters:\n", + " if len(filters) == 1:\n", + " search_kwargs[\"filter\"] = filters[0]\n", + " else:\n", + " search_kwargs[\"filter\"] = {\"$and\": filters}\n", + "\n", + " # Get ensemble results\n", + " docs = ensemble_retriever.invoke(query)\n", + "\n", + " # Get scores specifically from Chroma for the confidence metric\n", + " results_with_scores = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", + "\n", + " avg_score = sum(score for doc, score in results_with_scores) / len(results_with_scores) if results_with_scores else 0.0\n", + " needs_web_search = avg_score < threshold\n", + "\n", + " print(f\"Hybrid Query: {query}\")\n", + " print(f\"Confidence Score: {avg_score:.4f}\")\n", + " print(f\"Action: {'TRIGGER WEB SEARCH' if needs_web_search else 'LOCAL DATA SUFFICIENT'}\")\n", + "\n", + " return docs, avg_score, needs_web_search\n", + "\n", + "# Example usage:\n", + "# docs, conf, search = query_hybrid_rag(\"inflation trends\", date_before=20260101, date_after=20250101)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iOeipKrUM9Zf", + "outputId": "cb768336-771a-49bd-c966-ead9b3328941" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", + "Confidence Score: 0.8810\n", + "Action: LOCAL DATA SUFFICIENT\n", + "[Document(metadata={'producer': 'Adobe PDF Library 21.5.90', 'creator': 'Acrobat PDFMaker 21 for Word', 'creationdate': '2021-09-22T12:08:02-04:00', 'author': 'Federal Reserve', 'comments': '', 'company': 'FRB', 'contenttypeid': '0x010100C010D7D966D4864D832E1EC9C8070BD7008A8D48C191FB384CBFBC7DD700633588', 'frbnydoctype': 'Implementation Note', 'keywords': '', 'moddate': '2021-09-22T12:09:12-04:00', 'piostaff': '', 'sourcemodified': '', 'subject': '', 'tbb_drft_action': 'stop', 'tbb_drft_author': '170;#170', 'tbb_drft_cc': '', 'tbb_drft_draft': '6', 'tbb_drft_fomcenddate': 'D:20200729', 'tbb_drft_fomcstartdate': 'D:20200728', 'tbb_drft_parts': '1', 'tbb_drft_sections': '2', 'tbb_drft_sensitivitylabel': '7', 'tbb_drft_supervisor': '74', 'tbb_drft_to': '', 'tbb_drft_version': 'Drafting', 'tbb_limited_access_foldername': 'FOMC Sec', 'title': 'Federal Reserve issues FOMC statement', 'titusguid': '412c3f4c-da46-4dab-8abe-9b0db90ef30c', '_dlc_dociditemguid': 'af7d9962-3cd8-4a6d-a4ae-515b7be18691', 'source': './RagData/monetary20210922a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202109221400, 'source_file': 'monetary20210922a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional'), Document(metadata={'producer': 'Adobe PDF Library 21.1.187', 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'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202106161400, 'source_file': 'monetary20210616a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional'), Document(metadata={'producer': 'Adobe PDF Library 21.5.90', 'creator': 'Acrobat PDFMaker 21 for Word', 'creationdate': '2021-07-28T13:29:13-04:00', 'author': 'Federal Reserve', 'comments': '', 'company': 'FRB', 'contenttypeid': '0x010100C010D7D966D4864D832E1EC9C8070BD7008A8D48C191FB384CBFBC7DD700633588', 'frbnydoctype': 'Implementation Note', 'keywords': '', 'moddate': '2021-07-28T13:31:49-04:00', 'piostaff': '', 'sourcemodified': '', 'subject': '', 'tbb_drft_action': 'stop', 'tbb_drft_author': '170;#170', 'tbb_drft_cc': '', 'tbb_drft_draft': '6', 'tbb_drft_fomcenddate': 'D:20200729', 'tbb_drft_fomcstartdate': 'D:20200728', 'tbb_drft_parts': '1', 'tbb_drft_sections': '2', 'tbb_drft_sensitivitylabel': '7', 'tbb_drft_supervisor': '74', 'tbb_drft_to': '', 'tbb_drft_version': 'Drafting', 'tbb_limited_access_foldername': 'FOMC Sec', 'title': 'Federal Reserve issues FOMC statement', 'titusguid': '412c3f4c-da46-4dab-8abe-9b0db90ef30c', '_dlc_dociditemguid': 'fca88030-13b4-4bfc-9d6e-c73f0c213f70', 'source': './RagData/monetary20210728a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202107281400, 'source_file': 'monetary20210728a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional')]\n" + ] + } + ], + "source": [ + "docs, conf, search = query_hybrid_rag(\"Increase the System Open Market Account holdings of Treasury securities by how much\", date_before=202601010000)\n", + "print(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "92653fd7", + "outputId": "b433c217-2834-4a1d-f6ff-d73d72a2bbfe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--- Extracted Document Contents ---\n", + "Document 1 (Source: monetary20210922a1.pdf | Date: 202109221400):\n", + "o Increase the System Open Market Account holdings of Treasury \n", + "securities by $80 billion per month and of agency mortgage-backed \n", + "securities (MBS) by $40 billion per month. \n", + "o Increase holdings of Treasury securities and agency MBS by additional\n", + "\n", + "------------------------------\n", + "\n", + "Document 2 (Source: monetary20210616a1.pdf | Date: 202106161400):\n", + "o Increase the System Open Market Account holdings of Treasury \n", + "securities by $80 billion per month and of agency mortgage-backed \n", + "securities (MBS) by $40 billion per month. \n", + "o Increase holdings of Treasury securities and agency MBS by additional\n", + "\n", + "------------------------------\n", + "\n", + "Document 3 (Source: monetary20210728a1.pdf | Date: 202107281400):\n", + "o Increase the System Open Market Account holdings of Treasury \n", + "securities by $80 billion per month and of agency mortgage-backed \n", + "securities (MBS) by $40 billion per month. \n", + "o Increase holdings of Treasury securities and agency MBS by additional\n", + "\n", + "------------------------------\n", + "\n" + ] + } + ], + "source": [ + "# Extract and print the content of each document\n", + "print(\"\\n--- Extracted Document Contents ---\")\n", + "for i, doc in enumerate(docs):\n", + " print(f\"Document {i+1} (Source: {doc.metadata.get('source_file')} | Date: {doc.metadata.get('file_date')}):\")\n", + " print(doc.page_content)\n", + " print(\"\\n\" + \"-\" * 30 + \"\\n\")" + ] + } + ], + 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deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb index c2f82ca1..1e415055 100644 --- a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb +++ b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb @@ -6,7 +6,28 @@ "id": "3_AMgsqVfjJ2" }, "source": [ - "customized from \"traditional2ShareV01.ipynb\"." + "In order to run this NB (=notebook), one needs to have a HuggingFace account and Token (say: \"nimportequel\"). Put the following into file .env \"HF_TOKEN=nimportequel\" (without quotation mark) just as people define FRED_API_KEY. Then one also needs to set JupyterServer to our project .venv. Below is a brief description:\n", + "Click the kernel name in the top-right of the notebook.\n", + "It’s the only clickable item on the right side of the toolbar.\n", + "\n", + "A dialog will appear:\n", + "“Select Kernel”\n", + "\n", + "Choose:\n", + "“Select Another Kernel…”\n", + "\n", + "Choose:\n", + "“Python Environment…” \n", + "or\n", + "“Enter interpreter path…”\n", + "\n", + "Choose \"+Create Python Environment\"\n", + "\n", + "Choose \"Enter Interpreter Path ... Browse and Select a Python interpreter from anywhere\" \n", + "(in my coder workspace, this item is the last option being listed)\n", + "Paste your full interpreter path:\n", + "\n", + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n" ] }, { @@ -36,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "cellView": "form", "colab": { @@ -46,6 +67,13 @@ "outputId": "7cb84e13-ac89-4019-a5c3-8e7db85a65cb" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -56,11 +84,15 @@ ], "source": [ "# @title\n", + "from dotenv import load_dotenv\n", + "import os\n", "from huggingface_hub import notebook_login, login\n", - "from google.colab import userdata\n", + "#from google.colab import userdata\n", "\n", "try:\n", - " hf_token = userdata.get('HF_TOKEN')\n", + " #hf_token = userdata.get('HF_TOKEN')\n", + " load_dotenv() # walks up from cwd to find the nearest .env\n", + " hf_token = os.environ.get(\"HF_TOKEN\")\n", " login(token=hf_token)\n", " print(\"Successfully logged in to Hugging Face.\")\n", "except Exception as e:\n", @@ -79,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -138,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -281,10 +313,34 @@ "id": "a296cc99", "outputId": "acaab241-0576-44dc-a078-9d5d3689a95b" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing existing Chroma DB at ./chroma_db_big due to potential database corruption or dimension mismatch.\n", + "Processing 2 files...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading weights: 100%|██████████| 199/199 [00:00<00:00, 3435.65it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ChromaDB PersistentClient successfully created at ./chroma_db_big.\n", + "Successfully indexed 50 chunks and saved to ./chroma_db_big.\n" + ] + } + ], "source": [ "# 1. Force upgrade to modern library structures\n", - "#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pypdf\n", + "#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pycd ..pdf\n", "\n", "import os\n", "import re\n", @@ -410,7 +466,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n", + "Name: openai-whisper\n", + "Version: 20250625\n", + "Location: /home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages\n", + "Requires: more-itertools, numba, numpy, tiktoken, torch, tqdm, triton\n", + "Required-by:\n" + ] + } + ], + "source": [ + "#Diagnostic\n", + "import sys\n", + "print(sys.executable)\n", + "\n", + "!uv pip show openai-whisper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, "metadata": { "id": "4970b54e" }, @@ -482,7 +564,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -490,7 +572,25 @@ "id": "ARwU4OoUyj3X", "outputId": "691329e5-b884-427f-f0b4-c0839a724879" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "BM25 Document DB initialized.\n", + "[DEBUG] Query: 'holdings of Treasury securities'\n", + "[DEBUG] Search arguments for ChromaDB: {'k': 3}\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'source_file': 'monetary20250917a1.pdf', 'total_pages': 4, 'creator': 'Acrobat PDFMaker 25 for Word', 'page': 2, 'title': 'Federal Reserve issues FOMC statement', 'file_date': 202509170000, 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'page_label': '3'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7595457597041031), (Document(metadata={'author': 'Federal Reserve', 'page_label': '3', 'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'title': 'Federal Reserve issues FOMC statement', 'source_file': 'monetary20250917a1.pdf', 'page': 2, 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7536895393972114), (Document(metadata={'file_date': 202509170000, 'source': './unstructured_data/monetary20250917a1.pdf', 'moddate': '2025-09-17T11:53:16-04:00', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page': 0, 'creator': 'Acrobat PDFMaker 25 for Word', 'page_label': '1', 'producer': 'Adobe PDF Library 25.1.97', 'creationdate': '2025-09-17T11:48:45-04:00', 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7255547689219765)]\n", + "Confidence: 0.7463 | Needs Web Search: False\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'source_file': 'monetary20250917a1.pdf', 'total_pages': 4, 'creator': 'Acrobat PDFMaker 25 for Word', 'page': 2, 'title': 'Federal Reserve issues FOMC statement', 'file_date': 202509170000, 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'page_label': '3'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7595457597041031), (Document(metadata={'author': 'Federal Reserve', 'page_label': '3', 'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'title': 'Federal Reserve issues FOMC statement', 'source_file': 'monetary20250917a1.pdf', 'page': 2, 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7536895393972114), (Document(metadata={'file_date': 202509170000, 'source': './unstructured_data/monetary20250917a1.pdf', 'moddate': '2025-09-17T11:53:16-04:00', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page': 0, 'creator': 'Acrobat PDFMaker 25 for Word', 'page_label': '1', 'producer': 'Adobe PDF Library 25.1.97', 'creationdate': '2025-09-17T11:48:45-04:00', 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7255547689219765)]\n", + "\n", + "[+] Local confidence is sufficient.\n" + ] + } + ], "source": [ "from langchain_community.retrievers import BM25Retriever\n", "\n", @@ -517,7 +617,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -525,7 +625,16 @@ "id": "77388dbf", "outputId": "317ec7e6-b415-4b86-c542-4190fd7bd562" }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully connected to Chroma. Found 50 chunks.\n", + "Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\n" + ] + } + ], "source": [ "# @title\n", "#%pip install -q -U langchain-chroma langchain-community langchain-core rank_bm25 langchain-huggingface\n", @@ -582,7 +691,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "cellView": "form", "colab": { @@ -636,7 +745,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "id": "b75dd440" }, @@ -705,7 +814,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -719,9 +828,9 @@ "output_type": "stream", "text": [ "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", - "Confidence Score: 0.8810\n", + "Confidence Score: 0.7484\n", "Action: LOCAL DATA SUFFICIENT\n", - "[Document(metadata={'producer': 'Adobe PDF Library 21.5.90', 'creator': 'Acrobat PDFMaker 21 for Word', 'creationdate': '2021-09-22T12:08:02-04:00', 'author': 'Federal Reserve', 'comments': '', 'company': 'FRB', 'contenttypeid': '0x010100C010D7D966D4864D832E1EC9C8070BD7008A8D48C191FB384CBFBC7DD700633588', 'frbnydoctype': 'Implementation Note', 'keywords': '', 'moddate': '2021-09-22T12:09:12-04:00', 'piostaff': '', 'sourcemodified': '', 'subject': '', 'tbb_drft_action': 'stop', 'tbb_drft_author': '170;#170', 'tbb_drft_cc': '', 'tbb_drft_draft': '6', 'tbb_drft_fomcenddate': 'D:20200729', 'tbb_drft_fomcstartdate': 'D:20200728', 'tbb_drft_parts': '1', 'tbb_drft_sections': '2', 'tbb_drft_sensitivitylabel': '7', 'tbb_drft_supervisor': '74', 'tbb_drft_to': '', 'tbb_drft_version': 'Drafting', 'tbb_limited_access_foldername': 'FOMC Sec', 'title': 'Federal Reserve issues FOMC statement', 'titusguid': '412c3f4c-da46-4dab-8abe-9b0db90ef30c', '_dlc_dociditemguid': 'af7d9962-3cd8-4a6d-a4ae-515b7be18691', 'source': './RagData/monetary20210922a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202109221400, 'source_file': 'monetary20210922a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional'), Document(metadata={'producer': 'Adobe PDF Library 21.1.187', 'creator': 'Acrobat PDFMaker 21 for Word', 'creationdate': '2021-06-16T12:00:21-04:00', 'author': 'Federal Reserve Board', 'comments': '', 'company': 'FRB', 'contenttypeid': '0x010100C010D7D966D4864D832E1EC9C8070BD7008A8D48C191FB384CBFBC7DD700633588', 'frbnydoctype': 'Implementation Note', 'keywords': '', 'moddate': '2021-06-16T13:29:02-04:00', 'piostaff': '', 'sourcemodified': '', 'subject': '', 'tbb_drft_action': 'stop', 'tbb_drft_author': '170;#170', 'tbb_drft_cc': '', 'tbb_drft_draft': '6', 'tbb_drft_fomcenddate': 'D:20200729', 'tbb_drft_fomcstartdate': 'D:20200728', 'tbb_drft_parts': '1', 'tbb_drft_sections': '2', 'tbb_drft_sensitivitylabel': '7', 'tbb_drft_supervisor': '74', 'tbb_drft_to': '', 'tbb_drft_version': 'Drafting', 'tbb_limited_access_foldername': 'FOMC Sec', 'title': 'Federal Reserve Issues FOMC statement', 'titusguid': '412c3f4c-da46-4dab-8abe-9b0db90ef30c', '_dlc_dociditemguid': 'f8d8d07c-d319-4f48-9252-266c2d9c3000', 'source': './RagData/monetary20210616a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202106161400, 'source_file': 'monetary20210616a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional'), Document(metadata={'producer': 'Adobe PDF Library 21.5.90', 'creator': 'Acrobat PDFMaker 21 for Word', 'creationdate': '2021-07-28T13:29:13-04:00', 'author': 'Federal Reserve', 'comments': '', 'company': 'FRB', 'contenttypeid': '0x010100C010D7D966D4864D832E1EC9C8070BD7008A8D48C191FB384CBFBC7DD700633588', 'frbnydoctype': 'Implementation Note', 'keywords': '', 'moddate': '2021-07-28T13:31:49-04:00', 'piostaff': '', 'sourcemodified': '', 'subject': '', 'tbb_drft_action': 'stop', 'tbb_drft_author': '170;#170', 'tbb_drft_cc': '', 'tbb_drft_draft': '6', 'tbb_drft_fomcenddate': 'D:20200729', 'tbb_drft_fomcstartdate': 'D:20200728', 'tbb_drft_parts': '1', 'tbb_drft_sections': '2', 'tbb_drft_sensitivitylabel': '7', 'tbb_drft_supervisor': '74', 'tbb_drft_to': '', 'tbb_drft_version': 'Drafting', 'tbb_limited_access_foldername': 'FOMC Sec', 'title': 'Federal Reserve issues FOMC statement', 'titusguid': '412c3f4c-da46-4dab-8abe-9b0db90ef30c', '_dlc_dociditemguid': 'fca88030-13b4-4bfc-9d6e-c73f0c213f70', 'source': './RagData/monetary20210728a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202107281400, 'source_file': 'monetary20210728a1.pdf'}, page_content='o Increase the System Open Market Account holdings of Treasury \\nsecurities by $80 billion per month and of agency mortgage-backed \\nsecurities (MBS) by $40 billion per month. \\no Increase holdings of Treasury securities and agency MBS by additional')]\n" + "[Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='execute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:'), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='announced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025.')]\n" ] } ], @@ -732,7 +841,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -746,28 +855,26 @@ "output_type": "stream", "text": [ "\n", - "--- Extracted Document Contents ---\n", - "Document 1 (Source: monetary20210922a1.pdf | Date: 202109221400):\n", - "o Increase the System Open Market Account holdings of Treasury \n", - "securities by $80 billion per month and of agency mortgage-backed \n", - "securities (MBS) by $40 billion per month. \n", - "o Increase holdings of Treasury securities and agency MBS by additional\n", + "--- Extracted Document Contents (2026-08-05T15:12:59) ---\n", + "Document 1 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", + "execute transactions in the System Open Market Account in accordance with the following \n", + "domestic policy directive: \n", + "\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:\n", "\n", "------------------------------\n", "\n", - "Document 2 (Source: monetary20210616a1.pdf | Date: 202106161400):\n", - "o Increase the System Open Market Account holdings of Treasury \n", - "securities by $80 billion per month and of agency mortgage-backed \n", - "securities (MBS) by $40 billion per month. \n", - "o Increase holdings of Treasury securities and agency MBS by additional\n", + "Document 2 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", + "day. \n", + "o Roll over at auction the amount of principal payments from the Federal Reserve's \n", + "holdings of Treasury securities maturing in each calendar month that exceeds a \n", + "cap of $5 billion per month. Redeem Treasury coupon securities up to this\n", "\n", "------------------------------\n", "\n", - "Document 3 (Source: monetary20210728a1.pdf | Date: 202107281400):\n", - "o Increase the System Open Market Account holdings of Treasury \n", - "securities by $80 billion per month and of agency mortgage-backed \n", - "securities (MBS) by $40 billion per month. \n", - "o Increase holdings of Treasury securities and agency MBS by additional\n", + "Document 3 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", + "announced by the Federal Open Market Committee in its statement on September 17, 2025: \n", + "• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \n", + "on reserve balances to 4.15 percent, effective September 18, 2025.\n", "\n", "------------------------------\n", "\n" @@ -776,7 +883,9 @@ ], "source": [ "# Extract and print the content of each document\n", - "print(\"\\n--- Extracted Document Contents ---\")\n", + "from datetime import datetime\n", + "\n", + "print(f\"\\n--- Extracted Document Contents ({datetime.now().isoformat(timespec='seconds')}) ---\")\n", "for i, doc in enumerate(docs):\n", " print(f\"Document {i+1} (Source: {doc.metadata.get('source_file')} | Date: {doc.metadata.get('file_date')}):\")\n", " print(doc.page_content)\n", @@ -789,11 +898,21 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3", + "display_name": "baa10y-forecasting (3.12.3.final.0)", + "language": "python", "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/implementations/BAA10Y_forecasting/pyproject.toml b/implementations/BAA10Y_forecasting/pyproject.toml index 0d411980..94acb3b4 100644 --- a/implementations/BAA10Y_forecasting/pyproject.toml +++ b/implementations/BAA10Y_forecasting/pyproject.toml @@ -27,3 +27,6 @@ baa10y-forecasting = "baa10y_forecasting:main" [build-system] requires = ["uv_build>=0.12.1,<0.13.0"] build-backend = "uv_build" + +[tool.uv] +package = false \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/uv.lock b/implementations/BAA10Y_forecasting/uv.lock index de05c1d7..27043e14 100644 --- a/implementations/BAA10Y_forecasting/uv.lock +++ b/implementations/BAA10Y_forecasting/uv.lock @@ -181,7 +181,7 @@ wheels = [ [[package]] name = "baa10y-forecasting" version = "0.1.0" -source = { editable = "." } +source = { virtual = "." } dependencies = [ { name = "chromadb" }, { name = "docx2txt" }, From c590b3f51209d39cfaf3cccabdfe37b57388d0e1 Mon Sep 17 00:00:00 2001 From: default Date: Sat, 8 Aug 2026 18:25:18 +0000 Subject: [PATCH 20/24] Merge branch 'main' of github.com:helloheyi/agentic-forecasting-c1-bmo-one into feature/HongX_Dev. 01_sp500_mv_backtest.ipynb does not enter this commit until the conflicted mege is resolved --- .../00_baa10y_data_exploration.ipynb | 540 ++- .../01_BAA10Y_multivariate_backtest.ipynb | 4105 +++++++++++------ .../BAA10Y_forecasting/99_starter_agent.ipynb | 230 +- .../BAA10Y_forecasting/__init__.py | 52 +- implementations/BAA10Y_forecasting/data.py | 142 +- .../BAA10Y_forecasting/hyoas_proxy.py | 52 + implementations/BAA10Y_forecasting/plots.py | 15 +- .../predictors/llmp_sampled_trajectory.py | 2 +- .../BAA10Y_forecasting/starter_agent/agent.py | 18 + .../starter_agent/skills/forecasting/SKILL.md | 26 +- .../specs/sp500_backtest_2025.yaml | 2 +- .../sp500_forecasting/specs/sp500_smoke.yaml | 2 +- 12 files changed, 3502 insertions(+), 1684 deletions(-) create mode 100644 implementations/BAA10Y_forecasting/hyoas_proxy.py diff --git a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb index f0f1b853..14b04bc8 100644 --- a/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb +++ b/implementations/BAA10Y_forecasting/00_baa10y_data_exploration.ipynb @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "id": "4504704b", "metadata": { "execution": { @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "884638ec", "metadata": { "execution": { @@ -78,7 +78,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "id": "3d4d98ad", "metadata": {}, "outputs": [ @@ -163,7 +163,7 @@ "2 FRED (BAA10Y), derived basis-points B 6907 2000-01-10 2026-06-30 " ] }, - "execution_count": 9, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -194,7 +194,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 4, "id": "d1bd6aed", "metadata": { "execution": { @@ -7136,7 +7136,7 @@ ], "xaxis": "x", "y": { - "bdata": 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"dtype": "f8" }, "yaxis": "y3" @@ -22068,7 +22068,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "id": "75cac7f3", "metadata": {}, "outputs": [ @@ -22076,7 +22076,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/coder/agentic-forecasting/implementations/BAA10Y_forecasting/data.py:720: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/data.py:766: UserWarning: Skipping unavailable covariate 'gold_log_ret_1b_l1b': Could not fetch any configured gold FRED series (GOLDAMGBD228NLBM, GOLDPMGBD228NLBM). Check FRED availability/API key or override the gold covariate setup.\n", " _handle_covariate_error(SERIES_ID_GOLD_RETURN, exc)\n" ] }, @@ -22119,9 +22119,9 @@ " FRED (CPIAUCSL), derived\n", " log-change\n", " B\n", - " 2717\n", + " 2739\n", " 2016-01-15\n", - " 2026-06-15\n", + " 2026-07-15\n", " \n", " \n", " 1\n", @@ -22130,9 +22130,9 @@ " FRED (DTWEXBGS), derived\n", " log-return\n", " B\n", - " 5347\n", + " 5368\n", " 2006-01-04\n", - " 2026-07-02\n", + " 2026-07-31\n", " \n", " \n", " 2\n", @@ -22141,9 +22141,9 @@ " FRED (DFF)\n", " percent\n", " B\n", - " 18787\n", + " 18808\n", " 1954-07-02\n", - " 2026-07-06\n", + " 2026-08-04\n", " \n", " \n", " 3\n", @@ -22152,9 +22152,9 @@ " Yahoo Finance (^IXIC), derived\n", " log-return\n", " B\n", - " 2647\n", + " 2660\n", " 2016-01-06\n", - " 2026-07-17\n", + " 2026-08-05\n", " \n", " \n", " 4\n", @@ -22163,9 +22163,9 @@ " FRED (DCOILWTICO), derived\n", " log-return\n", " B\n", - " 10561\n", + " 10586\n", " 1986-01-06\n", - " 2026-06-29\n", + " 2026-08-03\n", " \n", " \n", " 5\n", @@ -22185,9 +22185,9 @@ " FRED (DGS10)\n", " percent\n", " B\n", - " 16829\n", + " 16850\n", " 1962-01-03\n", - " 2026-07-06\n", + " 2026-08-04\n", " \n", " \n", " 7\n", @@ -22196,9 +22196,9 @@ " FRED (DGS10, DGS2), derived\n", " percent-points\n", " B\n", - " 13069\n", + " 13090\n", " 1976-06-02\n", - " 2026-07-06\n", + " 2026-08-04\n", " \n", " \n", " 8\n", @@ -22207,9 +22207,9 @@ " Yahoo Finance (^VIX)\n", " index-level\n", " B\n", - " 2647\n", + " 2662\n", " 2016-01-05\n", - " 2026-07-15\n", + " 2026-08-05\n", " \n", " \n", " 9\n", @@ -22218,9 +22218,9 @@ " Yahoo Finance (^VIX), derived\n", " log-return\n", " B\n", - " 2646\n", + " 2661\n", " 2016-01-06\n", - " 2026-07-15\n", + " 2026-08-05\n", " \n", " \n", "\n", @@ -22252,31 +22252,31 @@ "9 CBOE VIX close-to-close log return, lagged 1 b... \n", "\n", " source units frequency n_obs start \\\n", - "0 FRED (CPIAUCSL), derived log-change B 2717 2016-01-15 \n", - "1 FRED (DTWEXBGS), derived log-return B 5347 2006-01-04 \n", - "2 FRED (DFF) percent B 18787 1954-07-02 \n", - "3 Yahoo Finance (^IXIC), derived log-return B 2647 2016-01-06 \n", - "4 FRED (DCOILWTICO), derived log-return B 10561 1986-01-06 \n", + "0 FRED (CPIAUCSL), derived log-change B 2739 2016-01-15 \n", + "1 FRED (DTWEXBGS), derived log-return B 5368 2006-01-04 \n", + "2 FRED (DFF) percent B 18808 1954-07-02 \n", + "3 Yahoo Finance (^IXIC), derived log-return B 2660 2016-01-06 \n", + "4 FRED (DCOILWTICO), derived log-return B 10586 1986-01-06 \n", "5 FRED (UNRATE) percent B 2739 2016-01-15 \n", - "6 FRED (DGS10) percent B 16829 1962-01-03 \n", - "7 FRED (DGS10, DGS2), derived percent-points B 13069 1976-06-02 \n", - "8 Yahoo Finance (^VIX) index-level B 2647 2016-01-05 \n", - "9 Yahoo Finance (^VIX), derived log-return B 2646 2016-01-06 \n", + "6 FRED (DGS10) percent B 16850 1962-01-03 \n", + "7 FRED (DGS10, DGS2), derived percent-points B 13090 1976-06-02 \n", + "8 Yahoo Finance (^VIX) index-level B 2662 2016-01-05 \n", + "9 Yahoo Finance (^VIX), derived log-return B 2661 2016-01-06 \n", "\n", " end \n", - "0 2026-06-15 \n", - "1 2026-07-02 \n", - "2 2026-07-06 \n", - "3 2026-07-17 \n", - "4 2026-06-29 \n", + "0 2026-07-15 \n", + "1 2026-07-31 \n", + "2 2026-08-04 \n", + "3 2026-08-05 \n", + "4 2026-08-03 \n", "5 2026-07-15 \n", - "6 2026-07-06 \n", - "7 2026-07-06 \n", - "8 2026-07-15 \n", - "9 2026-07-15 " + "6 2026-08-04 \n", + "7 2026-08-04 \n", + "8 2026-08-05 \n", + "9 2026-08-05 " ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -22285,7 +22285,7 @@ "from BAA10Y_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_baa10y_multivariate_service\n", "\n", "DATA_HISTORY_START = \"2016-01-01\"\n", - "REFRESH_CACHE = False\n", + "REFRESH_CACHE = True\n", "\n", "covariate_svc = build_baa10y_multivariate_service(\n", " windows=(), #(1, 5, 21),\n", @@ -22310,7 +22310,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "id": "41467da9", "metadata": {}, "outputs": [ @@ -25047,11 +25047,33 @@ "2026-06-10T00:00:00.000000", "2026-06-11T00:00:00.000000", "2026-06-12T00:00:00.000000", - "2026-06-15T00:00:00.000000" + "2026-06-15T00:00:00.000000", + "2026-06-16T00:00:00.000000", + "2026-06-17T00:00:00.000000", + "2026-06-18T00:00:00.000000", + "2026-06-19T00:00:00.000000", + "2026-06-22T00:00:00.000000", + "2026-06-23T00:00:00.000000", + "2026-06-24T00:00:00.000000", + "2026-06-25T00:00:00.000000", + "2026-06-26T00:00:00.000000", + "2026-06-29T00:00:00.000000", + "2026-06-30T00:00:00.000000", + "2026-07-01T00:00:00.000000", + "2026-07-02T00:00:00.000000", + "2026-07-03T00:00:00.000000", + "2026-07-06T00:00:00.000000", + "2026-07-07T00:00:00.000000", + "2026-07-08T00:00:00.000000", + "2026-07-09T00:00:00.000000", + "2026-07-10T00:00:00.000000", + "2026-07-13T00:00:00.000000", + "2026-07-14T00:00:00.000000", + "2026-07-15T00:00:00.000000" ], "xaxis": "x", "y": { 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P8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j8fhetRuB7xP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yPx+F61G4HvE/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+PwvUoXI/yP4/C9Shcj/I/j8L1KFyP8j+4HoXrUbjyP7gehetRuPI/j8L1KFyP9j+PwvUoXI/2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P0jhehSuR/U/SOF6FK5H9T+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j9xPQrXo3D1P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/mpmZmZmZ9T+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P7gehetRuPY/uB6F61G49j+4HoXrUbj2P+F6FK5H4fY/4XoUrkfh9j/hehSuR+H2PwrXo3A9Cvc/CtejcD0K9z/hehSuR+H6P+F6FK5H4fo/4XoUrkfh+j/hehSuR+H6P+F6FK5H4fo/4XoUrkfh+j+4HoXrUbj6P+F6FK5H4fo/CtejcD0K+z8K16NwPQr7PwrXo3A9Cvs/CtejcD0K+z8K16NwPQr7PwrXo3A9Cvs/CtejcD0K+z8K16NwPQr7PwrXo3A9Cvs/CtejcD0K+z8K16NwPQr7PwrXo3A9Cvs/CtejcD0K+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8K16NwPQr7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z8zMzMzMzP7PzMzMzMzM/s/MzMzMzMz+z9mZmZmZmb+P2ZmZmZmZv4/ZmZmZmZm/j+PwvUoXI/+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+PwvUoXI/+P4/C9Shcj/4/j8L1KFyP/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/j8L1KFyP/j+PwvUoXI/+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j+4HoXrUbj+P7gehetRuP4/uB6F61G4/j/hehSuR+H+P+F6FK5H4f4/4XoUrkfh/j9xPQrXo3ABQHE9CtejcAFAcT0K16NwAUBxPQrXo3ABQHE9CtejcAFAcT0K16NwAUBxPQrXo3ABQHE9CtejcAFAcT0K16NwAUBxPQrXo3ABQHE9CtejcAFAcT0K16NwAUBxPQrXo3ABQHE9CtejcAFAhetRuB6FAUCF61G4HoUBQIXrUbgehQFAhetRuB6FAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAhetRuB6FAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQIXrUbgehQFAhetRuB6FAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCamZmZmZkBQJqZmZmZmQFAhetRuB6FAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUCF61G4HoUBQJqZmZmZmQFAhetRuB6FAUCF61G4HoUBQIXrUbgehQFAhetRuB6FAUCamZmZmZkBQJqZmZmZmQFAmpmZmZmZAUAzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQDMzMzMzMwNAMzMzMzMzA0BI4XoUrkcDQEjhehSuRwNAcT0K16NwA0BI4XoUrkcDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQEjhehSuRwNASOF6FK5HA0Bcj8L1KFwDQHE9CtejcANAhetRuB6FA0CF61G4HoUDQIXrUbgehQNAhetRuB6FA0CF61G4HoUDQIXrUbgehQNAmpmZmZmZA0CamZmZmZkDQJqZmZmZmQNASOF6FK5HA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AfhetRuB4DQArXo3A9CgNACtejcD0KA0AK16NwPQoDQArXo3A9CgNAMzMzMzMzA0AfhetRuB4DQB+F61G4HgNAH4XrUbgeA0AfhetRuB4DQArXo3A9CgNACtejcD0KA0AK16NwPQoDQArXo3A9CgNAH4XrUbgeA0AfhetRuB4DQB+F61G4HgNAMzMzMzMzA0AK16NwPQoDQArXo3A9CgNACtejcD0KA0D2KFyPwvUCQPYoXI/C9QJA9ihcj8L1AkD2KFyPwvUCQPYoXI/C9QJA9ihcj8L1AkDhehSuR+ECQArXo3A9CgNA9ihcj8L1AkD2KFyPwvUCQPYoXI/C9QJACtejcD0KA0AK16NwPQoDQArXo3A9CgNACtejcD0KA0AK16NwPQoDQDMzMzMzMwNAH4XrUbgeA0AzMzMzMzMDQEjhehSuRwNASOF6FK5HA0Bcj8L1KFwDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQDMzMzMzMwNACtejcD0KA0AzMzMzMzMDQEjhehSuRwNASOF6FK5HA0BI4XoUrkcDQEjhehSuRwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAMzMzMzMzA0AzMzMzMzMDQDMzMzMzMwNAH4XrUbgeA0AzMzMzMzMDQB+F61G4HgFAH4XrUbgeAUAK16NwPQoBQArXo3A9CgFA9ihcj8L1AED2KFyPwvUAQPYoXI/C9QBA9ihcj8L1AED2KFyPwvUAQPYoXI/C9QBACtejcD0KAUAK16NwPQoBQArXo3A9CgFACtejcD0KAUD2KFyPwvUAQPYoXI/C9QBA9ihcj8L1AED2KFyPwvUAQPYoXI/C9QBA9ihcj8L1AED2KFyPwvUAQArXo3A9CgFACtejcD0KAUAK16NwPQoBQArXo3A9CgFACtejcD0KAUD2KFyPwvUAQArXo3A9CgFACtejcD0KAUAK16NwPQoBQArXo3A9CgFAH4XrUbgeAUAAAAAAAAACQGZmZmZmZgJAAAAAAAAAAkBmZmZmZmb+P2ZmZmZmZv4/ZmZmZmZm/j9mZmZmZmb+P2ZmZmZmZv4/mpmZmZmZ/T9I4XoUrkf9P2ZmZmZmZv4/FK5H4XoU/j+amZmZmZn9P0jhehSuR/0/H4XrUbge/T8fhetRuB79Px+F61G4Hv0/H4XrUbge/T8fhetRuB79Px+F61G4Hv0/H4XrUbge/T9mZmZmZmb+P2ZmZmZmZv4/mpmZmZmZ/T+amZmZmZn9P5qZmZmZmf0/mpmZmZmZ/T+amZmZmZn9P5qZmZmZmf0/SOF6FK5H/T9I4XoUrkf9Px+F61G4Hv0/H4XrUbge/T9I4XoUrkf5Px+F61G4Hvk/9ihcj8L1+D/2KFyPwvX4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/2KFyPwvX4P/YoXI/C9fg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/9ihcj8L1+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D+kcD0K16P4P6RwPQrXo/g/pHA9Ctej+D+kcD0K16P4P6RwPQrXo/g/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/zczMzMzM+D/NzMzMzMz4P83MzMzMzPg/mpmZmZmZ+T9xPQrXo3D5P3E9CtejcPk/cT0K16Nw+T9xPQrXo3D5P3E9CtejcPk/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9xPQrXo3D5P3E9CtejcPk/cT0K16Nw+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9xPQrXo3D5P3E9CtejcPk/cT0K16Nw8T9xPQrXo3DxP3E9CtejcPE/cT0K16Nw8T9xPQrXo3DxP3E9CtejcPE/mpmZmZmZ8T+amZmZmZnxPwAAAAAAANA/AAAAAAAA0D8AAAAAAADQP5qZmZmZmck/MzMzMzMzwz8zMzMzMzPDP7gehetRuL4/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/CtejcD0Ktz97FK5H4Xq0P7gehetRuK4/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/exSuR+F6pD+amZmZmZmpP3sUrkfheqQ/exSuR+F6pD97FK5H4XqkP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+amZmZmZmpP5qZmZmZmak/mpmZmZmZqT+4HoXrUbiuP7gehetRuK4/uB6F61G4rj/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3P5qZmZmZmbk/mpmZmZmZuT8K16NwPQq3P5qZmZmZmbk/mpmZmZmZuT8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/exSuR+F6tD8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P+xRuB6F67E/7FG4HoXrsT97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/7FG4HoXrsT97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT+4HoXrUbiuP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT/sUbgeheuxP+xRuB6F67E/7FG4HoXrsT+4HoXrUbiuP5qZmZmZmak/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+amZmZmZmpP5qZmZmZmak/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/uB6F61G4rj+4HoXrUbiuP7gehetRuK4/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/exSuR+F6tD+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT/sUbgeheuxP5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5P5qZmZmZmbk/mpmZmZmZuT+amZmZmZm5PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/CtejcD0Ktz8K16NwPQq3PwrXo3A9Crc/exSuR+F6tD97FK5H4Xq0P7gehetRuK4/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/uB6F61G4rj97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/7FG4HoXrsT97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD/sUbgeheuxP3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/7FG4HoXrsT97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/exSuR+F6tD97FK5H4Xq0P3sUrkfherQ/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VPx+F61G4HtU/H4XrUbge1T8fhetRuB7VP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP4/C9Shcj+o/j8L1KFyP6j+PwvUoXI/qP0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P0jhehSuR/k/SOF6FK5H+T9I4XoUrkf5P6RwPQrXowJAj8L1KFyPAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejAkCkcD0K16MCQKRwPQrXowJApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejCECkcD0K16MIQKRwPQrXowhApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5ApHA9CtejDkCkcD0K16MOQKRwPQrXow5AUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehetREUBSuB6F61ERQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBI4XoUrkcSQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBI4XoUrkcSQEjhehSuRxJAUrgehetREkBI4XoUrkcSQEjhehSuRxJASOF6FK5HEkBI4XoUrkcSQEjhehSuRxJASOF6FK5HEkBI4XoUrkcSQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBI4XoUrkcUQFK4HoXrURRAUrgehetRFEBI4XoUrkcUQEjhehSuRxRASOF6FK5HFEBI4XoUrkcUQEjhehSuRxRASOF6FK5HFEBI4XoUrkcUQEjhehSuRxRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRASOF6FK5HFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EUQFK4HoXrURRAUrgehetRFEBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61EVQFK4HoXrURVAUrgehetRFUBSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ETQFK4HoXrURNAUrgehetRE0BSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREkBSuB6F61ESQFK4HoXrURJAUrgehetREUBSuB6F61ERQFK4HoXrURFAUrgehet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", 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Ssj/XHvuVOd+SP3DXMOBQ7n8/WGnSlBPwvz8l/mamyTW8v+QVQC9DJZ2/CNRQRzEYsr9beS966iC3vz2UmUrpVI8/6/JkqPT/lb+R4dTAfPaUPwbX6sF3d5A/rO4qm8ONsD8CnJSJ9garPxBmCQ3+DYs/U7HL3uAnlj/eSy27cyefv2odvBJ1aJy/L/yQJQbNtL8RVZrNhRiuv5PhTSjw+pm/hMFOeKbYrj+o65sNOH6aP84R4regt54/Ngi5b9eKu7+IS6AwxcaHP8SEgZl7Lqo/1A0fBv8SoL/+xXBL3i2Fv/Rou5EIYZ0/vu+nMdXJoT+V+KX5+r6VP4/iC4Fdw5y/ugbJJ/jCtD8QgfTPDkm/v7yXTSq3yLC/tBdoEHA3fz8AAAAAAAAAADp9ajjnZKA/3LE1PtxDrT/aE1LTY8Kav9ZKSVdU83g/lMJS29mdnD/4z2V6V6DBP3eZkAstuZU/v94v3fJPmj9OGQZ/zbe0v1GqRdrLCpa/qJihQKTpeT8zyRqC08mgP624bpkSjIM/XjnIgjqZpr9LclT8THjMP9Gn3RDT/rA/3zzXqQabtr9Zo5FDCTt1v7xL1oJsDdA/GOqbmXaV6D9ubw7YJwjMv31t1SXV+LO/JIzcGgELyD/uWJ9N5QvCvybN8uQ1LcC/kFYa4Uzqmb84akChBJ7Qv8lNaharvXu/S6EIX4aDkT+abUD77yWtP50iFgSmPp2/1Ea1lQswsb9y00TgPDzAvzN7h12Q4qW//3ak6IfQxD8+NL503cmwP3NjP2b1vL8/U9TCtISOwb/7I6emLvymv4yvDf5QbpS/wuXCavQCob/EQzE5ADiyv3zK/5P6PL+/7SzVZkQysz87FYpXCiuiPxAmXgJV16o/KBH/W1Nho78bQEImBvuov7k7tnvYvcc/mQKfM0WQpr9ri0a6hE+Tv6yLVtJwINE/BnZR0R5BsD8gKqp7mHfFv60aWGn1S7E/DN6H7sKzkD+JeYy6LFvBv56ZxqqGfMU/7oiludLhvL8LGLPrEuGpv97BayVNaq2/lRRMXfoqwD/zBfXUAoOKP9r5mkZvhq+/SsMwpjUkh7/RnfAQeSa3v8JJXCeb0K6/4U8yn8Kgqb91jHyq3Bi1v1+MJ3hsPJc/t0F1oLlclz9C7Cljx7GsP2sSz62WpaC/3wdjBcINuT+aiWDQZo+Ev4abpcYkDri/0Ss+XCDcqr9qGp7V8/2gPzsJx/SFrpy/Fhvu+O8/nz/G2VNtSP5xv2YNWG5g37K/uFfEOHgzVr/4RYNu6T5mv+xaSV/8fre/MS3ylOwzjb/xOV0V7fOmvxPx8XYga5Y/340N8ESfvz+GJ33wmBm2v33iNQukaEg/iN81C6RoSL+dNQsMFkeavxs2N/rWzYU/eVAoeSJoqb/yLeqP809wvz2WJesgcqs/9Kr4so0r0D+a4q3fNa/Av/tp1wLlhaA/JXIhXtN7wb8jQRo3xiWsv3psSosLs5u/gafdoRUxsL/ckG8xqBylP1/2dv9f2XA/9j00+Q1jjD9pK6hNTYtKvwprQFjvTqg/sTb7AmbCsL8FRtN+ZcuHv0S9kVVZ05s/t1GnsULDtD/7ZhBkzfClvy/dze7BSsE/UNqBhCZer7/337Us5W7NPwkTxnCTubW/pzX+NAcnvj+HvxCCgjyvv6JnH+9Toae/rHw2+FWrn79GUKbLVmyhPz4Fjuq9Q7O/SCkd0tnvvL8FOepJ2Lmqv0rb1EqaK3C/W4UR0NxNsz/oIH+rsYW0v6WXlPJSi6C/ELh7oIyeqj+gJ/YcTbCvvz95JB82IGs/vIpPT0uWrz8kTv9Io3hJv2MTqWx9SpO/71j7Qtgukb8i2OzkVOaDvz11nQBQJom/heZm0ZMcsj8uJ1sBnRe3P55/u01WDbu/ZCVlPRA6mT/KyxWgAmizv+YiYbBjo6e/u7b+RgiKoL8hNCxrRF6fvzgtWJAQF36/sR+hRwRyoz9PihIyRdjDPw+E7Q1FuL0/oNLrSYDRur9z4nxkxWG4P3C+tTMKtLW/9t4OkDvNr79424sCAnOIv0gbrf3e5J0/duzx9YrhqL8S2X54gJOKP5hIm+nCoKG/wGXo2mfVjD+XnWNmFj2cP/er4LwEsJS/H63LMzJxuT/KbaVx2QCqv486k1AZnZc/G8+Wp85grD+KTfPew4mqP7K6Kjc3548/O9/6zsVkqb9jA8W0rpWxv/coxqyp3Hi/rFcR2wbrrr/QVf/y8COZvxhEx8DxBsA/cL1+f7E4sb+F0i3uIba1v2/JOF6TZHE/E7M/bQjvhL9yucyvOU2mP8IwpXMTTZI/MMX0zIEEoz/dJJHAFW6jvwiG4WiONpi/HLBaiNVghL/l32mZAQhxPww2p7qaC6O/N8P8Ysj0yT8/AObN9iilP+z+kCMgNa0/n2KIyWjUkD8joALHmlDXPwG5LqYflrU/vrh2+M1WxL+RKZyV9cI+v3QrYyYvR8i/g7h+eWi7ib9ITNgAfzXCPw6Z7/kNboG/tpdq5pvnib/l/AmRJymrPyKJqsb2RMk/chcgfPPqpL+d820FqFFkv1i+1T+ropY/plzsmRHHrL81p4e31F24v2vXuoeP3re/2AxIDmXsgT+FU5Rcr1mXP5sNOcbli2S/lVIL1xEjyb/83hzU4EmWP53aYRh7O6M/m4f+5tz3xD8yk+i52sKVv/KiK1Euh64/XSdMvVSNr7+w9IwVlru4v0DtWnUFRLo/QikH/+GlvD8l5W+dYeKzv1E0VtRabKE/1fOZQvidwL8r8tkhoOx5P06Cd9Z28Jy/UoD6sil7kD8x78cyNwGkv+dJEUh9M7i/43V++Im9zT/dpj2S+fqVP5QqnPu/h7c/qPSf0P5Xmr9IN0GKUkykvzMyVYyNboy/3WN7+guzo79muklyRb2nP6fb7/NdDcA/Y7SyYC+rpT8AAAAAAAAAAF4QVQB6l7o/6/edB+NLrj979WIyCR7HP13N72kr2cW/Ut7uW0eIjr86lcaTJXasvyPQx2xA1ru/yd3SjHgst7/j0dZYxXm3P7Td/QsmTsa/MfLegqqjTj9eBe93mb+mv8mPS+Jiz5i/BZK8WqHmmL+GzUmZiM2xv2Irz2RqMKg/d5VrcMCNmb9EfugyDfGXP/4Ovg0uDqu/0vfXOsGyjb+rski+w+/DP4bZLB5pQrC/GwaevxLMoL9Yj8blT720v/AlprXkd7Q/NKhTc4gGjD8qC3iV93e0v1DHuSQxT7C/IuYw+pnsmr/NTp70NVmav5aBjrMs8IS/U0iDjjQlib/rT7RimPCvP0h1/suYvqS/vy7GZScokD8SOS4tp5yhvzC76hV0/ow/zmgjEfxQoj8EGw3P+Y61v8ArQ77Vf2C/ldo9HyB7rr+7RG57l6SfP0j0RFaRZbG/yvtHp7g1uD//2A3F4tSVP7jseL0jHaC/zTHHDw87dj/1SZ7Vp921v8DG0ekkQbM/JWoCnYOufj/40Ihzzs2wP/K6nH237ao/SFQjKFjxoL/1b9BAvQS9vy8qUaboaKS/26Z0NqEgm7+aSo2j7GV7PyA98pU2Eqi/TaHNRc9XkT+h151fkKuhP4eBo+pkGJo/DvHhW6HSlL9lxSiXzE3IP/VSH3fguYK/V6D9HcFEu78sgynmqSKgP1zkhKIK7qi/8nVbnMY0qr9Q/RibcmuXv2Ju9/WCfWi/UOJwxR+4nD8udPndEP2HvzXDC2iofK2/MXPHPNlbnD9300NVU4W0P+BWmhRXM7K/jflBpLzJlb+PMlwk1qu0v1YMePWMGJo/itNe5uZnh78HyUx8i1uhP0YH3Z+nJ6W/dZr7zWGTmz/hw2qMbDeHv3qi6EoTVLE/lqsX3rsSgT85u6Ajn3+kv4Ry6ROtHp4/DPtcKlf8SD+zNokBZdi+P0OPLXSkopq/ZuFtwpAcvb/CtsP70k3HP7BzdDvoscw/Y1Ux9vXscD/9Umbe3eqPvxhrgJFhWca/dICDZRm3zz+cwhg7XIjAvxNKCe41D7i/fF5n8JCQsr9VtAK7OvWlP+cPc3WiNpY/nYtbfQdKtr98h8jHN5WLv2jwoe2DkcE/267aaEK5sL8BuJx1Hlq5P6axy51nJJc/Vk4pqcx0ob+nmWdJ2g60PxVxpdiNWoA/JnuYcVsIu7/rqL89dkOrvwB7DM+kd4S/FilWHhGDlz+fwGwKkt6Wv9p5+nv/p2k/eEJ2SVKLdL/PzHO8dDx3v+mV9lonyKG/LkYVssK4cj8ffo5xCNyKv1Li4m+Jfay/Ykrsi8aUnT/PfVLPZhSmP1p7adsHtIK/GcpRgFWQsD+kNvWGcaZxvwiFcboU8Ji/uvJHthSHqL/+Zuq51+2xv/mfwTrZcrW/VkNsoDfqnL9xfCEJ5iGsP+9Ipd5Wkak/FBr34r/7gj//9JJFogW0v1ECRClkjn+/HbfVnJjXpb/o0rJvB7GXP+QpL2I33ow/kCAVG8IxtT9U04TEqmKgv4fp/sxI17A/iOn+zEjXsL/HpDPN/wayv2VpTp2kaKa/M/Vu9vuoqz9XFRL7RNunv+K3i8P0das/RCBMrO09tT/DX/NvI5nCP/gclUhPYro/vhsfQS8Ejr9JzUh2LF7VP0cUkDTEXMm/vtV3WBiPob9V0Sqz7yzCv150AW37IK8/khztCgF+xD/7c2TDBL3Hv9qTok3cuc0/IG53jDfFpb9rjvzxQIbBvxo1EcJwC7e/kjbyuvR3oj+AAkBJLym6v/VxEsEpwKs/iNxcImxmxj+8izL+dL6cv2o7SRIAlqk/uUm0IqDKqL/e3ewT+zm0v193Rs5lka4/AXIgWMcFoj/zliLMlSXAv5wM7EdhKLC/BsfZc0rOtL8ZNremokSSPwfaqn3t0XK/Iu2QjglFpL+oUA1exLSbvzNVOuTFlKG/9YCDsivEsD8/cqpZhy6Qv/IiOJDyrKG/WUW3lONBfT9HIuN+RSe2P0WgcuMvx5u/MfO+qscqwT9zPkF4m+mwv4Y/Tr02Inw/zocR2KuxsT9rRw8JDQCuv9f0oGKOF8E/AnR0vt4yuj9xnEb6upayv0hhBA/fe72/UHQedmMQqD+emHrd4UPAP3MGIg5alrW/fLzXuJFErr9eBH+4vsW+v7nU56pyKLG/u/NY+9vEsr+scDx0LRGFPwLUhdbkZoA/2GmW4eTMoD9mCS6D1h+Sv+EKaohpjaA/MgnIFtIvoL9jFG5wViqWvyGJkD2SmbS/1BtjG6RJoj+t0Cnd4AV8PwK5iNJXdLG/BoqVMJHXsT+tRxgLN3eyv1Zf70qFmaU/KAMxBG9TlT/aXUUx1xyjv0Pb6WIMxoE/SjJ2KA9Cq7+5STcRj6WpP4zIs+yI1Em/0zRbA4SFmT9KRgkvh3JvP65mn+vBaLS/DG3BZIQhoT8f/GrWoi2gP8U5uCBkj3m/7hqUVq2qmz8K/b+Ff8WvvxqUyA/E4aO/sxySeSLfnL/xiKQZgHeSP4Xl+/M1SbI/Rcm1QwpYxT/ypzzd82uxP8axcuM9UbO/oXZfjf2Ok78OcZlHgGGwv8mjygVJfcM/U0tKpU5gh79UyexLmQqnvwxFV+NIl7K/QneMU5RDub8aboy7Z0Kkvzl1nQBQJok/kDz9t87hlz+HRJYTfyF6v+YvTWFbNEo/9xfqBEROgD/c4hRBdnFzPyvAJs054lm/spzGyoqirj9LoyNkaja5PyUF6GFboLK/gre6do2Sub+gTFAVJP69P3yBZtoh/Ii/+WrFoHfIcb8n8KXBUZCZv/L51EEp77G/VWOg2U0cWj/9DxTWnMGTv6TlM+0CNXc/4tb1wqrPYz+XaFcnbI6Av3a5P6htc5K/yGs6aHTKrj8kj7DYohRzP02GJigoJnY/0mFb4gNovT+TNMdR78XMP9O06usp9ry/rk5drg+Vez+4sT0fhOqsv5ATN76RD8k/0TuND+c0qL9vAacuO+28v5n9vRHxi62/unGTFsvYib821FNi4ymhP/L73VGs3Zy/IS8rJsf5gj8oUi+Lz4O5v11VVd/nG54/8feCZJFLob+tbRot4qm0P5GpN36mjZ+/l01fQIIwtD+ozMxYQty3P2Cg31xsddg/3SRKdntUuz8BQdk4A3CFv76l46yKe9Y/5OTwf4+LmD/MFWaXN1vHvyGuNxSRzbg/zpKXxsP/wb9iapm8bGHLP0GDgcjHIq0/neilQu+30D+kU2Y72grCv1qfReEpuMA/wAd3l+6K1T8Q1u863wnRv9b/HV7F4tY/JZOqq57mtb/IcyM7vgh9P+F2ST19tK6/xJT/gKsetr8Yyyc53duxv94BBsNcRFU/G8omc2+Woj9v0+XVOS6ov6cxgYieYLI/qqjy8MKwwb/MwR1j7WOwvwYtELv1TLA/khCB5/wxvb/XtuHskYy1vxWOuoSJW6G/0U5zZixDoD9eD907Vg6zvzA2RN6nPKS/JsCtFwC5iL8LXjidNSK2v+eJAgjKErQ/vPWAvRt4kr/xCqGYsaapv4fGSwv2w8E/Hl93dcEhoj+q1cpoIhu0v6qNw3Fxe42/TJtD6KkTwr+3Oc4jaomzv87QfHxKJ4E/QAVtAzd/sr/AaOXZ1eG2P8reO6661LU/ydoZ99kTob8EHgJBYV+xv9UUeTPL144/XYdOrgXxtL9tQAjs6Ni9vwgh0Q5nO46/lCWvjrQqxz9C/Fy7+sqwP5yF1bh/JbS/YhyGLsXclr/t5hycOa+1v+0kdbz8DaU/PHBLfp88tr9EXwIKJGyrPzmk5Dd4Uqi/VozXjl7gdb+GbQsDQ7eMv3kharwwrKE/Dttj4z63o7+J6HmjqHSaP13XY00T2qm/Wf24Ev4Ep7+Wh/qUxd93P3AHG85vQKq/gwcbzm9Aqj9qt4aJKeOwPwAAAAAAAAAAc1lhoMER2T9lJgIg/la/vxjlmTAcjqi/3NOJ4Nb2lb/itZxYBmd4v20uGd9OWJC/HQu4Qr9nsD/OhXT53qm5vxj28CTy8om/Pk0eNBJnsz+Jjyld4h2pv+ZgnftGNLM/qyVSnGm5tr/pwCbKmTmmvzT5cifAZa+/aE92dTwZob/tn+WQsCWDPxPY5Dbfmao/BD3GVb8KqL++wkLVbBOlP8AJc1Lt17G/cIMJbSgjxT8SJigDniq2v5okMbo9ea+/DfSNXUOhgT/AtfxKVyS2vyBHIDi466i/oNrYXIOSjz+jHfvM+amVv6Eavt/64rE/9gWdgxHvgr+WKHLg70Wmv8/ZJWAvkpw/37uunmy0q797xUt+gKqbP+4alv1C94i/dOJFruJAfr/K7AK0SCuWv02VFRcP3qC/6c50MqCDjr9GM4CVihaUvy+btT2Ij22/Z9jH3iEWtT+uiHs+aluzv9L7xAR6q3u/OpCRO/Cqbb+2+aMlfYSgv+L9UtTflH4/VKNWbLXypz/PR869MUqAP/xHzr0xSoC/Kkr1Bn4Cf7/MErbby12Pv1FgPyd6Caw/FFFIjay+qT+Ie/dTRU6wv6lsLW4q68E/7XM6ed2chr+9W04jyn2RP09W00wRDM4/4CSExtmwtr+cP/KZ7V+XP9kJ0APMhra/jim1XNh/nz9e7ruonLi5v6HGaxZ30KO/3v88lgG0hL9iOiHlvtmRP78syAxgYpA/jaAYMAWtmL9EH/K3saGyP3qLbiJDPqG/R/hZkIDHrz87ewt2whZkv8CFJZ3K4LO//m1XqpCbfb/ED7NBMfxoPy3w9EjxH28/9SFPEVd4iT/HVcbotYehP4NtIAfBUIg/xcN9k5Qaqz/CE/KDoUapv7Emeyum/6+/T0hASCIfq7/U+JBny+dmP6A6MhypBqQ/gLNYM+fCiT/+W1I6sN+VP63vuts4kpA/xvIkLfUEsD/iJlIZL8t3P0ysvo3tt5i/AM2nBRV8k79ufrTfGJuUv/3F9EIm/sQ/kOKSmMiymz9N6gWigirIPyFIIW+msbG/6IFrBWdLhz83TDIpa0GYv0IovuqvQ6a/51fwPDSTx7+WZDl629WxvxaQEhKnlLq/n7TU62QCoj/bWqTPID+jv6K+ymiBqKy/Y5fu/srxsz/QIDn7UMu3vzbVfD8cOp2/gcs1hwCVhz/0y4SKy9yoP2pfokOF2J+/gytRm43QmT/tsDDts/mmv0Wn0BfklKe/w008ZX2fkr+AG/HtQ/OTvzaExQf/tIq/gwdp2/7Qgz+8qc5bgoiTPx7pMr1bOnU/DMvk88nal7+kp+QACNGYPwLdPP+5P56/7IJAD3r2sj+9Knmvy9yGP9hN/A4up6E/ByYLp+ERrj+0cXiVibCzv5nFIUfkmJG/XFbphYtGmr+P9LWlCvOQvzS8lc6ytMM/xJDuGa9Io79mGiJjrdGjvxSLQJzbVbS/HY77C4AbgD96EoO8IZGvPwUaElm0sYu/IpeohofLTL8qtClBzcfFP/JNyIUm66+/dR6lAVLwhb8fV+v/5Su9v2ANzBUU4qK/HtwqRHlMvD8I8dK3UTOgvyqngLNpMKm/Hzlo4S9upz+twhaTMHWnP7DT+QWUrae/pXaoWb34sr+QU+jqFtOIv2wpWlrk85s/dpDPdwISrT9DOsZGKSN+v3HH0eXTu94/hiQCGQCtyr+6Bnhxlk+3P+6VobyJDre/45zP1oKFxb/GK6srQAW8vymj+B/+Iaq/2mLgpeCdpb86I4aKxP6RP1MBQdO2oZI/AwhKtBrnkD+9zZxdtoahvwZ0qVbzzq+/lHMpcfFrsj9ZZHWczYJePxTW8maTDKc/qdrFlX07lL9pUCxjhoSvPx65STdE6Y2/mUkUcQ1mtL/17nAF6mLTP2EPQXGi76C/KTXfY1kEx7+lSRaI0i+gP9s5GRmZ8Lk/+4FvLQyesT8BqL37ntbCv+TRjPwVjKA/2gtw/ybMrb+IR0RQ8Ciwv+kaCdfY752/a9NXoHVVrb9dlTxTRJmgv20HFj33r4i/pCPgEOpknb9MfmssAIS9P+yKCcffVp6/TsPRKxOiur9uvSuohZCyPwbIA6bzNaY/QSMckUhcsb+qP1wKVympv7JRet1JU7g/nP0Q9j6vrL8d8JfvzwyGv5Ezl4Cv4ry/O0VwiOfaoD/c6B7Dst+HPy+QCUrw3qu/Uk/5k6o3ib+C1pRKgKiPv8jLcLfA0Yo/wwdkYNK7j78HaJp6K7OUP3JPb03BoZm/t6rnhwv4k78sMTwVHMOvPwkNl9iTy7M/2TpvbmS0sL/W3kXcjR2xP5GwA2FNnbO/Mm2+kMonkj+MtvcjQp5yv+KNuGqjdZC/rEMAuARfkz+LxfNLY0esPyX2KRZOpJC/5KLNO7m2rz9EPU3M4X6Rv+iWwuLZu6S/ZDZek8/UuL+F3T9+n/bEP/tjlY6S67o/zzD1jTfqzT8gTg0kwJHGv2EroSCEdsq/Pvf0DXUwqD/m+posEDa0P1DdK+BkxKM//9iziskMsr9CIg39MReav1DzlOYuQre/mk7OEeQymD9iVP1Pu/G0v45yJkjJnqK/6qRiST2QUz/B3CB6otiwP5nH+BMtUJi/rezC4kAloD98CGuxYBa4vwAAAAAAAAAADGbR7Ifgoz86XiT0zwWoPx+fnTD5/Lq/dvheIF0Hnb94LnpKlqenPx9D6yu/T6M/K2VMwr50sD+rhCmh4tGWvyhr844FrsM/8RozvZWswr8RS1HUNTyyv4pEt5Q5HZW/oO9xrRwzlr8zaFbQEbGWv1MKPqszRoI/oJ/Bs23BkD/j+ahJVW+Iv6wq6QYG5Za/cbJI6rB8m7/UhwyNYUa2P6Qoubo/Ho6/Q3f0ZCBoxD/r8N1Vm5DEv8K80HRGQkS/DmK72N06rT9LLZFkTTCov78fTIdp46Q/7yHoxKbNtD9UmwnJ1mbJP78A1nrl0MC/cWlUPaXGuL+Cc7u3BlCJvxlOWGKnw5y/tFmyv31glj8VaCHvw7C4P0JZDvzOjKy/YTQe+BBZob9rg9AAB8aePx5TDwoRk7A/18lRq6Bls7/Ffvfrhdlvv0yU5IP5C6S/jd25YC9Qsb/TB4mcTcKhPzCEQFboHHE/rEBd77FDo7/2D8vNy7Oiv3BrwxtyeYK/7nxZ+T28pT/KTmdP1vS6PxlMDFQFzcc/kz2hDw7ycj9QLn0pbNTDv6xgUwQJOrS/iauROSyvcD8NBJZ9FOWZv2I5ylaqfb0/H6dntvDUwb88UBqE+SSJvzANS18GLpI/tzpQiJJAl7+l+qrhuuSSPwAAAAAAAAAAMIQRc5OouT9qtmzBaGyEv06aTPVGZ6c/m8mqC1y4uz/7momO4hK0v407ivau/HI/vWMwuv9qsb8RQGRfnlScP0OwgWGDgbs/hBT+t6wQyz8F1bTFsp2rv9C/aP7nysO/QG0nwu4Qvb96fSs6Q8aov7KqmZS2Vaw/KIq5NEx3yz8B4niQitmev4JBTwdX/pk/CoHvK0ugt78SXsAkg6C1P6tUVEA/NrO/hThyrNAMjb/FEPcdcXKyv7dNHEuYlaS/otMLJLo/sD9rlIp48NV+vyukKGrWGbC/zkL5f5Kxub/zAB68eEuhv8wqPdzsGEQ/rx6jOSmEo7+6vHENGJSLvywcoLLhHaC/HOHRA2VCnD8aM7fnAWCJv1KhcXiyRqg/o46fCtcTrz8Zb95v+oCbvxbX8NE03ZC/jr4VjGvOgj8up5Hyvf2Xv1LEi83Pma6/cQ2PLxjNlj+Y2C9BB7CwPzI5NpwzfaY//4INGaxioD9lWZzJlqaqP8QCfyg4Hq6/dgHJEBSstL9Kq/cis/2IP9mbua526n2/ZSpMZQujpj9FMfa52lScPxk4+o4ZdpI/K+zBg55nsT/SamZuHlCGPx9DsQ9zlaW/+Dxyhjem2z8JyqbUgDTMv4xUAe/tpMU/lcYEOrpHwT9/JVUGw4C7vyg7EF0uxrc/mnlJbvvsvr+8R8kWr7TLv3bdz1M/Zri/jSZEHYO/tD/bVp3ZU2fCv1VDygipR7U/RCH84Lotsz9xc9HWSC/Av0Ne7656crA/Xsxev/RNqD+BlBONrPatP2WE0ZtBt7W/JAbQRRfHvr90mwbpqMCiv6iGgGENF5C/THb0rRtIgL9c3f3Sx4Shv0rJn2sNtJY/nj6dZvQUer9ypOPEPsaiv+HJPG5N8pI/139sJ/q9wz9ypyUw+Px4v9RANAAosKa/4kROBPQsoT95yKiWbdGqvxrB54e6dKa/m+aEXKIvwj+Y/yD87Qqtv5pXMbriAcY/m98HVNdGpz+YxwRQ4AayPxDE8klQsr4/2MK4UZ9Noj/s44DZOSKlP+KcBmRR9Zk/9hUdWrMbqL9hlgSm9O+4v+OO2i6cobu/FK5Lzclxv7/W2HexKi14Pxcj4Zun77g/P7LUw0RUqL9ow86OEQCQvxqwEEzYarC/75BH/apPsr8deagBzhzHP3ealWGlQME/7r2fb3HWoT9j1U4DMfG4v5e4cnbm46y/l9PKVR2ywj8SE4VS2GWKvzowUpp0MaM/rKUEB77rsj/iFKNwVl+Xv+W2VhKYJ7i/sfqc0hu3tj9fcsLl0pe5P2QACni/obS/RnxyYE9lgb/M2LwmspioP1mwOC4Qv8A/Qq/IMrjior/6qLZ6nFC0vwKX/7NAJLK/9/ZHI+t2kT9BS3TxM7WgPx8t9ZZIIbC/8JzRCWmqvL8stm2wF5Gjv0a54F2AnLK/xp1BOJBhjb98vnX/6ACav87hmIcnvps/g9Ek9weEtb+dS3WUybukv2VAgzJI462/QsIDcj5no7+TyH4ASwmXP8kYsY+3la8/y7rsnR6zp7/ZYNVtBGysv6ea/Fra2L8/GplUC8poqT/toBQqg86ZvzBbx1qVs5K/BiaehykUwj9jRlkVv4dyv6paqo3xIru/vRZq2lI+pD+OhYhIIjGYv8Xjwr8l0aK/2MtnM7zLqb++8wIB9SC8P84mIoy/7cs/quUURBcRpr9mmUvHoZfLP2AkJG1NM66/+Qvx3CrGqr97Q45Is5G7P+kDI+Y2g6C/OmWUs3i1ub9UZmn4xPbBv4DLu4eROco/8eScszvZoL+jQI2CbwHCP1w9u854nKq/TvXYQaLeir/4nCgD0CaZv0qvKLgRgbi/vDvVFGZzqb8hoFoXiDGqv0g28H1828U/jYxYw7JXq78UhkbJeUxmP7AzON/MzKC/6G1LhtgloT9UZwszESGjvx7qPwzP5J+/int7LXshsb9i3UJqHYuSPyYZ4zEMvZO/MzVEJ9y0o7/p2sPXRypnP745PrKMAIc/9i+/h+znpb8RPAEzMH1kv4gds5V2zbU/7r63sgKVrz9OKXx9QhPKP1JN8VcTa6S/a9C5/h0/ub/ymTveTEa7P5nJIDtsF62/qtbHCaZln7/wN1qyOnmlv64Ty5d0P2w/7SYI5x+QsL9+RAuiBSuFvxkmIiczHKo/fWIxsPr8fL/BgYV8os6TP5Rhea65lLK/2mZKpSe4nz/f1f/SyZGev89f+CxnNZm/oIDU4pMUrb/rrm9pHtiuPzmNQczJdKU/Peg13BjKkb93bRWjpSmQv5effmsv9bW/Ten0n/4fpj8Ud+nGgXOgv742Ykk9P5q/QKtAcf3HoL/8LcgLT2hsv9c2LLtGI40/cAF7St5ZrD+H08ln7Pyuv+gcLIaFc6S/2wU/wkN1p7+ps8l9loKxP5umCZ+D3qc/1ZF8rhActr9qXOK+sRKYv6mly5cA5Iu/YO4/Ax8Gez+JlQI/mdSWP6ue+3IMD7m/kacmRqaWlz/qQmzrK0Whv3Bf4j6+yZU/bMNuS7/dir9A6o0QELqFPyIMkhqMpZG/+AxLIR2Gqj8Q03x5hHvCP8sQJEnmgIo/p9PWoJEnrL/vXqqW4OGnv0BCCiATfMQ/Sb6b4A63mT8AAAAAAAAAAJPfTKuavYq/3ZzqFYOwiL92htvGYeVsv9ACUe6GKKw/9vnrHXuPtr+V4WT53tylv2nLQJ8yGaK/nD+IEba0pz9TX6LJigvBPx2q1KvJRqW/o1WHHe4vcT/Vs8jJ6bJSP53DAX9wPpW/6rdcoqQYqz/ThZboDtOeP5obq4aWr5e/I9X7DNT9tj+EvZXg6kazP6KiE0/AeIU/URx0R/m+s7+3/rM6G2qrP4HVoTtPZny/RMJyJjg5qb8/jdhUutOhv2niojele5K/5ocC+egUsT8WGw9VeM2bP2efbrlefqE/c6kqQKRJoj9uCN98AEJdv1REkUzue6m/cTRsECh+ZD/Pqbi9OQCVvyO6U1HlzJy/nfKZ82ZigT8Wx+lWFU2av3XyJjgr6IO/KTHUqp4Ddj9EflvkNGqov/pCav1VrqW/g2Pj4ZN7cD9KvYh/0pyvvwj8rPptoHQ/IAy94wQwZr849hsfrLVfPyXx8XYga5a/LQQXVpTQnr8q1qtfp8CAv93rtuH4bag/3uJsH0zRlT/9+lOhSXC6v1dB4r1xdqa/K26FY9DLqj/GvBY7Wy+hP1+DflQeGpK/lY2ZYdKxfr+DYKRQcaGhv9jWmegAHaG/JAa3SEQbqb+oD3HFwx6nv55r1wuyFH4/KiaaIpiCtD+SXdFe17iNv99E5EN5mK+//pocdM6/or++/oowEYmkvwm8l+yRv7U/Xl38WRPysD/Mb7YdEEqXPyhS/6L3wpG/aV1h6QKDmD/XqJlhsT63P6iuwVtvZ7q/kqtOooiHsL9msXacRrCzP/BlBDzr7IK/yYHkSD8wgr9YPCSs9+2lv3rMJYyjYbG/S37A1dcntz8qamiMhkyqvwSLyOJhyaI/LfSfcOfqlj9xgS5PBXOgv4soq59k2oU/uFXElztErD89UdhHsUukv1u6QnmIuZQ/KrpFjelAr7/c9EsZuPWjPxfiMXBPu7C/mxOvVhgRmT/98yA6WgS9v65Ke9wDcZq/ZMzTS8Zuqz+uXB1oX0ipP7Afeq9QC4I/OEBjeBH/oL9JXM4HXIZgv3qB9LF5A6C/OAkVKzaueb/jS7/qYPWSv/NNC9ilMoi/ts8wQPgMsz+wMg2+GB2cv8hCMcPCB7W/7FIK28oQqD/6VL8xBBuWvysk9CvG1q0/yz0uGAq0pL/9oOCCTvKpP+K/9Qzpa6s/vGi+xLPggb/Yb1J/wgqDv60fc0Z6qbK/a29H2CjAor/HoxTQguO5PyL2SUwok36/TAUteTsJwT9R2Sba6k2avxC0PMQLH6u/CGhhzCxbmT8sFojU8EyhvxiM5YcMloi/aXV1im7Qd78WHUjW6T2pv1KM5AGMlq2/0J/43k9/ej86eAUGokaqP655IsIfZ5m/sW5Be/CGxT/sayeV46q3PzeV852NKrE/iH4bwux0vL9RUVmgE8O4P8kHWvmmb8C/YypGk3qguj/ZDnUL7wysv3FxdAYnML+/fkf/ctqmpD/AMiMGZfOPPyPlTKMPF6S/6eoY4uaTq7/BxBEPMM6fv72Qhv4gRaa/ni9AuZh6db9hKkjC7l+RvzEz3/uEfoC/tuEs4WOLmD/l8TWnyDVxP9M4J/KalKK/1Hx/MYQ9nz9jkzO+XPl7PxHhNSUxKUG/vu07k07psb9FxWTjwXClvxT2mYdT5Xy/QCHUZwAafb8w6TfsbMOWv39XBsY9n6U/zdHfQUcjmL9qinlgcTR9P+FvUPKf4bo/RzWtKAducT9pU68ng9u5v+g2oXkFhLO/lJ7qlfJIkz/J6/fQPLq5P7yTEbojwZ8/gGxaEc1Utz954El5WPTDv+ImKZVjLIm/aZBo9z6NpT9YlhzaXsKmv/TPPjmHWUO/T4dThWYfeD8Sq0JZ7JZ1Py8QAr0OYak/KZvKKJh0sL8AxxEUE4Opv2Dny5sPsKc/K4F7Fr8UmD9cWvb9JOuyP2T+L0hU7bM/P/dTx1RFp7+b04rLxG6wv2vr4EiIV5y/oO5XdXLFmz87cpkI3XrBvywUcopux7G/84ikc6Angj9yO33JRH2rvyV4ElJ6fVe/EPnVsgWHlb8L1FoBg9SKP72+3BLc9bQ/NmbfMaGom7+f7duJYz6qv/N7UCXOX6Y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"dtype": "f8" }, "yaxis": "y10" @@ -50962,7 +51136,7 @@ ], "range": [ "2016-01-01", - "2026-07-17T00:00:00" + "2026-08-05T00:00:00" ], "title": { "text": "Timestamp" @@ -51223,6 +51397,236 @@ ")\n", "fig.show()" ] + }, + { + "cell_type": "markdown", + "id": "5393f2ef", + "metadata": {}, + "source": [ + "## 6. Plot the HYOAS_OPTIONAL_COVARIATE_SERIES_IDS\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "67ae19cc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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series_iddescriptionsourceunitsfrequencyn_obsstartend
0hyoas_hyg_dgs3_proxy_change_1b_bps_l1bHYG-DGS3 duration-based high-yield spread-chan...Yahoo Finance (HYG) and FRED (DGS3), derivedbasis-pointsB26592016-01-062026-08-04
1hyoas_observed_change_1b_bps_l1bObserved ICE BofA US High Yield OAS daily chan...FRED (BAMLH0A0HYM2), derivedbasis-pointsB7802023-08-092026-08-04
\n", + "
" + ], + "text/plain": [ + " series_id \\\n", + "0 hyoas_hyg_dgs3_proxy_change_1b_bps_l1b \n", + "1 hyoas_observed_change_1b_bps_l1b \n", + "\n", + " description \\\n", + "0 HYG-DGS3 duration-based high-yield spread-chan... \n", + "1 Observed ICE BofA US High Yield OAS daily chan... \n", + "\n", + " source units frequency \\\n", + "0 Yahoo Finance (HYG) and FRED (DGS3), derived basis-points B \n", + "1 FRED (BAMLH0A0HYM2), derived basis-points B \n", + "\n", + " n_obs start end \n", + "0 2659 2016-01-06 2026-08-04 \n", + "1 780 2023-08-09 2026-08-04 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from BAA10Y_forecasting.data import build_baa10y_multivariate_service,HYOAS_OPTIONAL_COVARIATE_SERIES_IDS\n", + "DATA_HISTORY_START = \"2016-01-01\"\n", + "REFRESH_CACHE = True\n", + "\n", + "\n", + "covariate_svc = build_baa10y_multivariate_service(\n", + " windows=(), \n", + " include_covariates=True,\n", + " covariate_series_ids= HYOAS_OPTIONAL_COVARIATE_SERIES_IDS,\n", + " start=DATA_HISTORY_START,\n", + " refresh=REFRESH_CACHE,\n", + ")\n", + "\n", + "covariate_svc.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "bc38b294", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'series_df' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[10]\u001b[39m\u001b[32m, line 26\u001b[39m\n\u001b[32m 22\u001b[39m plotted_series = []\n\u001b[32m 23\u001b[39m \n\u001b[32m 24\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m series_id \u001b[38;5;28;01min\u001b[39;00m summary_df[\u001b[33m\"series_id\"\u001b[39m].tolist():\n\u001b[32m 25\u001b[39m series_values[series_id] = (\n\u001b[32m---> \u001b[39m\u001b[32m26\u001b[39m series_df.set_index(\u001b[33m\"timestamp\"\u001b[39m)[\u001b[33m\"value\"\u001b[39m])\n\u001b[32m 27\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m series_id \u001b[38;5;28;01mnot\u001b[39;00m \u001b[38;5;28;01min\u001b[39;00m SERIES_LABELS:\n\u001b[32m 28\u001b[39m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[32m 29\u001b[39m \n", + "\u001b[31mNameError\u001b[39m: name 'series_df' is not defined" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "PLOT_START = pd.Timestamp(\"2016-01-01\")\n", + "OBSERVED_ID = \"hyoas_observed_change_1b_bps_l1b\"\n", + "PROXY_ID = \"hyoas_hyg_dgs3_proxy_change_1b_bps_l1b\"\n", + "\n", + "SERIES_LABELS = {\n", + " \"hyoas_observed_change_1b_bps_l1b\":\n", + " \"Observed HYOAS (FRED)\",\n", + " \"hyoas_hyg_dgs3_proxy_change_1b_bps_l1b\":\n", + " \"HYG–DGS3 proxy\",\n", + "}\n", + "\n", + "SERIES_COLORS = {\n", + " \"hyoas_observed_change_1b_bps_l1b\": \"#ffb000\",\n", + " \"hyoas_hyg_dgs3_proxy_change_1b_bps_l1b\": \"#3987e5\",\n", + "}\n", + "summary_df = covariate_svc.summary()\n", + "series_values = {}\n", + "\n", + "fig = go.Figure()\n", + "plotted_series = []\n", + "\n", + "for series_id in summary_df[\"series_id\"].tolist():\n", + " series_values[series_id] = (\n", + " series_df.set_index(\"timestamp\")[\"value\"])\n", + " if series_id not in SERIES_LABELS:\n", + " continue\n", + "\n", + " series_df = (\n", + " covariate_svc\n", + " .get_series(series_id, as_of=as_of)\n", + " .sort_values(\"timestamp\")\n", + " .copy()\n", + " )\n", + "\n", + " series_df[\"timestamp\"] = pd.to_datetime(\n", + " series_df[\"timestamp\"]\n", + " )\n", + " series_df[\"value\"] = pd.to_numeric(\n", + " series_df[\"value\"],\n", + " errors=\"coerce\",\n", + " )\n", + "\n", + " series_df = series_df.loc[\n", + " series_df[\"timestamp\"] >= PLOT_START\n", + " ].dropna(subset=[\"value\"])\n", + "\n", + " fig.add_trace(\n", + " go.Scatter(\n", + " x=series_df[\"timestamp\"],\n", + " y=series_df[\"value\"],\n", + " mode=\"lines\",\n", + " name=SERIES_LABELS[series_id],\n", + " line=dict(\n", + " color=SERIES_COLORS[series_id],\n", + " width=1.3,\n", + " ),\n", + " hovertemplate=(\n", + " \"%{fullData.name}
\"\n", + " \"Date: %{x|%Y-%m-%d}
\"\n", + " \"Change: %{y:.2f} bps\"\n", + " \"\"\n", + " ),\n", + " )\n", + " )\n", + "\n", + " plotted_series.append(series_id)\n", + "\n", + "fig.add_hline(\n", + " y=0,\n", + " line_width=1,\n", + " line_dash=\"dash\",\n", + " line_color=\"gray\",\n", + ")\n", + "\n", + "fig.update_layout(\n", + " title=(\n", + " \"Observed HYOAS vs. HYG–DGS3 \"\n", + " \"Spread-Change Proxy\"\n", + " ),\n", + " template=\"plotly_dark\",\n", + " height=550,\n", + " xaxis_title=\"Date\",\n", + " yaxis_title=\"Daily spread change (basis points)\",\n", + " hovermode=\"x unified\",\n", + " legend_title=\"Series\",\n", + ")\n", + "\n", + "fig.show()\n", + "correlation = series_values[OBSERVED_ID].corr(\n", + " series_values[PROXY_ID]\n", + ")\n", + "\n", + "print(f\"Correlation: {correlation:.4f}\")\n", + "\n" + ] } ], "metadata": { diff --git a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb index c85259e8..516ae9ea 100644 --- a/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb +++ b/implementations/BAA10Y_forecasting/01_BAA10Y_multivariate_backtest.ipynb @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "id": "be2f6012", "metadata": {}, "outputs": [], @@ -168,15 +168,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "e189b4fe", "metadata": {}, "outputs": [], "source": [ "# BAA10Y imports\n", "\n", - "from BAA10Y_forecasting import DEFAULT_COVARIATE_SERIES_IDS, build_baa10y_multivariate_service\n", - "\n", + "from BAA10Y_forecasting import (\n", + " DEFAULT_COVARIATE_SERIES_IDS,\n", + " HYOAS_OPTIONAL_COVARIATE_SERIES_IDS,\n", + " build_baa10y_multivariate_service,\n", + ")\n", "from BAA10Y_forecasting.leaderboard import build_leaderboard, build_spread_change_compare_frame\n", "\n", "from BAA10Y_forecasting.plots import (\n", @@ -192,7 +195,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "id": "cecd0b36", "metadata": {}, "outputs": [], @@ -206,7 +209,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "c93b6090", "metadata": {}, "outputs": [ @@ -257,7 +260,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "d8d4a643", "metadata": {}, "outputs": [ @@ -265,17 +268,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Config: 'smoke' → baa10y_smoke.yaml (LLMP on)\n", - "MultiTargetBacktestSpec (spec_id=baa10y_smoke)\n", - " description: Smoke multivariate demo: weekly origins in late 2025 (post-cutoff), forecasting close-to-close cumulative changes at 1/5/21 business days.\n", - " start: 2025-10-06 00:00:00\n", - " end: 2025-11-14 00:00:00\n", + "Config: 'backtest_2025' → baa10y_backtest_2025.yaml (LLMP on)\n", + "MultiTargetBacktestSpec (spec_id=baa10y_backtest_2025)\n", + " description: Main multivariate comparison: weekly origins across 2025 (post-cutoff), forecasting close-to-close cumulative changes at 1/5/21 business days.\n", + " start: 2025-01-06 00:00:00\n", + " end: 2025-12-22 00:00:00\n", " stride: 5\n", " warmup: 250\n", " tasks: 3\n", "\n", "Task: baa10y_change_1b\n", - " description: BAA10Y close-to-close cumulative changes, 1 business day ahead (next-session change / direction).\n", + " description: BAA10Y close-to-close cumulative change, 1 business day ahead (next-session change / direction).\n", " horizons: 1\n", " frequency: B\n", " payload: continuous\n", @@ -287,7 +290,7 @@ " frequency: B\n", "\n", "Task: baa10y_change_5b\n", - " description: BAA10Y close-to-close cumulative changes, 5 business days ahead (forward 1-week change).\n", + " description: BAA10Y close-to-close cumulative change, 5 business days ahead (forward 1-week change).\n", " horizons: 5\n", " frequency: B\n", " payload: continuous\n", @@ -299,7 +302,7 @@ " frequency: B\n", "\n", "Task: baa10y_change_21b\n", - " description: BAA10Y close-to-close cumulative changes, 21 business days ahead (forward 1-month change).\n", + " description: BAA10Y close-to-close cumulative change, 21 business days ahead (forward 1-month change).\n", " horizons: 21\n", " frequency: B\n", " payload: continuous\n", @@ -326,7 +329,7 @@ "# ONLY — pre-cutoff is leaked for LLMs, so the predictors cell\n", "# drops the LLMP rows for this config.\n", "\n", - "EXPERIMENT_CONFIG = \"smoke\"\n", + "EXPERIMENT_CONFIG = \"backtest_2025\"\n", "\n", "_BACKTEST_SPEC_FILES = {\n", " \"smoke\": \"baa10y_smoke.yaml\",\n", @@ -362,13 +365,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "c2e433fb", "metadata": {}, "outputs": [ { "data": { - "image/png": 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Fs+c18vLykJSUJNl2VFSUuJ2aVH2uZ5nns9izZw9uuukmtGjRAl988QUyMjLE+gJ2ux2Ae0z5TTfdhN9++w0vvvgitm3bhj179uDaa68V1wlWTEwMAPjcQHE6nbBarZIA4/7778fevXtx8OBBFBYWYv369WKhsmCcP38eLVq0CLpNHmq1WjKMYeHChXjiiScwZswYfP/999i9ezfef/99APB5/zVtKy8vz+/x6b2fSkpKwPM8pk2bJjk2VCoVsrOzfY6/cKjpOPHH895rKlgYzDHmScOOjIzEnXfeiZSUFFx11VU4dOhQbd+ShFwux/jx43Hs2DHYbDbxNf1VrS8pKRGPw6ysLMyfPx9z586FyWRCaWkpysvLAbinqfP8vz9Tp07F22+/jU2bNuHKK69EYmIiHnvsMfH1i4qKUFxc7LOP77vvPnAch7y8PMhkMmzevBldu3bFQw89hNatW+Oyyy7Djh07AKDGx/0pKSnx+52VnJyM4uJiybKajuf33nsPd955J+bPn4+ePXuiTZs2WLx4sc+2NRqN+L4JIaSu0Zh5QghpBJRKJQYNGoTt27fDYrHAYDBIHrdYLNi+fTsGDRokKcDlr1cvPz8fqamp4t+jR4/G6NGjYTab8dNPP2HatGm4++67sWXLFvFC/rvvvkPr1q19ttW2bVvJ3/6Kvo0ZMwYajQbffPMNRo0ahQMHDmDevHni43FxcZDJZPjtt98kheA8PEW2UlNTfYqemc3moINofwXTCgoKxM9izZo1iI6OxjfffCMWTMvKypKsf/LkSfz1119Yu3atpGp5bS7ODQYDWrdujePHj0uWnzhxAowxyVj6gQMHonv37vjss8/Qpk0baLVaSbGymsTHx+Ps2bMht7GqVatW4aabbpLsv6NHj9ZqW6mpqX6PT+/9FBMTA5lMhmeffVYyxtsjISGhVq9dnZqOE38850lpaSlSUlICrhfMMQYA/fv3x8aNG2Gz2bBt2zY8+eSTuPnmm3Hq1KlQ305QunTpgvz8fJSUlEjGzR8/flw8Ds+cOQOn0+mTfQO4sxkGDBjgU1zTQy6X47HHHsNjjz0mThM4c+ZMJCQk4IUXXkBcXBwSExP9FtsDKm+wdOrUCatWrYLL5cKuXbvw7LPP4sYbb0Rubi4iIiJqfLyquLg4v/s7Pz8fnTp1qvmD8xIdHY2FCxdi4cKFOHToEN555x383//9H3r06CGZnrK0tFRyw5UQQuoS9cwTQkgj8fjjj6O4uNjvHOTz589HcXExHn/8cclyk8mErVu3Sv7+5ZdfMGDAAJ9tREVF4ZZbbsGtt96KY8eOAXAHkXq9HkajEZdddpnPv2AuSiMjI3HDDTfg66+/xtdffy1Wsva45pprALh7j/29hicNu3///li3bp2kevvq1atrfH2Pbdu2SXoft27diuLiYvGzsNlsUKlUkhsSX375pWQbnqDd+6ZDVlYWfv/996Db4e3aa6/F999/L0k7XrlyJWJiYjBo0CDJuvfffz++/PJLfPrpp5g0aZLPDZ3qDB8+HKtXr0ZZWVmt2ulhs9l8brhU/YyC1a9fP2zdulXSpp07d0p6RA0GAwYOHIhjx475PTbS09Nr9drVqek48cdzw8m7Ors/wRxj3nQ6Ha677jo8+OCDOHPmjHjjSq1Wh5wJ4iEIAlatWoXu3buLU0eOHDkScrkc3377rbheSUkJNm/ejOuuuw4A0Lt3b2zbtk3yzzPjxJIlS/DBBx8E9fotW7bEE088gV69eonfM8OHD0dhYSHUarXf/Vz1mFOpVBg6dChmzpwJs9nsc6Oqpsc9Bg8ejC1btkgKfp44cQIHDx7E4MGDg3o//vTs2VP8bDzv0SMzMzOkWQAIIeRCUM88IYQ0EjfddBMefvhhzJkzBzk5OWLP7LfffouPP/4YDz/8sM8c83Fxcbj33nsxd+5cxMTE4LXXXgNjTAz6P/zwQ2RkZGD06NFITU3FmTNn8L///Q8jR44E4O4ZffHFF/HUU0/BaDTiqquugkKhwOnTp/H999/j22+/hV6vr7HtkydPxrhx45CVlYWJEydKsgc6deqEhx56CHfeeSdmzJiBAQMGwOVy4Z9//sG2bduwdu1aAO4pvfr164ebb74Z//d//4fTp0/jrbfeCjrNPjIyEtdeey1mzpyJ0tJSPP300+jfvz9GjRoFwD0mduHChXjkkUcwduxYZGRk4IsvvpBso0uXLmjVqhVmzpwJnudRXl6O2bNno2XLlj6vt3HjRlgsFuzduxcAsG7dOkRGRqJbt27o1q0bAPeUgl9++SUmT56M//u//8OhQ4fw5ptv4pVXXvEJYO688048/fTTKCoq8qkwXpPZs2dj/fr1GDx4MJ566imkpqbi6NGjsFqtPnOIV2fEiBF45513sGjRInTq1An/+9//cPLkyZDa4jFt2jR88MEHuP766zFjxgyUlpZi7ty5SEhIkEwl9+abb2LYsGGYNGkSbr31VsTGxsJoNOLnn3/G3XffHfQMAp4bP0ePHgXP8+Lf/fr1Q1pamrheTceJP23btkVqair27dsnVl73J5hj7Mcff8Snn36KsWPHok2bNjh37hzee+89XHHFFeKx3rVrVyxbtgzr1q1DamoqWrRo4XcYRVZWFqZMmYLJkyejQ4cOKCkpweLFi7F3715J4N6qVSvcd999mDFjBhQKBVq2bIlXX30V0dHR+O9//wvA/V0Q6LO+9NJL0bdv34Dv+7///S9iY2Nx+eWXIzY2Fr///jv+/vtv/N///Z/4udx4440YPXo0nnrqKfTq1QsWiwVHjhzByZMn8cknn+DgwYN44oknMGnSJLRv3x4mkwnz5s1Deno62rdvX+Pj/kybNg2ff/45Ro4cieeeew52ux3PP/882rRp4zM9Yk2uuOIKjB07Fj169IBCocDy5cuhVqslvfIWiwXHjx/H7NmzQ9o2IYTUWsPW3yOEEFLV//73PzZw4EBmMBiYwWBgl19+OVu+fLnPep7KzevXr2ddu3ZlarWa9enTh/3+++/iOrt27WLXX389S01NZWq1mrVp04Y99thjPtXtv/76a9avXz+m0+lYVFQU69OnD3vhhReYy+VijNVctdtut7Po6GgGgO3cudPncUEQ2Hvvvcd69OjB1Go1i4uLYwMHDmQLFiyQrPfdd9+xTp06MY1Gwy699FL2xx9/sOjo6KCq2T/00ENs3rx5LDU1lWm1WnbjjTey3NxcyXqvv/46a9WqFdPr9WzEiBHsn3/+YQDYm2++Ka6ze/du1q9fP6bValnHjh3ZsmXLfKpke14TgM+/qm39/fff2YABA5hGo2GtWrVi8+bNk1Ru9zZy5EjWrVu3at9rIEeOHGE33XQTi4qKYnq9nvXu3ZutWLFCfLzq+2SMsbfffpt5XwqUlZWxqVOnstjYWBYbG8vuv/9+tm7dOp+K5sFsizHGduzYwXr37s3UajXr2rUrW79+PUtPT/eZtWHPnj3suuuuY9HR0Uyn07GOHTuyBx54gOXk5AT9/v3tCwDs888/F9cJ9jjx55FHHmGDBg2SLPN3XtR0jB0/fpyNHz+etW7dmmk0GtaiRQs2depUlpeXJ27DaDSy6667jsXExFQ7m8P58+fZTTfdxFq1asXUajWLiIhgV111ld/K/Ha7nT3xxBMsKSmJ6XQ6Nnz4cHbs2LFq33Ow1eyXLl3KrrjiChYXF8e0Wi3r1q2bpFo8Y4w5HA42d+5c1rFjR6ZWq1liYiK7+uqrxe+2/Px8dscdd7B27doxjUbDkpKS2Pjx48UZHWp6nDHfavaMMfb333+zESNGML1ezyIjI9m4ceNYZmamZJ1gjucZM2awnj17soiICBYVFcWuuOIKn+r83377LTMYDAFnDyGEkHCTMcZYvd05IIQQQohfZrMZLVu2xJw5c/DEE080dHPqxL///osuXbrgs88+w5QpUxq6OSE5ePAg+vTpg9OnT0t6+gnxmDhxIiIjIyUzUxBCSF2iYJ4QQghpQGVlZTh69Cg++OADrFmzBpmZmZJK903ZM888g169eqFFixY4ffo0Xn31VdhsNhw/ftxvwbLGbuzYsWjbti0WLFjQ0E0hjcyZM2fQvXt3HDp0KGDaPyGEhBuNmSeEEEIa0L59+3D11VejdevWWLZsmU8gLwgCBEEI+HyFQuF3loHGwOl04umnn0Z+fj50Oh2uuuoqvPnmmyEF8o3p/b/xxhv4/vvv6+W1SNOSm5uLjz76iAJ5Qki9op55QgghpBGbOnUqli1bFvDxbdu2BV0krilq7u+fEEIICYSCeUIIIaQRy8zMRFFRUcDHO3fuLE7vdzFq7u+fEEIICYSCeUIIIYQQQgghpImR17wKIYQQQgghhBBCGhMK5gkhJMzmzJkDmUwm/ktMTMSwYcOwc+fOsL+OdyGxzMxMzJkzB2fPnpWst337dshkMuzduzesrx+Kqm29EKWlpZDJZFi6dGlYtkcCO3DgAObMmQOr1SpZvnTpUshksmrT3+tbeno6Hn744bBsa+HChY22qGBjUlpaijlz5uDo0aOS5ZmZmZDJZFi9enUDtSyw3r17Y+rUqQ3dDEIICQsK5gkhpA7odDpkZGQgIyMDixcvxvnz53HNNdfg8OHDYXuN++67D9u2bRP/zszMxNy5c32C+b59+yIjIwNdu3YN22uHqmpbSdNw4MABzJ071yeYb4zWrFmDJ598sqGb0ayUlpZi7ty5PsF8amoqMjIyMGzYsAZqGSGENA80NR0hhNQBuVyOyy+/XPy7f//+SE9Px5IlS7Bo0aKwvEarVq3QqlWrGteLioqStKUhBNtWUv8YY3A6ndBoNA3dlAvSp0+fhm4CqaDRaBr8O4cQQpoD6pknhJB60KZNGyQmJuLMmTMA3HNnv/zyy0hPT4dGo0GXLl3w4YcfSp5jNBpxyy23IDk5GVqtFm3btsW0adPEx71T17dv346rr74aANCvXz8xxd/zmHea/VVXXYUbbrjBp42LFi2CTqeDyWQC4A7y3nrrLXTq1AkajQbt2rXD22+/Xav3XzXN3tOmn3/+GbfddhsiIyORlpaGN954w+e5H3/8MdLT06HX63HNNdfg5MmTfl9j6dKl6NWrF7RaLVq2bInnnnsOPM8DAMxmM9LS0jBhwgTJcx544AHEx8f7ZDN485e+vXbtWshkMmRmZgKoTCtetmwZ7r33XkRHRyMuLg7Tp08Hx3E1fj47duzAoEGDoNPpkJCQgHvuuQfFxcXi423btvWbQv7kk0+iVatW4jzsDocDzz77LNLS0qDRaNC1a1d89dVXkudMnToVPXr0wIYNG3DJJZdAo9Fg3bp1PtteunQp7r77bgBAYmIiZDIZ0tPTJevk5OTg2muvhcFgQMeOHbF8+XKf7fz4448YMGAAdDodEhMT8eCDD8JisQT8LJxOJ/R6PT799FNx2fz58yGTyfD++++Lyz788ENER0eL+7jqfvK8z+3bt6NPnz4wGAzo378/9u3bJ3k9s9mMu+66C5GRkUhMTMRTTz3ld59lZWVhwoQJiI6OhsFgwKhRo3Do0CHx8Zdeeglt2rQR/xYEATExMUhKSpJsJzU1FW+++WbA9++PZ1jDX3/9FZbP+8iRIxgyZAi0Wi06duyIL7/8EjfffLNkir/jx4/j1ltvRevWraHX69GtWzfMnz9fPNYyMzPRtm1bAMDEiRPF75zMzEyfNHvPvqhq/fr1kMlkOHHihOS9BjqPQ7Vr1y5ceuml0Gq16NGjBzZu3Oh3ve+++w69e/eGVqtFixYtMH36dNjtdvHx9u3b44UXXhD//vbbbyGTyTBjxgxx2aZNmyCTyVBYWAig8nt29erV6Ny5MyIiIjBs2DCcOnWqVu+FEEL8YoQQQsJq9uzZzGAwSJaZTCamUCjYfffdxxhjbPr06UyhULDZs2ezTZs2sUceeYQBYO+99574nKuvvpp17tyZrVixgm3bto0tW7aMPfLII35fx2Qysffff58BYJ9//jnLyMhgGRkZjDHGtm3bxgCwPXv2MMYYW7JkCVOpVOz8+fOSNg4aNIiNGzdO/PuRRx5hOp2Ovfzyy+znn39mc+fOZSqVii1evFhc58yZMwwAmz17dkifiadN7dq1Y7Nnz2Y///wze+ihhxgAtnHjRnG9devWMQBs6tSp7KeffmKvvPIKS09PF9+nx/z585lCoWBPPvkk27x5M3vnnXdYREQEe/rpp8V1tmzZwmQyGVu+fDljjLENGzYwAGzFihXVtj0tLY099NBDkmVr1qxhANiZM2ckn0OLFi3Etr766qtMqVRK2uDP3r17mVqtZiNHjmTr1q1jn3zyCUtISGD9+/dnHMcxxhibOXMmS05OFv9mjDFBEFirVq3Y9OnTxWU33XQTi4uLY++88w7bvHkze/zxx5lMJmMbNmwQ15kyZQqLjY1l7du3Z59//jnbsmUL+/fff33aVVBQwJ5//nkGgP30008sIyOD7d+/nzHG2Oeff84AsK5du7IFCxawzZs3s4kTJzKZTMaOHj0qbmPVqlVMLpeze++9l23cuJF99tlnLCkpiU2aNKnaz+Sqq65id911l/j3jTfeyLRaLbvlllvEZZMnT2ajR48W/666n6ZMmcLi4+NZz5492ZdffsnWr1/PevbsyVq3bs2cTqe43vjx41lERARbtGgR+/HHH9n111/PWrZsybwvkcxmM0tPT2ft2rVjX331Ffvuu+/YpZdeymJiYlh2djZjjLHt27czAOz06dOMMcb27dvHVCoVk8vl4mdy4sQJBoD98ccf4rYBsClTplT7eYTz87ZaraxFixasW7dubNWqVWzVqlWsR48erFWrVmzo0KHier/88gubNWsW++GHH9i2bdvY22+/zaKioticOXMYY4zZ7Xb23XffMQDs1VdfFb9z7Ha7eD6sWrWKMcbYTz/9xACwQ4cOSd7Xbbfdxvr27Sv+Hcx5HOxnlpeXxyIiItjAgQPZ999/z5YuXcrS0tJYQkKC5Lnff/89k8lkbPLkyWzjxo3s7bffZjqdjo0fP15cZ+rUqWzIkCHi34888gjTarWsf//+4rJnnnmGdenSRfx76NChrGXLlqx///7su+++Y6tWrWKtW7dml19+ebXtJoSQUFAwTwghYeYJXF0uF3O5XOzMmTNs3LhxYlBUWFjIVCoVmzlzpuR5kydPZomJiWLAZjAY2Lvvvlvj63hUDdoDLS8qKmIqlYp99NFH4jqZmZlMJpOJF98nT55kMpmMffjhh5JtPf300ywlJYXxPC8+T6FQsLlz5wb1mVRt04wZM8RlgiCw9PR0du+994rLBgwYwK688krJtl544QVJMG82m1lERAR75plnJOstXryY6XQ6VlRUJC579NFHWXR0NPvrr79Yamoqmzx5crXtZiy0YN5fW/V6PSsuLg64/bFjx7I2bdpIAsxNmzYxAOyHH35gjDH2999/MwBs8+bN4jq//vqrZL9u3bqVAWCbNm2SbH/SpEmsX79+4t9TpkzxCSgD8QSRhYWFfpe///774rLy8nKm1+vZSy+9xBhz78+0tDSfz3jjxo1MJpOxw4cPB3zdWbNmsfT0dHE7sbGx7OGHH2YpKSniOq1atWKvvvqq+Le/YL7q63iOu507dzLGGDty5AiTyWTs008/FdfhOI61bdtWEsy/8847PoHz+fPnmcFgEG+m2Gw2ptFo2NKlSxljjL399tts0KBBrE+fPuINsI8//lj8bvBQKBTsnnvuCfhZMBbez/v9999nCoVCPHYZcx+/CoVCEsx7EwSBuVwu9sorr7DU1FTJ87yD9kDLXS4XS0xMZM8++6y4jsViYREREezNN99kjIV2HgfzmT399NMsMjKSlZaWisu2bNnicyOgT58+bODAgZLnfvjhhwwAO3jwIGOMsc8++4xptVpmt9sZY4xdcskl7OGHH2ZKpZKVlZUxxhi74oor2H/+8x9xG0OHDmUGg4EVFBSIyzz7MScnp9q2E0JIsCjNnhBC6oDFYoFKpYJKpULbtm2xbds2LFq0CKNGjcKff/4Jl8uFiRMnSp4zadIkFBYW4p9//gHgLlz31ltvYfHixQFTy2sjPj4eI0aMwIoVK8RlK1euREREhJh+/8svvwAAxo8fD47jxH/Dhw/HuXPnkJOTAwBIS0sDx3GYNWtWrdoycuRI8f9lMhm6du0Ko9EIAOB5Hvv27cPYsWMlz6maKr9r1y6Ul5dj4sSJPm212WySooOvvfYaUlNTcfnll0Mul0vStsPBX1utVqskHbuqnTt3YsyYMVCpVOKykSNHIiYmBr/99hsAoFevXujWrZtkn61YsQIdO3bEZZddBgDYvHkz4uLiMGzYMMnnMGLECPz111+SVOX4+HgMGDDggt+v9/4zGAxIS0sT998///yDrKws3HLLLZL2DB06FHK5vNrZFYYMGYLMzEwYjUYcOnQIZWVleOqpp1BQUIATJ07g9OnTMBqNGDJkSLXta9GiBbp37y7+3a1bNwAQ27hnzx4wxiT7TaFQ4Oabb5ZsZ+fOnejRo4ekiGRcXBxGjBgh7iOtVot+/fphx44dANxDJ4YOHYqhQ4fi119/FZcNHDgQSmVlySKO4yRDCqoTjs97z5496Nmzp2TIRHp6Oi655BLJa9ntdsyePRsdOnSARqOBSqXCc889h7y8PJSXlwfVXg+lUomJEydi5cqV4rL169fDYrHg1ltvBRDaeRzMZ/bnn3/i6quvRnR0tLhs2LBhiIuLE/8uLy/HgQMHfL5TJk2aBADivh0yZAjsdjt2796N0tJSHDp0CA899BCioqLw+++/w263Y8+ePT7HY+/evZGYmCj+XfX4I4SQC0XBPCGE1AGdToc9e/Zg7969yMzMRFFRER566CEAQElJCQAgOTlZ8hzP356x0itXrsQ111yD5557Dh07dkSXLl3w3XffhaV9kydPxvbt23Hu3DkAwNdff42xY8dCq9UCAIqKisAYQ0JCgnhTQqVSYcSIEQAgBvMXKiYmRvK3Wq0Wx6oWFhaC4zifMcdVPzfP9Gh9+/aVtLVjx44+bdXpdLj55pvhcDhw2223ITY2NizvwyNQW/Py8gI+p6SkxOc9eZ7rPW5+8uTJ+O677+B0OsFxHFavXo3JkyeLjxcVFaG4uFjyGahUKtx3333gOE7SBn+vVxvV7T/Pfhk7dqykPXq9HjzPV3sMDRw4ECqVCr/++it27NiBSy+9FK1bt0aPHj2wY8cO7NixQwyeQ20fALGNeXl5UKlUPsdB1c8n2H00dOhQMZj/7bffMGTIEAwZMkQS4Nd0AyLU9xPq552XlycJMD2qHrtPP/003nzzTdx///3YsGED9uzZg+effx4AJOPJgzV58mScOnUKu3fvBuD+zrnyyivFwpihnMfByMvL83lPgPR9lpaWgjHms2+jo6Oh0WjEfdu+fXu0bNkSO3bswG+//YakpCR06dIFgwcPxo4dO/DHH3/A6XT67Nuajj9CCLlQVM2eEELqgFwuF3tMq/L0DBUUFKBly5bi8vz8fMnjqamp+Oyzz/DJJ59g3759ePnllzFp0iScOHEC7dq1u6D2jRkzBhqNBt988w1GjRqFAwcOYN68eZI2ymQy/Pbbb+IFqLfOnTtf0OsHIzExEUqlEgUFBZLlns/Jw/N5fffdd2jdurXPdjxFugDg4MGDWLBgAfr06YP33nsPd999d41T9mm1WjidTskyzw2ZqgK1NTU1NeD24+LifJ7nea53L+Ktt96KF154AT/99BM0Gg0KCwslwXxcXBwSExOxYcMGv6/jHcTUxxzqnrYvWrTIbxZAixYtAj5Xr9fjsssuw44dO1BSUiIGSUOGDMGvv/4KtVqNAQMG+D02Q5GamgqXy4WSkhJJQO/vGPMu0ua9nvc+GjJkCF555RVs2bIFxcXFuOKKK+BwOJCXl4etW7ciKysLV1555QW1OZBgP+/U1FQcOHDA5/GCggJERkaKf69atQr//e9/8fTTT4vLfvzxx1q374orrkDr1q2xYsUKdO7cGRs3bsTChQt92h/MeRyM1NRUv+eV97KYmBjIZDKf9UwmExwOh8++3bFjB0wmk7gPhwwZgjVr1kCj0SAtLc1vuwkhpC5RME8IIfWsf//+UKlUWLVqlWQ6rW+++QZJSUno1KmTZH25XI5+/frh5Zdfxg8//ICTJ0/6DeZD6fWJjIzEDTfcgK+//hrFxcVITEzE8OHDxcevueYaAMD58+dx44031up9XiiFQoG+fftizZo1kir+ngrZHgMHDoRer4fRaPRJc/fmdDpx5513on///tiyZQuuuOIK3HXXXcjIyJCkPVfVqlUrHDt2TLJs8+bNftf111a9Xo+ePXsG3P7gwYOxdu1azJ8/X2zHzz//jNLSUgwePFhcr0OHDujXrx++/vpraDQa9O7dG126dBEfHz58ON544w2o1Wr06tUr4OuF4kJ6Ert06YJWrVrh9OnTYlZKKIYMGYK1a9fCZDJhypQpANw939OmTYNGo8Ftt90W8jar8vTsr1mzBvfccw8A9/COtWvXStYbPHgwVq9ejRMnTog3skpKSvDLL7/gP//5j7jeoEGDoFQq8dJLL6FPnz6IjIxEZGQkunXrhhdffBFqtbrOpmwL9vPu168fli9fjjNnzogBcmZmJv7++2/J8Waz2SQ3S3ielwzzAEI7PmQyGW699VZ89dVX6NGjB3iel6S3B3seB6t///5YvHgxTCaTmGq/detWSSZFREQEevfujdWrV0vO22+++QYAJJ/HkCFDMGPGDJw/f16c5WHo0KF49tlnIQjCBWVcEEJIbVEwTwgh9SwhIQGPPPII3nzzTWi1Wlx++eXYsGEDvvrqK7z33ntQKBQwmUwYNWoU7rzzTnTu3BlOpxPvvfceYmJi0LdvX7/b7dSpExQKBT777DMolUoolcqA2QGAO+113LhxyMrKwsSJEyUBbadOnfDQQw/hzjvvxIwZMzBgwAC4XC78888/2LZtmxjsZGVloX379pg1a1atx81X57nnnsOYMWNw991349Zbb8W+ffvwxRdfSNaJiYnBiy++iKeeegpGoxFXXXUVFAoFTp8+je+//x7ffvst9Ho9Zs2ahVOnTuHvv/+GWq3G8uXL0bdvX7z88suYM2dOwDZMmDABDz74IObOnYtBgwZhw4YNyMjI8LvuqVOnxLbu378f8+bNw7Rp06pN53/uuecwaNAg3HDDDXjkkUeQn5+PmTNnon///rjuuusk606ePBkvvPAClEolnnvuOcljI0aMwI033ojRo0fjqaeeQq9evWCxWHDkyBGcPHkSn3zySQ2fti9P1sL777+Pm2++ucYbE95kMhkWLFiA2267DRaLBddffz0MBgOysrLw448/4tVXX/W5ceVtyJAheP311yGXy8WgasiQIeJ443AET926dcPYsWPx+OOPw263Iz09HR988IFPJsbdd9+Nt99+G9dffz1efvllaLVavPLKK1AqlXj88cfF9SIiItCnTx/8+uuvmD59uuS9LF68GFdccYU4lMVDJpNhypQpWLp06QW9l2A/77vvvhuvvPIKbrjhBsydOxeAe+rIlJQUyOWVoy9HjBiBjz/+GN26dUNCQgI++OADOBwOyWumpKQgJiYGX3/9Ndq2bQuNRlPtjaTJkyfjzTffxAsvvICRI0ciISFBfCzY8xhwj8GfMmVKtePmH3/8cbz//vu49tprMXPmTJSUlGD27NmIj4+XrDdnzhzcfPPNuOOOO3DHHXfgxIkTePbZZzF+/HjJsT5kyBCUl5dj3759+PzzzwEAffr0gUajQUZGhngzKFQdOnRAWloatmzZUqvnE0KauQYuwEcIIRcdf1PTVcXzPHvxxRdZmzZtmEqlYh07dmRLliwRH7fb7ey+++5jnTt3ZjqdjsXFxbGRI0ey3bt3V/s6S5YsYe3atWNKpVKsxh2oyr3dbmfR0dGS6t7eBEFg7733HuvRowdTq9UsLi6ODRw4kC1YsEBc50KnpqvapjFjxvhU1F6yZAlr3bo102q1bOjQoezPP//0mZqOMca+/vpr1q9fP6bT6VhUVBTr06cPe+GFF5jL5WK///47k8vlks+YMXfFcaVS6dMOby6Xiz355JMsOTmZRUdHs//+97/sq6++8lvN/vPPP2dTpkxhkZGRLCYmhj322GOSKvWBbN++nQ0cOJBpNBoWFxfHpk6d6jN1IGOM5ebmMrlczmQymTglmjeHw8Hmzp3LOnbsyNRqNUtMTGRXX321OB0fY+4q7927d6+xTR5z5sxhrVq1YnK5nKWlpTHGAle5v+SSS3ymDNu8ebNY2dtgMLDu3buzJ554QlJl3B/PdI69e/eWLO/SpQtTKpWsvLxcstxfNfuq77OkpMTn2CkpKWG33347MxgMLD4+nk2fPp29+eabrOolUmZmJhs3bhyLjIxker2ejRgxQqx27u3JJ59kANjatWvFZStWrGAAfCq1l5eXMwA1Tl8Y7s/78OHDbPDgwUytVrO2bduyzz77jF111VXs5ptvFtc5d+4cu/nmm1lkZCRLTk5mTz/9NPv444992rFmzRrWtWtXptFoxHMiUJV7xhjr3LkzA8C++OILv++1uvPYA0FMTccYYzt27GC9e/dmarWade3ala1fv97vZ7Z69WrWq1cvplarWUpKCnv88ceZzWbz2V5iYiKLi4tjgiCIy0aPHs0AsBMnTkjWHTp0KLv++usly/766y8GgG3btk1clpaWFnAWAUIIqYmMMcbq8+YBIYQQcrHJzMxE27ZtsWrVKp/K2IQEsmXLFlx33XU4deqUWAiuIRQXF6Ndu3aYNm0aZs+e3WDtIIQQEhpKsyeEEEIIaQC///47pkyZUu+B/Ouvv47k5GSkp6cjLy8Pb731Fnier3WqOCGEkIZBwTwhhBBCSAOoizoTwZDL5Xj55ZeRm5sLpVKJAQMGYOvWrVSNnRBCmhhKsyeEEEIIIYQQQpoYec2rEEIIIYQQQgghpDGhYJ4QQgghhBBCCGliaMx8GAiCgLNnzyIyMhIymayhm0MIIYQQQgghpIlijKGsrAwtWrSAXB64/52C+TA4e/YsFY0hhBBCCCGEEBI2OTk51c54QsF8GERGRgJwf9hRUVEN3BoCuLMlSkpKEBsbW+3dLNJ40D5rOmhfNT20z5oe2mdNE+23poP2VdPS3PaX2WxG69atxTgzEArmw8CTWh8VFUXBfCMhCAI4jkNUVFSzOOEvBrTPmg7aV00P7bOmh/ZZ00T7remgfdW0NNf9VdMQ7ubzSRBCCCGEEEIIIRcJCuYJIYQQQgghhJAmhoJ5QgghhBBCCCGkiaFgnhBCCCGEEEIIaWIomCeEEEIIIYQQQpoYCuYJIYQQQgghhJAmhoJ5QgghhBBCCCGkiaFgnhBCSK1t27YNpaWlDd0MQgghhJBmh4J5Qgghtfbjjz8iOzu7oZtBCCGEENLsUDBPCCGk1gRBAM/zDd0MQgghhJBmh4J5QgghtcZTME8IIYQQ0iAomCeEEFJrgsAgCEJDN4MQQgghpNmhYJ4QQkitUZo9IYQQQkjDaHLB/Pvvv4/09HRotVoMGDAAu3fvDrjukSNHMH78eKSnp0Mmk2HhwoU+68yZMwcymUzyr0uXLnX4Dggh5OIhCAL1zBNCCCGENIBaBfPZ2dnYuXMnNm3ahP3798PhcIS7XX6tXLkS06dPx+zZs7F//35ccsklGDVqFAoKCvyub7Va0a5dO7z22mtISUkJuN3u3bsjLy9P/Pfbb7/V1VsghJCLCvXME0IIIYQ0DGWwK2ZmZmLx4sVYsWIFjEYjGGPiY2q1GldeeSX+85//YPz48ZDL66bDf8GCBbj//vtx9913AwCWLFmCH3/8EZ999hlmzpzps36/fv3Qr18/APD7uIdSqaw22CeEEOIf9cwTQgghhDSMoIL5Rx99FMuWLcOoUaPw8ssvo3///mjRogV0Oh2Ki4tx+PBh7Ny5E7NmzcLcuXPx+eefi0F0uDidTuzbtw/PPPOMuEwul2P48OHIyMi4oG3/+++/aNGiBbRaLQYOHIh58+ahTZs2Add3OBySbASz2QyALmobE0EQwBgV5mpKaJ81Hd77ihcEcBxH+62Ro/Or6aF91jTRfms6aF81Lc1tfwX7PoMK5g0GA06fPo34+Hifx5KSkjBs2DAMGzYMs2fPxk8//YScnJywB/NFRUXgeR7JycmS5cnJyTh+/HittztgwAAsXboUnTt3Rl5eHubOnYsrr7wShw8fRmRkpN/nzJs3D3PnzvVZXlJSAo7jat0WEj6CIKCsrAyMsTrLFCHhRfus6fDeVw67HSaTCcXFxQ3dLFINOr+aHtpnTRPtt6aD9lXT0tz2V1lZWVDrBRXMz5s3L+gXHj16dNDrNgbXXnut+P+9evXCgAEDkJaWhm+++Qb33nuv3+c888wzmD59uvi32WxG69atERsbi6ioqDpvM6mZIAiQyWSIjY1tFif8xYD2WdPhva8UCgX0ej3i4uIaulmkGnR+NT20z5om2m9NB+2rpqW57S+lMrjR8EGPmfew2WxgjEGv1wMAsrKysGbNGnTt2hWjRo0KdXNBS0hIgEKhQH5+vmR5fn5+WMe7x8TEoFOnTjh58mTAdTQaDTQajc9yuVzeLA6upkImk9E+aWJonzUdnn0lMNZs7pI3dXR+NT20z5om2m9NB+2rpqU57a9g32PIn8SYMWOwfPlyAEBpaSkGDBiA+fPn4+abb8bixYtD3VzQ1Go1Lr30UmzZskVcJggCtmzZgoEDB4btdcrLy3Hq1CmkpqaGbZuEEHKxomr2hBBCCCENI+Rgfv/+/bjyyisBAKtXr0ZycjKysrKwfPlyvPvuu2FvoLfp06fj448/xrJly3Ds2DE8+OCDsFgsYnX7u+66S1Igz+l04sCBAzhw4ACcTidyc3Nx4MABSa/7k08+iV9//RWZmZnYtWsXxo4dC4VCgcmTJ9fpeyGEkIsBBfOEEEIIIQ0j5DR7q9UqFobbvHkzxo0bB7lcjssvvxxZWVlhb6C3SZMmobCwELNmzcK5c+fQu3dv/PTTT2JRvOzsbElKwtmzZ9GnTx/x77feegtvvfUWhg4diu3btwMAjEYjJk+ejPPnzyMxMRGDBw/GH3/8gcTExDp9L4QQcjEQhOZTWZYQQgghpDEJOZjv0KED1q5di7Fjx2LTpk2YNm0aAKCgoKBeir89/PDDePjhh/0+5gnQPdLT08EYq3Z7K1asCFfTCCGk2aGeeUIIIYSQhhFymv2sWbPw5JNPIj09HQMGDBDHq2/evFnSC04IIeTiJzCBeuYJIYQQQhpAyD3zEyZMwODBg5GXl4dLLrlEXH7NNddg3LhxYW0cIYSQxo165gkhhBBCGkbIPfP33HMPDAYD+vTpIxmf3r17d7z++uthbRwhhJDGjdGYeUIIIYSQBhFyML9s2TLYbDaf5TabTZyyjhBCSPNAPfOEEEIIIQ0j6DR7s9kMxhgYYygrK4NWqxUf43keGzZsQFJSUp00khBCSOPEGKNgnhBCCCGkAQQdzMfExEAmk0Emk6FTp04+j8tkMsydOzesjSOEENK4CYIAjqc0e0IIIYSQ+hZ0ML9t2zYwxjBs2DB8++23iIuLEx9Tq9VIS0tDixYt6qSRhBBCGieBUZo9IYQQQkhDCDqYHzp0KADgzJkzaN26taT4HSGEkOaJCZRmTwghhBDSEEKemi4tLQ2lpaXYvXs3CgoKfKoY33XXXWFrHCGEkMZNEHgK5gkhhBBCGkDIwfy6detw++23o7y8HFFRUZDJZOJjMpmMgnlCCGlGBEEAL1AwTwghhBBS30LOlX/iiSdwzz33oLy8HKWlpSgpKRH/FRcX10UbCSGENFKCwCBQATxCCCGEkHoXcjCfm5uLRx99FHq9vi7aQwghpAlhVACPEEIIIaRBhBzMjxo1Cnv37q2LthBCCGliBIGBF6hnnhBCCCGkvoU8Zv7666/HjBkzcPToUfTs2RMqlUry+E033RS2xhFCCGncqGeeEEIIIaRhhBzM33///QCAF1980ecxmUxGF3WEENKMMIGCeUIIIYSQhhByMF91KjpCCCHNl8AYeCqARwghhBBS70IeM08IIYR4MJqajhBCCCGkQQTVM//uu+/iP//5D7RaLd59991q13300UfD0jBCCCGNn8BoajpCCCGEkIYQVDD/9ttv4/bbb4dWq8Xbb78dcD2ZTEbBPCGENCM0Zp4QQgghpGEEFcyfOXPG7/8TQghp3gRGafaEEEIIIQ3hgsbMM8bAGAtXWwghhDQxTKA0e0IIIYSQhlCrYH758uXo2bMndDoddDodevXqhS+++CLcbSOEENLICVQAjxBCCCGkQYQ8Nd2CBQvwwgsv4OGHH8YVV1wBAPjtt9/wwAMPoKioCNOmTQt7IwkhhDROjKamI4QQQghpECEH8++99x4WL16Mu+66S1x20003oXv37pgzZw4F84QQ0owIAg+BCuARQgghhNS7kNPs8/LyMGjQIJ/lgwYNQl5eXlgaRQghpGlgAgMvUM88IYQQQkh9CzmY79ChA7755huf5StXrkTHjh3D0ihCCCFNg8AE6pknhBBCCGkAIafZz507F5MmTcKOHTvEMfO///47tmzZ4jfIJ4QQcvGSQUYF8AghhBBCGkDIPfPjx4/Hn3/+iYSEBKxduxZr165FQkICdu/ejbFjx9ZFGwkhhDRScoWcCuARQgghhDSAkHvmAeDSSy/F//73v3C3hRBCSBMjl8shUM88IYQQQki9q1Uwz/M81qxZg2PHjgEAunXrhjFjxkCprNXmCCGENEGMMcgVSuqZJ4QQQghpACFH30eOHMFNN92Ec+fOoXPnzgCA119/HYmJiVi3bh169OgR9kYSQghpfARBgFKhoJ55QgghhJAGEPKY+fvuuw/du3eH0WjE/v37sX//fuTk5KBXr174z3/+UxdtJIQQ0ggJggCFknrmCSGEEEIaQsg98wcOHMDevXsRGxsrLouNjcUrr7yCfv36hbVxhBBCGi/GGBRKJU1NRwghhBDSAELume/UqRPy8/N9lhcUFKBDhw5haRQhhJDGTxAEKJUqCAL1zBNCCCGE1LeQg/l58+bh0UcfxerVq2E0GmE0GrF69Wo8/vjjeP3112E2m8V/hBBCLl5imj2NmSeEEEIIqXchB/M33HADjh49iltuuQVpaWlIS0vDLbfcgsOHD+PGG29EbGwsYmJiJGn44fT+++8jPT0dWq0WAwYMwO7duwOue+TIEYwfPx7p6emQyWRYuHDhBW+TEEKIG2MMCoUSAo2ZJ4QQQgipdyGPmd+2bVtdtCMoK1euxPTp07FkyRIMGDAACxcuxKhRo3DixAkkJSX5rG+1WtGuXTtMnDgR06ZNC8s2CSGEuLl75hUQOFdDN4UQQgghpNkJOZgfOnRoXbQjKAsWLMD999+Pu+++GwCwZMkS/Pjjj/jss88wc+ZMn/X79esnFuXz93httkkIIcTNPTWdEnaHo6GbQgghhBDS7IQczDcUp9OJffv24ZlnnhGXyeVyDB8+HBkZGfW6TYfDAYfXxaunPoAgCFQIqpEQBAGMMdofTQjts6bDs684joNMLgdA+62xo/Or6aF91jTRfms6aF81Lc1tfwX7PptMMF9UVASe55GcnCxZnpycjOPHj9frNufNm4e5c+f6LC8pKQHHcbVqCwkvQRBQVlYGxhjk8pBLQ5AGQPus6fDsK47jAOa+iVlcXNzQzSLVoPOr6aF91jTRfms6aF81Lc1tf5WVlQW1XpMJ5huTZ555BtOnTxf/NpvNaN26NWJjYxEVFdWALSMegiBAJpMhNja2WZzwFwPaZ02HZ19xHAe5UgW5XI64uLiGbhapBp1fTQ/ts6aJ9lvTQfuqaWlu+0upDC5MbzLBfEJCAhQKhc8c9/n5+UhJSanXbWo0Gmg0Gp/lcrm8WRxcTYVMJqN90sTQPms6ZDKZe3/J5ABktM+aADq/mh7aZ00T7bemg/ZV09Kc9lew7zHkT8Jms8FqtYp/Z2VlYeHChdi8eXOomwqJWq3GpZdeii1btojLBEHAli1bMHDgwEazTUIIaS48d8kJIYQQQkj9C7lnfsyYMRg3bhweeOABlJaWYsCAAVCpVCgqKsKCBQvw4IMP1kU7AQDTp0/HlClTcNlll6F///5YuHAhLBaLWIn+rrvuQsuWLTFv3jwA7gJ3R48eFf8/NzcXBw4cQEREBDp06BDUNgkhhPgnCAJkcjlYQzeEEEIIIaQZCjmY379/P95++20AwOrVq5GcnIy//voL3377LWbNmlWnwfykSZNQWFiIWbNm4dy5c+jduzd++uknsYBddna2JCXh7Nmz6NOnj/j3W2+9hbfeegtDhw7F9u3bg9omIYQQ/xhjkMku/lQ3QgghhJDGKORg3mq1IjIyEgCwefNmjBs3DnK5HJdffjmysrLC3sCqHn74YTz88MN+H/ME6B7p6elgrOY+o+q2SQghxL/KNHvqmyeEEEIIqW8hd6l06NABa9euRU5ODjZt2oSRI0cCAAoKCqiSOyGENCPuNHsaM08IIYQQ0hBCDuZnzZqFJ598Eunp6RgwYIBYKG7z5s2SlHZCCCEXN3fPvJw65gkhhBBCGkDIafYTJkzA4MGDkZeXh0suuURcfs0112Ds2LFhbRwhhJDGizEGORXAI4QQQghpELWaZz4lJcVnHvb+/fuHpUGEEEKaBrFnnhBCCCGE1Luggvlx48Zh6dKliIqKwrhx46pd97vvvgtLwwghhDRuNGaeEEIIIaThBBXMR0dHV1Qsdv8/IYQQQlPTEUIIIYQ0nKCC+c8//9zv/xNCCGm+PFPTyWQyCIIAuZwCe0IIIYSQ+hLylZfNZoPVahX/zsrKwsKFC7F58+awNowQQkjj5hkzL5PLwfN8QzeHEEIIIaRZCTmYHzNmDJYvXw4AKC0tRf/+/TF//nyMGTMGixcvDnsDCSGENE6eMfNyuRyCIDR0cwghhBBCmpWQg/n9+/fjyiuvBACsXr0aKSkpyMrKwvLly/Huu++GvYGEEEIaJ0/PvFyuoJ55QgghhJB6FnIwb7VaERkZCQDYvHkzxo0bB7lcjssvvxxZWVlhbyAhhJDGyTNmvmrPPGM08zypGR0nhBBCyIUJOZjv0KED1q5di5ycHGzatAkjR44EABQUFCAqKirsDSSEENI4McaAijR77575CRMm1FsbPvzwQ2zbtq3eXo+Ez+TJkymgJ4QQQi5AyMH8rFmz8OSTTyI9PR0DBgzAwIEDAbh76fv06RP2BhJCCGmcBEGAXCaHzCvN3uFw4J9//qm3NpjLyiRFWUnTUVhUBLvd3tDNIIQQQpqsoKam8zZhwgQMHjwYeXl5uOSSS8Tl11xzDcaOHRvWxhFCCGm8/KXZG41GcBxXb21wubh6fT0SPjzPw2azQafTNXRTCCGEkCYp5GAeAFJSUpCSkiJZ1r9//7A0iBBCSNPAGANk0jR7o9EIQRDAGINMJqvzNrhcLqqk30TxPA+r1Yq4uLiGbgohhBDSJNUqmN+7dy+++eYbZGdnw+l0Sh777rvvwtIwQgghjZsgCJDLFZBV6ZkH3IGaUlmrn5iQuDjqmW+qeM7dM08IIYSQ2gl5zPyKFSswaNAgHDt2DGvWrIHL5cKRI0ewdetWREdH10UbCSGENEKVafaVY+Yzs3PEx+qDi+NoWrwmihcomCeEEEIuRMjB/Kuvvoq3334b69atg1qtxjvvvIPjx4/jlltuQZs2beqijYQQQhohdyq9XNIzn52TA7VGU28BtsvlomC+ieJ5gYoXEkIIIRcg5GD+1KlTuP766wEAarUaFosFMpkM06ZNw0cffRT2BhJCCGmcBEHwmZou15iL5NRW9RZgc1QAr8nyFMAjhBBCSO2EHMzHxsairKwMANCyZUscPnwYAFBaWkp32AkhpBnxpNkrFAqxZ95ms0Gn19dfMM/z1DPfRAmUZk8IIYRckJCD+SFDhuDnn38GAEycOBGPPfYY7r//fkyePBnXXHNN2BtICCGkcfJOs+d5HmazGVq9QTLvfF3jXC5wHAXzTZFAafaEkAAWLFiA8+fPN3QzCGn0Qi41vGjRItjtdgDAc889B5VKhV27dmH8+PF4/vnnw95AQgghjRPP82IBPEEQYDQaEZeUgrLSkvoL5jkOHPXMN0mCwFMwTwjx6+jRYygtLUV8fHxDN4WQRi3kYN57Pli5XI6ZM2eGtUGEEEKaBneafWXPfG5uLmISU2EpM9dbNXuOpqZrsnieh8VKafaEEF8cz8HlcjV0Mwhp9Go1CTDP81izZg2OHTsGAOjWrRvGjBlTL3MKE0IIaRzcafYyyGTuYD47JweR8cmQZ/5bj2PmaWq6pogxBsYYLNQzTwjxg+N4ulFLSBBCjr6PHDmCm266CefOnUPnzp0BAK+//joSExOxbt069OjRI+yNJIQQ0vgIggBZRTV7QRCQmZWNuI6XiT319YGjeeabJM8+ozR7Qog/lHVFSHBCLoB33333oXv37jAajdi/fz/279+PnJwc9OrVC//5z3/qoo2EEEIaIUEQAK80+5wc95h5uaI+C+BxVACvCaoM5inNnhDii+MpmCckGCH3zB84cAB79+5FbGysuCw2NhavvPIK+vXrF9bGEUIIabwYY5DLKnvmz507h5j4JMhl9dszTxd8TQ/P85DLFdQzTwjxi6c0e0KCEnLPfKdOnZCfn++zvKCgAB06dAhLowghhDR+3j3zHMfBxXFQKJVidfv6wPM8eIF65psaQRCgMxgomCeE+EU3agkJTlDBvNlsFv/NmzcPjz76KFavXg2j0Qij0YjVq1fj8ccfx+uvv17X7SWEENJIuKvZyyCTy5Gfn4+oGPdsJ/U9Zp7S7JsejuOg0+thtVEwTwjxxfE81UMhJAhBpdnHxMRAJpOJfzPGcMstt4jLGGMAgBtvvJFOPEIIaSYYY5DJ5ZDLFMjOzkZcUioA1O+Yec4Fnqfem6ZGEATo9RGw2WjMPCHEF8fR1HSEBCOoYH7btm113Q5CCCFNjHeafVZWFqITkgEA8nrsmed5HjxfPyn9JHx4nodWb4CdeuYJIX7wlGZPSFCCCuaHDh1a1+0ghBDSxAiCAHlFmn3W6Wx0H9oLAMR55+sDx3HgqGe+yeF5Hmq1GjaLuaGbQghphHieCuAREoygxsxnZ2eHtNHc3NxaNYYQQkjTwRgDKuaZz8rOQlxSCoD6TbPnOQ48jZlvcnieh0yhAFhDt4QQ0hhxFMwTEpSggvl+/frhv//9L/bs2RNwHZPJhI8//hg9evTAt99+G7YGEkIIaZwEQYAMMshkcphNpsox8xVT1dUHjuOoVksT5M7qkFMsTwjxi9LsCQlOUGn2R48exSuvvIIRI0ZAq9Xi0ksvRYsWLaDValFSUoKjR4/iyJEj6Nu3L9544w1cd911dd1uQgghDUwQBMjkcsgUcqjVGkQ2QDV7nufBUTDf5HAcB3lFzzxjTFJklxBCKM2ekOAE1TMfHx+PBQsWIC8vD4sWLULHjh1RVFSEf//9FwBw++23Y9++fcjIyKjzQP79999Heno6tFotBgwYgN27d1e7/qpVq9ClSxdotVr07NkTGzZskDw+depU99RKXv9Gjx5dl2+BEEIuCp6p6eQyORJTUsWATC6v7zR7uuBragRBgFwuh1KthtPpbOjmEEIaGQrmCQlOUD3zHjqdDhMmTMCECRPqqj3VWrlyJaZPn44lS5ZgwIABWLhwIUaNGoUTJ04gKSnJZ/1du3Zh8uTJmDdvHm644QZ89dVXuPnmm7F//3706NFDXG/06NH4/PPPxb81Gk29vB9CCGnK3MG8HDK5AgnJqeLyep1nnufA11NKPwkfnuchkyug0WphtVrpd5cQIsFzHFwuCuYJqUlQPfONxYIFC3D//ffj7rvvRrdu3bBkyRLo9Xp89tlnftd/5513MHr0aMyYMQNdu3bFSy+9hL59+2LRokWS9TQaDVJSUsR/sbGx9fF2CCGkSeN5ATK5DJDLEJOYIi6vz2BeLpPTPPNNkCAIkCvkUGu0NNc8IcQHz/NwUc88ITUKqWe+ITmdTuzbtw/PPPOMuEwul2P48OHIyMjw+5yMjAxMnz5dsmzUqFFYu3atZNn27duRlJSE2NhYDBs2DC+//DLi4+MDtsXhcMDhcIh/m83uqXUEQai3ok+keoIggDF2UeyPrKwsCIKAtm3bNnRT6tTFtM/Ched57NmzB5dffnmtnr9//3507NgRkZGRF9SOnTt3YuDAgVAq3T8Znn3FC7y7AB7kiIxPBpi7nJlMrgDHcXW+LwVBgEKpBMfxdNzUoLGdXy6XCzKZO5i3WCz1WjDxzz//xBVXXAHAXRMoISHBb3ZfQ2ts++zgwYNo1aoV4uLiGropjVpj228N7ddff63VFNc8z8PpdIblc9y9eze6d+8Og8EgWV7dvjp06BBatmxJx3sj0tzOrWDfZ5MJ5ouKisDzPJKTkyXLk5OTcfz4cb/POXfunN/1z507J/49evRojBs3Dm3btsWpU6fw7LPP4tprr0VGRgYUCoXf7c6bNw9z5871WV5SUkLjexoJQRBQVlYGxhjk8iaVgOJj06ZNcLk4TJ58a0M3pU5dTPssXAoLC/Hggw/ihx9+8LkICcYXX3yBsWPHSoYV1cZbb83Hc889iw4dOgCo3FcWiwUyXone/fpDLlcAjjIAgIznYDKZUFxcfEGvWxOn0wmVSgWnw1Hnr9XUNbbzq6SkBBB4KBQKnDt3rt4y4goKCvDue++ha9euACrr6owYMaJeXj8UjW2fLVu2HP3792uUn1Vj0tj2W0MSBAEvvDALa9euCel57oCNR1l5WVi+2z9fuhQTJ0xA7969fdoXaF+tWLESV1wxqNY300n4Nbdzq6ysLKj1mkwwX1duvbUyQOrZsyd69eqF9u3bY/v27bjmmmv8PueZZ56R9PibzWa0bt0asbGxiIqKqvM2k5p5CnPFxsY2+RNerdZAJpdf9HeHL6Z9Fi5lZWWw2+34448/MXFi6LVK5AoFDAbDBR87Nrsdubm56N+/P4DKfaXRaCCDGlHJadLXVWuh0+nq/Ji1WCxQa7WQyWQX/flxoRrb+WUwGKBUayFXKqFSqept/9lsNjgdTvH1ZAolnE5nozx+Gts+s9odOF9c0ig/q8akse23hmS1WmGxlId8zHg6xpTK8Hw3MMjhcDh8tlXdvlKqlPXyO0aC19zOLU82ZI3r1XE7wiYhIQEKhQL5+fmS5fn5+UhJSfH7nJSUlJDWB4B27dohISEBJ0+eDBjMazQav8V65HJ5szi4mgqZTHZR7BOO58FxfJN/H8G4WPZZuLhcLqR37IKVq1dj0qRbQn4+z/NhuYNts9lx8PBRjB8/Xlwmk8nAmHt8PKpMK6ZQKOrlzjljDCqVGhzfPM6PC9WYzi/GGOQKBdQaLRwOR721SRAE2Gw28fU4jsf5kpJG8Zn405j2mdlsRnaOsVG0pbFrTPutITkcDlitVjDGAma7+sMqhmzxYbr24TgOhYVFfrcVaF/xvCDOukEaj+Z0bgX7Hmv1SXzxxRe44oor0KJFC2RlZQEAFi5ciO+//742mwuKWq3GpZdeii1btojLBEHAli1bMHDgQL/PGThwoGR9APj5558Drg8ARqMR58+fR2pqasB1CKlPLpcLLs7V0M0gDcDhcCC1dTqYXIWTJ0+G/HyO48NSiI4XeBw5csRnuftCx/cCTVZPU9NxHAeVWkMF8JogdzV7OZRqdzX7+sJxHKw2q+Tv4uKSenv9pqysrAw5RmNDN4M0IZ5z22KxhPQ8T898uK59OJ7HufyCkNvgctG1F2n8Qg7mFy9ejOnTp+O6665DaWmpeMEWExODhQsXhrt9EtOnT8fHH3+MZcuW4dixY3jwwQdhsVhw9913AwDuuusuSYG8xx57DD/99BPmz5+P48ePY86cOdi7dy8efvhhAEB5eTlmzJiBP/74A5mZmdiyZQvGjBmDDh06YNSoUXX6XggJlotzgaPpWZolh8MBpUqN3kNGY8XKb0J+vsvlCktQbYiIRHFxsU8xFk/Kmw+5rF6CeZfLBbVaA4FvHsVwLiaeG0Gqeq5mz3Gc5PVcnIuC+SDZ7DYUFoYWEJHmzXOulZeXh/Q8z+8Hx4Xnd4TjXCgI8djlOJrnnjQNIQfz7733Hj7++GM899xzkpSZyy67DIcOHQpr46qaNGkS3nrrLcyaNQu9e/fGgQMH8NNPP4lF7rKzs5GXlyeuP2jQIHz11Vf46KOPcMkll2D16tVYu3atWAxKoVDg4MGDuOmmm9CpUyfce++9uPTSS7Fz506a85Y0Gu4flPqZ5os0Lp5gvnu/wdi6fTucTmdIz+f4C++Z5zgOMpkcCamtxEwsD4EJ7jT7KmQyRb1Um3X3zKvrbRo8Ej4cx1X0zGvqNZjneR52r9fjOd5djI8EwT20hs43EqzaBvPh7pnnOR4FBYWhtSEMv5+E1IeQx8yfOXMGffr08Vmu0WhCTqOpjYcffljsWa9q+/btPssmTpyIiRMn+l1fp9Nh06ZN4WweIWHHuThKs2+mHA4HFCo1VGo1OvS8DFu3bsXo0aODfj7PcRd8MWKz2aDRapGS1gFHjx6VTJEo8P575uWK+plnnud5qNQa2C3mOn8tEl6C4L4RpNZoUV4P1w4eHMeJ6bMqlQouzoXSUgrma+K5ORcTn4hz586hZcuWDdwi0hR40uxrE8wrVaqwZSVyPA+TqTTkNlDPPGkKQu6Zb9u2LQ4cOOCz/KeffhKneiGEhI+Lc8FFPyjNkt1uh1KlAgD0HToaX4WYah+O+detVis0Wh2S09rj70OHJY8JjPkN5mWy+gnmXS4XlCoVeEqzb3J4nodcrqgI5utvzLznuPT0GHIuDqWlpfX2+k2VxWKBVm9AbGIqcnJyGro5pIm4kDR7tVoDLlw98zwHszm0m74cT2PmSdMQcjA/ffp0PPTQQ1i5ciUYY9i9ezdeeeUVPPPMM3jqqafqoo2kiaEf+tAVFBQETDWlcVvNl9PphEKpBgCkprXH+eJSnD17tsbnZWdnA3BfjNQUVDPGqj1nbTYbVFotWrbtiMOHpUXwmOA/zV4ul1/QMetyuYJ6nxzHQaFUgoH5fby8vJzmnw9CXl5eyEM4LhRfMQOBSlu/BfB8gnmeR7nZLFbPJv6ZTCboDZGISkiGkYrgNRu5ubkhBbTZ2dmSc8k9c4SiVj3zao02bEMMeY6HUqUO6XuO5wW69rrIXKzxScjB/H333YfXX38dzz//PKxWK2677TYsXrwY77zzjmTOdtJ83XvvvQ3dhCbns88+w+7du/0+RgXwmi+HwwGFWi3+3a57Xxw8eLDa55SXl+M///0vAPcFTE3BfH5+PmbOnBnwcZvNBo1Gh5j4JOSdy5NcqAUqgCdXKC6ot3z//v1Y+M47Na7HcRzkCiUCxPLYtGkTVqxYWet2NBcL3n4b+/fvr9fX5HkeMpkcao2u3qvZA5XpvxzHQaOr3zY0RWVlZdAaDIhJTEFm9sV5QUx8zZ+/wG82biBPPPGEpHaV1WpFZExsrXrmVWp12IJpnucRG5+I8+fPh/AcSrO/2Nx3330N3YQ6Uaup6W6//Xb8+++/KC8vx7lz52A0GimAIyKTyUS9HCFyVTM2i3NxYUs1I02L3W6HUlkZzCvVGjgcjmqfYzQaxZ6UYHrm7XY7LNWkOVutVig1GshkMsQlJEt6zN3BvL+eeQV4ofY9KgUFBXA4az7meZ6HQqH0X1Ef7tRgq91e63Y0F+fO5dd4XIWbIAjiPPNWa/0WwAO8euY5F6Jj46kIXg1MJhO0+gjEJaYgm4L5ZqOs3ILCwuALx+XkGCXfJe5gPh7msrKQXtcTzIdriCHP84iOTwzpvfAcDyel2V9UykI8DpuKWgXzHnq9HklJSeFqC7lI2Gw2upsZourmA+dozHyzZbPbofTqmVeoVDWmCRqNRvEChAuiAJ7D4ai2AJnNZoNarQUAJFcUwfMQBAaZ3M+Yebn8gtIj8wsK4AwimHe5XJAp5AFvHlqstnoPUpuiwsLCek+z91SzV2u0sNbz1HSAV8+8i0MUBfM1KisrcwfzyamUZt+MlFutyA9yfnaLxYKSkmLJd67FakNkbBzM5tCCKPdMJRrw4QrmBR6RMfGhBfMCDxdlRV40BEG4aDOwQq5m36dPnwAFj2TQarXo0KEDpk6diquvvjosDSRNC2MMDocDDocDqorCXaRm1QVdNDVd82V3OKBSR4h/K1XqGqfxys7OFodlBFMAz+FwVPsDZ7PZoNJUBvMHDx3GNddcA8B9saMO1DPP175H/Fx+QVA3sDiOg0IR+GfMYrHAZqOe+ZoUFRXBXs8ZDJ6sDrVWC1sDpNl7j5mPjEugYL4GJpMJan0E9BFRMJlMDd0cUk9sNivOBRnMe27yVO2Zj4pNCLlnnuO4igJ44Qum9dFxIffMczwF8xcLjuPgcDjAAhTubcpC7pkfPXo0Tp8+DYPBgKuvvhpXX301IiIicOrUKfTr1w95eXkYPnw4vv/++7poL2nkPL071BsWGp4PHHTR9CjNl8PuEKvZA+5g3u6ovgc1MztHPF74INLsHQ5HtcGU1WqFsqJnvmXbjjh0pLIIniD4/1GUyy+smn1+QUFQQ0vcY+YVQIDfZYvVSt9FNXA6nSgzm+r9c+J5XuyZr+955gHvNHsOhuhYCuZrYDabodEZIJPJ3N9DNHylWbDbbMgvqH0wb7FaERUTh7JajJlXqjXhy0pkQERMHPLy84N+CsfzVK/oIuJ0OiEIF2dRw5B75ouKivDEE0/ghRdekCx/+eWXkZWVhc2bN2P27Nl46aWXMGbMmLA1lDQNni9xuoAODccHrljv4jhwNG6rWbLZ7TCovMfMq2G3Vz+9TnZOjnhjiOdrLoDncDhgs1kD3q222WxQajQAgPjkFsipqJQPVDdmXg7OWftgvrCgMKjMHo7jIJcrEKhEh5XS7Gvk6alqkDHzcgXUGl29BvPu+avVYjYKz3GIiI6jYL4GxaUm6CNSAACxicnIzc1F+/btG7hVpK7ZrDYUFAYXzOfkGKGqUtfFarUhMr4Nik/lhvS67jR7NThX+G4aRcXEIf/w8aDXF3iehjheRDy1hC7GzOGQe+a/+eYbTJ482Wf5rbfeim++cc+BPHnyZJw4ceLCW0eaHM/derprHxqO4wL3zLtcF+WdRFIzu8MBpVcwr1Jrajy38vLyEBkVDSC4YN5ut4MxFjCgslqtUFX0zMvlchiiYsUAkDHB75h5uUIB4QJ65ouKCoM65sVq9gFYbdQzX5OioiLIZLJ6/5w8Y+aVKhWcrvobr8/zPLR6g6QAXmRsPIrO0xSG1SktdRfAA4CYxBQaN99cyGUoDrICfGZ2NpJbtpEG8zYromLjUW6p3Tzz4RozDwCRsfEoCDLLwNMGmmf+4uG5prgYrwlCDua1Wi127drls3zXrl3Qat0XfIIgiP9PmhfPSVLfxZSaumoL4PE8uAsIjEjT5XQ4oFJrxL9VKjXs1fwQMcbgcrnnXgcqxvzVcDHkOVctAYrglVusUHt9n7dI74Bjx44BCNwzL5PLL2hqOrPZHPSY+WqDeaut2s+LuHvmY+ITYa3n2gKCIEAml7uzQepx8hOe56HTG2Dx9MzzAiKiYnC+mIL56pjNZugM7mA+Kj75op2vmUgplSq4gqzZk5NjRHLLNJ+e+ajYeJSXBy6y6o+nAF7YOjJkQGRMHIqKioJ+Cs9TvaKLiXfP/MUm5DT7Rx55BA888AD27duHfv36AQD27NmDTz75BM8++ywA99y+vXv3DmtDSdNAY+Zrp7opxJrSmHmHw4HS0lLExcUFncbkTpWuDAidTqffm4FOpxNqr8ruzYHN7pBUs1fWEMyfP38ekdExKK8oUOUeM19zATwAAYvgWSxWqBMqZy1JatMBhw4fQY8ePSAITLLvPORyRa0LB9ntdmg02hp7ZJxOZ8XUdIqA69hsVghCcJFidceXy+WCQqHw+15D2U51anoN940aV9jPgYKCAsQmpQZ90yNc56FniATgG8vX5bnOcRy0eoNkOkZDVDRONLI0e47jQpri1eVyQan0P02j57elunOlJiavYD42KRWZ2f/6rFNXx2i4eI4rp9MJlUp10RXBClVN5xljDGCAQqkI6pw8e/YsOvYd5FMALyI6FtZqZkzx1y7PmPlwXfswBmh1+pCqmfMCT9MCX0SoZ97L888/j48//hi7d+/Go48+ikcffRS7d+/Gxx9/jOeeew4A8MADD2DdunVhbyxp/GjMfO3wvFBDMN80flBef+Mt3DxuAj77/POgn7N48RJs2rRJ/Pu2227zu94tt9xywe1rapxV0uyVKnW159bBgweR1CodMrnMXeiF52qc792Tth8wmLdaodboxL8TW7bGqdOnAdQwz3wts0kKCwsRm5BU40Xc+PHj3VPTyRUBe3ZtNhucQX4XPffcczh16pTfxz5YvBg///xzUNu58847UR5isScAeP/997Fz586Aj584cQKzZ88Oebs1ycsvQFxiStDf2YHOz1B5CuD5428oX7jwPA+NV888ABgio1FaUlpnr1kbX3zxP3z33XdBrz9//gLs2LHD72PffvstVq1adUHtsVgs0OoMAIC4JP9zze/evRsvvfTyBb1OXbr11lsBAPNee91vhmlzkpWVhSeffLLadex2O1QadVA92owxOJxOaPURku8Sm80KjVYX9I2p4uJi3H//f9zBvEod9o4MBtQ4w4sHz9HUdBeTi7lnvlbzzN9+++3IyMhAcXExiouLkZGRIfmB1+l0lGbfTNGY+drhqxkzzzehnvlyiwV9rxqN3LN5QT/n5OnTkuAnN9e3UA7HccjKygpLG5sSR9U0+xrGzH+9chV6XTECCoXSPd1hENMa2u0OyOTygGn2VpsVak3l97lKrRHngHenSoe3mn1hYSFiEpKq7dlnjOHU6dOwWq2QKxTizYuq7LbgC+CZy8oDBuHmsnKcPn0mqO2UlJpqNZ7YZDZXexOgtLQUJaXhnxIsP9/dMx/sFH6hjDmtDscLYhaCQqmUDM06d+5cWF7D7+tyHLQ6g3jzigEV062V1tlr1oapzIyTQR5zAHCuoDDgMVpeboG5LPQbTN5cLpc4fCcuMQVnz571WSczMxNnQ6gWXp+cTieKKsZ+l5WX468DfzdwixrW+fPnUVZD6rvNZoNaowtqfvaSkhJEREZDoVJLvkvsNhvUGm3QI2nOnTuHUpPJPe2oSgWB1X64lj+GyGiUlpYGtW4wNWdI00HBvB9OpxNGoxHZ2dmSf6R5ozHztVNdNXtPUNYUuFwuxCWmoKAg+Llcc3KMYoDK8zxMJpPPXXybzQar1drsitHYHQ6oJNXsVQF/iAoLC5FtNKJ1hy6QKxRwuVwQgimA53DAEBEVMJi3WW2SMfMKhVLcD4wFHjNf2zoPhYWFiIxLrHZKoPLycvAch+LiYsgVSsgV/jMBXBwX9HeR0+kM+Nk6nS5k+umJDLSd2gTzNruj2osMzzkQbgWFofXMW222oHu2qsPzvHtaQQCaKtPTWSyWOruI5jgOOkOE5LNUKJWNbmysw+5Adgjj0k0mU8Bj1OlyXvANdp7nxP2l1vqfgSAzKxslxY1ruIKHzWaDq+ImpNPpkkyx2RyZzWY4avhutFrd9VIMMTXPz240GhGblAKVWjoUjON5KCqGfwRzThcWFsJut4vDcGSB5h0NVcUlRVRsXNA3JAWBCuBdTCiY9/Lvv//iyiuvhE6nQ1paGtq2bYu2bdsiPT0dbdu2rYs2kiaExszXTnVBV5MaM+90IiYhucYffm95Z3PFH39PZfWqP6CeC++ysrLwNbYJcDqkY+ZVKjUcAeaZ/27NGlwyeARkMhkUSoX7JlCQ1ewNUdHV9MzbJD3z7sDHfTxWN8+8UMsCeAUFBYiIiQerpi/HM41YSUkJZHJ5NZkAsqDTOx3VBvNO5BiDC6wcTqffFOSa2IMI5i3W8E/hVlRYhLik4IN5zuUKywWu95j5qsGh0+WqsxvCroox81bxs2Tif0MZo17X7A4Hco3BT+dlLisLWJTO5eIu+DeZ4zgovIpNavUGmEzSTJGsHGPQvZ71zWazicPVXByHY0ePNXCLGpbZbA74W+Jhq/juN0TF1hgAG41GRCekuIu0et84qjil3OdczTcjCwoK4HA4K272ycNyTgqCAM89gYjomm9MePDVdLKQpudiHjMfcgG8qVOnQqlUYv369UhNTW32BUSIlKNijO/FeLLUpeoq1ruDsqbxg+J0OhEZEweTObh0YLvdjuLzRbDbK4N5z3a8i+14LvTNZjPi4uLC3OrGy+n0nZrO4fDtYWOMYc3a73HXzDcBuHvPPYF8TT3kdrsDhsjogBdatipp9t49856K5FUF6ikPxrn8AkTGtKi2R8YTMJw/fx7aFgmQKxT+L7oYA4L8jaq+Z96J3CB7210uJzJrUenbbreL54E/VqsVNlv4e+ZtNhv0kdFBf2e7KgJtjUZT88rV4AUeMqX72FFrtOLxJwgCeM4dfOp0uuo2USueNHtzSUUqv1ewUV5ejsjIyLC/Zm04HA6cyws+mC8vK4MlwPRfTpfrgmd14LnKTArAPW7eaDQiOjpaXJZrNPr9fmoMvDO7XC4nysvLYDKZJO1vTkwmE5zO6o8JTzAfGROHc/nVB/NZ2TmITkiGUq2Gw1R5k8ATimt1epSVldV4fuUXFMDhsEtu9l0o7ywgfVRs0BXteZpn/qJyMffMhxzMHzhwAPv27UOXLl3qoj2kibPb7dAbImjMfIiqmwJFCJDK3Bi5XC6o1GoIAgNj/nttveXm5kKhUIjHiydot9vtiIiIENfzDuabE65KtXZlgJ75ffv2IT61DQwV88uLafYCX+N873aHA4aomMBp9jYb1NrKoEqhVMJV0cPlPjb99cwran0DKr+gAK1b96h2HU/P/PniYrRpo4RCrvBJ/Wasom8/yJ4dp9MVOJh3OVFUWFBj9XzAXeMipxY98w6HA7ZqvjctFovf1OYLwfM8IJP5pMYGwhgDx3Fh6ZnneQEKjfuzVHml2df1BRfHueeZz694Pc/RYYiMRklJSaMJ5u12O6wWC8rLy4O6gSkIAngX7/d7l3O5qj22gsEg3W50gjuY7969u7isvLwcOr0hqPOkvlmtVvGGH8dxSGvfGceOHcPll1/ewC1rGKUmc43nsdVqhaoimM87eLTadbOyc5DUYxAEjoPZXnFuVVTDBwCNzhBUYdBz+QViNXuZQhH0zdjq8DwPZUVWiSGIGxMe7oxJCuYvFhdzz3zIEUK3bt1CmqeRNC8OhwNaQ0S1PUzEV3Vp9jLImkwGjNPphEKphFanD+qH22g0IjG1lSTNHvD9svUEms0tmK+aaa5Uq/32pny14hv0HjJK/FuhUIqfYTBT0xmiogMWQ3LYpUX4FEqvMfN1MM98QWERomLjq02vLCkpgVqrqxgzr4BM7tsz73Q6oQxyekTP+tWNmY+OjQuqMFtUdCzy8nyLg9XE4XRUexPUYrXBHuZgvqSkBBHRMUFnU3k+43CkwPMcJ2Z1qL2C+boeqsXx7jHzVbMc9BFR4k2ixsDucCKpRWu/heb8YYwhIioGxcXFPo85XYFvVAWtyukYlZAiGdNvsVig1mgrigmGv1DjhbLZbHC5XOIwrhbtu+BwMx43X2oyiYVMA7HZbFCptYiMja8xzT7HmIO4pBR3z7yj8hxWadyZZRqdLqhrgvyCQjidDq+e+QtPs+e4ynoPUSEE8zzPV1u7hTQtF3PPfMjB/Ouvv46nnnoK27dvx/nz52E2myX/SPPmdDqhM0RccEpfc8PxfMApxBhjjWosZ3Xccx2rERXEjz8A5OTkIKFFmxqD+ebaM191r3unuHuUlZXh74MH0aHnpeIyuUIpfpY1zffuHjMfuGeeMelc8gqlSpwDXhCY32r2igtIsz9fVITImPhqL+GKi4sRn9wCJRXBvL/Xs1qt0FRMqRfM+VNTmn1yy7QaC9sxxtyV2V2hzREOAA6Hs9oxrFarFXZ7eIP5wsJCRMXEQ6lSBzWFX1iDea9q9ip1ZZp9nQfzHA+NTg+H3SHZR7rIxhXMOxwOJLZog7y8mmcGcTqdkCuViK1Ifa/K5XLBcYE32KveUI5LSkWWVwZKbm4uYpNSYGhkn6OH5/jyBGitO3TF34cON3CrGo7JZILLVfOYeZVGg6iYOBQWVT/OvCC/ANFxCZIbg1avaU012uB65ouKCqE3RIjBfDgufbyD+WBuTHh4MpHIxcHlckGt1VEwDwDDhw/HH3/8gWuuuQZJSUmIjY1FbGwsYmJiEBsbWxdtJE2I1WaDzhB5wSl9zQ3Pc9X2ZDaNUB5wOZ1QqFRBzUsLAJnZOUhuleaTZu8vmFdrdc0umK8aJstkMp+DYf369ehx+VBpwO01dKGmoNrhcI+ZD9QzX7UQnaIihR8InGYvu4Cp6dzT8amrza4sOl+MhJSWKCkthaKimn3VNHv3MaOFUqUKKi2c41wBp2dzuZyIT2ldYzDvcrmgVKkQGR2D8xXTYAXL4bDDVs14Y4vVCrvNHtYbewUFBYiIjnPXYqhh/CwArzHH4Uiz58UxsUqvMfOebdfVUC3OVXlh7z3dms4Q1aiKtznsdiS2aIPc3Jp75svKyqAzRCAm0X8w7x4zf6Fp9lJxSSnIzql8rZycHMQkpkBfMVyhsfEexuFyudCybUf8888/DdyqhmMyl4ELIs1eqdZCrdXBXs3UlTzPQ2AMcrnCPWTH6/dcUzETilofXLZembkM0bFxsNvtkNXBmPmoIKbZ81AEqsVCmiSXywWtTh/0NKxNScjB/LZt27Bt2zZs3bpV8s+zjDRujz76aJ1u3253QKuPaPAiOJ988glOnDgRlm0tX74ce/fuvaBtTJs2rdrHBV4Qezur8lxEuVwuPPzII3XaS2+1WvHYY48HfLym48fpckKpVEIfZMXY7OwcJLVME3uNAgXzVqsVMXEJAefZnj59uvije/ToUSxZsqTG1/aWlZWFV+fNC+k5wXj++ef9pr3OmPEUrFYr1q1bh/Xr10see+ONN8VpPmva09ffeBOWfPwp+nil2APunvnKNPsggvmoaFitlcF8RkYGVqxY4W5DlUbIFV7V7Hn/afae6vKrVq1CRkZGwNc+e/YsXnvtNfFvjuMqx0hW8+bPF5cgIaUl+IoK2zK53Oeiy1O8yfvisjoup9Mno+iRRx4B4E6zT2jRqsbp6ZxOJ5RKFWITU0Oens7pcMJhdyA/Px8vvfSSz+NWqw2CwIe1ynthYSF00TFQqlRwOpw4deoU3n33XQDAU08/7ZMu7d0z/9VXX2Pnzp21fm2O57x65jVhTbM/evQo3n//A/+vW9FLJzBWMbbbHcxrI6J8ztUtW7Zg3bp1tW5HdWbOnFntcelwOpHYohXyAgztKC4uxqxZswG4e1n1hkhEJaRIess9OBcXMOvj4MGD+PTTT/0+VlhYiFdeeQWAb3ZLTHwS8r3alpeXh8i4JOgifG+K7N+/H4OHDMW0J570eY38/Hy8+uqr4t/vv/8BDh8Of4+5JJjnXNDo9IBMUevpHh96+OFqz8Wly5bhr7/+qtW2A8nJycH8+fPFv999910cP348pG0sX74cf/75J8wmk9/fhqysLLz55lsAKoN5wP11HOi3pKCgADFxCQA8M65498xXBPMa/8H8E088Id6I9dQ5Uak1KC8vlxRc9PbMM8+GdGOf4zgoKm4M6CIiUWoqDep5CqWyxsw2j8cee6zG39qMjAx88cUXQW0v3H755Zc6+y7zdvLkSbz33ns+y6v7nqnq3UXvY/CQoVi58pugX/err77Gjh07JMtmz54j6VRyFz/VX5SZwyEH80OHDq32H2m8nE4ntm/fXqev4XA43Gn2DTxmPjMzK+hUqpqcycyq1bzR3vbs2VPtDz/P8+BrmLvZbrfj582bw36B4C0nJwe//PKz37babLYajx/OxUGhVMEQXfNUNoA7mEts0VrsFayuZz4qLgEmk/8f8D179oo/7kajEcdPhNbj8tdff+FwHUxVdOjIURw6dMhn+eHDh2E0GvHX3wfx198HJY+d+Ocf/PPPPwGr+XpfUtsdTkxfsAyxCcmSdSQ98zUcVw6nu2e+3KtnPj8/H/+ePOWenUIpHXcul1dOF+SuZh+gAJ4g4EwN52FeXh6OeH3u58+fR0R0rM/7rKq4pATxKS3dr6VQVBTck15IWSwWqDRaqFSaoAJDl8slCa68j3eXy4XEFm2QlZVd4zaUShWiE5JDD+ad7qnpioqK8Ofu3T6PW21WaLS6sM41fy7f3TMvk7mn8MvNzcWZzEwAwJ49+/DDD9KLP++e+czsLOzK+KPWr83zgtjz5q9n/kJuWhw/fhz/njrl9zGOr7yw906/NURFo+i8NJg/d+6cpPc5nA4eOlTtVJtOhwMRUbGwWPzv7/Pnz+OPP/8E4B5+pNVHIC7RfzDvcrkCDqM4d+5cwOO6pKQEp06f9vuYuxAmJwZilorATeen9oDRaETvwcPx778nfbaTl5eHw0cqi6vt3rsXpwO85oXwHF+eqV4VSiVS09qFHAwD7u+9Lb/8Uu3v4dFjx7F0WXgDt9zcXJw+kyn+fepMJvbsCa2j4eTpMzhy5CjKysqg1et9Hi8oKMDuvXsAuOt0qCt61iOiYwJmXLjnmE8F4K7r4vk9N5vN0OgNAAC1Tg+zn+P90KFDkmF0OoMBKpUaFqsVCoUCMplvlteuXbtCmv7Wu2feM21qMJ0iofTM79m7t8ZrntzcXGzfUfsboBciNzcXJ+vgvPL3OqfPnPFZbjQa/X43+fPXgQMYMuY2HDh4sOaVK/y6c6dPfZFDFddZHi6XCxqd4aIs0F3rEtlWqxXHjx/HwYMHJf9I45WXlxeW9Mjq2O3uYL6m+UvrmsPpDNsJa7M7LrigD8fx1V64udPsa+5BVWs0+DqEu5WhMhqN4DgOJ0/6XnTl5ubC4XBU+yPoqXgcGROPvCCKzFitVhgio8TjxW63Qy5X+Ow7q9WK6LhElAbYD2VlZvHzNZvNKA0Q9Ady4OChOknht1qtOHTYt8iS3eGA0WhEdo7RJ1got1qRk2OUFA/yx7tScFWennmlSgU+wCwJYlvsDkRExUiCRJfLhVKTuw6KPiJwde9AMy24e+bd04tVd1yXlZXB5PW5u8dwV1buDnSslXgF84oAY+a9e+aDDea918vNzYXT6XQXzHI6kdSyTY0BuqfoXnSAHtLqOBzuYN5msyHXmOvz3q1WG6JiYsNa0T6/wD1mHgAgk6GsrAz2inORFwR88+23kvU9AbbT6YTd7vB7bAeLFyovsNUaLSzW8PXMZ2Zni9uriuN4yCt6410ul9gzb4iMxvliabDicDjqrBfHZrVVuy8dTgf0UVGwBaiTYLPZcPZsLgRBqAzmk1KRY/QfzAcaRmG1WuEIUAjN4XBUWyQtKqYyA8tud0Cp1kAbEYXzVTIczGYzDNFxfn+TzWaz5DvAaMxFfphuxHvzHA8ul8t901mhRHKbDjhSiyJ45eXlMERG4asVgX+Ly8vKsXff3rAO3XCfn5X70eFwhjzu32q1IjMnB06nEyqV7++L1WoVp+G0ePWsR8XEBxw6l5OTg6h49w1l7xlX3DcM3L8fWp0eZrPvNZDT5RLPA08ND3fPvAVyucInoHa5XCgoyK/2eqoq7yE9gHsaykA1YrwpFMqAGZNV2ay2Gn8frFYrjh0Lf6dBMGx2e8DOkHAym82w2Xy/a8xmM5xBFhPMy8tDu66XICeEG6nHjh3zudFtNpslN304joNGr2/w+KQuhBzMFxYW4oYbbkBkZCS6d++OPn36SP6RxstoNEIQBJ+xpeFkc9ihM0Q2+J0vh9MZtoteu92OkgsO5l3V3hDgeSHg1HQAAOZuR6ful2Df/r+CGntWGzk5RsQmJuPoUd9paDw/VNX2mFXEH1Gx8civIZg3mUzQRUS6x+tWHC9Wqw36iEif1yi3WBEdnyi56PNwVVwMeD7fUpMp5JsvR44eg6UOPlObzYaDflJG7XY7cnJycDY3F2dzpXNJ26w2ZGbnuIN5PxdbMplMnEM+UBqiQqkQn1/TTSJnRZq9xSvNnuM4mEymiiDB4PMcT5jJBGlxPLGNcgUEXoDVZqu2Z8NkMsFskgbzhopg3l9vu4fZZEJ8RU+QXKH0u67NZoNSow2+UrvLKan14TneHQ4HXC4XImPiajyu3LM5qBCXFHow76q4AWm1WmG1WnyCAJvNisjomLAG8wUFBYiM9dw8YTCZTGKwoNZooI2IkQQ73mn2drsDJ44fq/WwH8G7AJ5GKx5/nnP/Qn5DsrONsAXIYOB4zn0DSOkuEqlQVvTMR0ajuErPo90RvpvCVVlt1mqzLDiOg04XEXB/W61WuJxOFBYWwmw2Q603ICrOf3EvF8cFPAdsNhvsAQJ9z7EfSFxS5XASu90OlUrt96ZISakJOkOk33uPZrMZ5orzShAE5OedRX5+8L2uwfIcXy6XS7zpnJreAX8fCj2YN5lMaNe5G/KLigLOcFFWXoZLh47G9z/8cEHtrvq63vvR6XSGHBxarTbkZOeI+8JfrZHzRUXid5EnmI+IjgvY85yZnY3oxBQA7hR5zzBLk8kk9sxrdAaU+fmN5VycJJiPiImDUqWC1WqFXOk+T71/Q/Ly8iAIQki/8S6XS/JbGRlb8zBAQRAqXrvm2i+MMdjtNQfzFqsN5wsLQq6nEg71FcybTCa/NbNKSk3guJo7ExljcDpdiEtugfz8/KBfMz/vLMot1QfzLpcLWq0+4A3SpizkYP7xxx9HaWkp/vzzT+h0Ovz0009YtmwZOnbsiB/C+KVFws/zRVPbwlTBcIg98w2bZu90usJ20etwOC74S9DFcdX2/HqCM7+Yu6CYp6exR/8h+PHHDRfUnkAys7PRo/8QHDjoG4DmVExDFMy+jYyJrfHH0mg0uqeyUVWm5VltNugjo/yMmbchKjYBZrPvD7jnc/XcqS8tNaGsLPj9JQgCiooKxUJY4aRSqXHmtG/KmdPpFAP2qsGCzWZDTk5ORc+6bzCvUqnhdDorUrr9t9mT3aBUqQLOkuDdFkNktCT4cTqdMJeZK9Ieq+mZDzA1nVyhAC/wNQYDJpN0XxUWFkIf5Q4uFcrAKY6CIMAQFV2xnv8x81arFSp1cMG8p2qx9x37qse7TCaDUlX9+HtPAbzYpFTx+cHieR4Op0P83qp6YWiz2RAZHRvWNPvCwkJExbp75hlzX3C5xJ5Yht5DRkkygbzT7B0OBzieD/qCqyr3MJLKqemsXj2nwIX1zBuNxoDf/zznvgmmUqlhsVgkPfNV04idLmedDRmz26rvmQcD1FptwGJN3seJO505wl39W/D9jXe5nAHT7C1Wa8Ded4fDAWfFdG7+eA8nsVd8X7mDeWnPfKnJBJ0hAoBv8Gg2V2ZVFRYWQq3RIL8w/D3zVqutsnhnxdtJTWtfqzR7z/di7ytHYtXqb/2u43A4MGD4jVj97Xdhq3Pj7t2s3Fculwvn8s6GNCTFarMiJyfb/X2mVPmtNQK4M5OsVpsYzOuiYwL+pmdnu3/LAYj1NzztVevc+12rN/jtTXe5XOJ3WmFhIQxRsVCq1bBaLVD46Zn3HG+hZNJ518YAgMjomovgeW6WVy0A64/T6YQgCMjMrn4YlsVqRXRcgt/OkrrmsDvrpYBwSanJ73d3aakJriB65ouLixEZHQNFRV2TYDofjx49iui4BPE3BHD/plvKyyQZog6HExq9gXrmAWDr1q1YsGABLrvsMsjlcqSlpeGOO+7AG2+8gXl1UECKhE9mxbi4Og3mnY0lmHfCGtZg/sJ65nmer/aLVOA5CH6CLp7nIZPLoVAq3GOA1Rr0GToaK1evvqD2BJKTY0TP/lfiyFHf3orM7BzIZLJq963nZy8iOq7Gu89GoxExCe55aZ1imr0Nhsho33nmrVZExyf4/Qw9yyp75s0oDyEFLzs7GwkpLQHIwp61IlcooNJofQuJuVw4cuQIdBER0BoiJI/zPIezeWfdwbzaN5hXVqSNcxwHRYB51D1T0ylV6hrT7AVBgFKllvRAeG4+eY959FbzmHn3PPN2h7PanvlSkxlWS7n4uZ/LL0CkV8+8v+cKgiAWSVKp1JDLFQGr2Ss1mqCCebGCutd6/o732MRk5FbJpPDm6ZmPio2vcSonb4yxisJRzoq5ndU+wbzdZoMhKrw981Vv1pSaTHBWTFfFGNC170Ds2pVROc2hV8+8w+FAeqdutUpTBgBBqEx9dafZS6emu5Ax80VFRbDaAvfMu6tua2CxWMQ26CIixB5iD6cj8HSFF0IQBNjt9hr3pUqjDTgdoec4ycnJQUmpScygiYlP8Okt5lxcwM/TYrEGfMxzM867toA374J7nu8rQ2QUSqr0zJtMZugMEX7Tm0tKTbBZLeA4DkajEW27dEdhQV30zFuhM0RKvlO0Oj3sTmfIx5onY+mSQddg3fofA/5uRMXGQx8VF7aCfsWlJklbnU4nWrXtgH///TfobdisNhQWFECnN7in0azy3t03Qd3fP1arFWqte2q5iOjA87O7b8xXZErJFRCY+/NwZ2R4gnk9ysp8e+ZdXGXHS35BAfTRcVCpKs5NhW/PvOe1Qg3mvY9fQxCdDe4bAArIfOaU8eX5zLKza06zT+/c84KGJ9WW3WH3m9kYbiaT2e93pslshiuIIQtGoxGxie5jKTrIaQQPHzmC9C49xd8QwP39yPO8JEPUxbmgoanp3CwWC5KSkgAAsbGVJ0TPnj2xf//+8LaOhFVWjhFqjaZOp9pwj5mPbPBqkU6nU3KX7kI4nA6Y/Iz1CgVfU8+84D/NnuMqUkIVSlgsFihVavcdcLk6pB/wYOWezUXLdp1gMpl9bvpk5xiRkJwa1BdhMPOMZ+fkIDI+WTJ3utVqhz7Cd5iG1WZFRJT/8cKez1UM6s2mkIL5o0ePIiWtIzQ6fVDj6ILluRHTIr2D5E48YwxRsbE4euQw4hJTEVdlbmi5QgGn01WRtqrx2a47jdFRMd7Xf5q9XKEQp3irzc07p9OF8opgXquP8F2h4q5N9WPmK3rmqw3mTRAEQfzczxVUBvNVL+I8ysrKoK+4QNQZIqBQKv0G/u5gRyve/Kj+/Tqh1enF4R6A+3iPT0px14moWBZo6i/v7bjbIwcgC/q71uVyQW8wwOlwwGq1IqllGrKr9OxzHA+t3hC2YJ4xBoExcWpBpUqFwqIisU4A4N4HXS4diE2bNont9LxPh8OB1h26+R1GEgx3AbyKnnmtFjavMfMara7W79PpdLp/5wL0AnkCU5W6ome+IrvF31ANp9NVJxd+dnFYUeAsCwb3UIdAr2+1WpHcMg1Z2TkVPd/umzKxSS18jtGq9SC8WSrS9f3xDuaVCt8sIPf0dO7j1F7xfaOPikZJaZVgvuKmkd4Q6XNj01MHpaysDEajES3bdfE7A8iFslltMEREVKTZV0pt0zbkKercNzkjoTNEICWtPf6sKETo4R3cXzJ0NL5euepCmi4qlWTOuI/1lu27hnRDzWazIaVla+gMkVBWZHl5s1htSGqZhpycHFhtVrEAXlRMfMBgvqSkBIbI6MoFFR+wd0aGRmdAuaX6NPtz+e5hPwpPmn1FcVPv79HMrGy0bNsx4Mw2/lQtJquPiqtxGGDVcfbVsdlsaNmmbY3ZWFarDeldeuBgiHUOwsHucKCsHoL5UpPJ780xk8kUVP0Bo9GI6ER3/YWYpOp/bz3+PnQY6Z17wOZ1A7esrAwJyako8MrycTpd0OoNFMwDQOfOncUpvy655BJ8+OGHyM3NxZIlS5Camhr2BpLwyTUakdyiVZ0G855q9oFS+uqL993eC2W3Oy4oPclTp6D6MfO83555T9VdhcLdM+/pie191WisCNMFggdjDE6HEyq1GomprZBZUdXaI9doREqrNgG/CKsWZKupRzQrOwdxSamSecptNhv0Eb5p9raKqrr+shU9Rdo8F4UmkxlqrTbosa4HDx9BSlp76AwRYU1Dcw+L0CAprQOOeAXzLpcLOr0BkVHRiE5MRXR8iuQigDEgIioGeXl5ftPsPZ+re47sGnrmlaoaq9l70gi9P1qXy4Xy8vKKsbh+gnnPcwXmd555uUIBgefhsNurTa0rNZlgiKi8wC8oqEz7Vij8B/MlJSXQV6TY6yMioVAoxJsH3sotFqi1OiiC6Jl3Op3QGaQ/8rlGI1Jbp1Usc386UQkp1V6wuVwu8RyNiU8MOJ7W7+tHRMLhcKDcYkVK67ZiJpWHOxtBG7Y0+7KyMui8si5UajUKCwvBuTixVwoA+gwdja8qUu290+ztDgfSOnfHoVqMOQYqe8iBijR7W2U1e60+otY3hHNzcxGbmBwwOZarSLNXVqTZyyVBqjQ7x+F0XvD87P549mFNv1FyuW/GiYfFYkFy67bIzjHCZDJDXxHMR/mZSYHjOMj9nKeetjgDDIXxZABxHAe5nyE9cUmpYpEqu91do0OrM/jUdCkzV/TMG3xTrU0mMwwRkSgrK0NmVjZik1rU+J1VG1arFbqISJ/vlKTWHUJOe3anj7vPnd5Dr8VXK1ZKHrdYLNDq3JXiu/S5HH/88UdYrkfMZrNk2JKLc6FVhy44GEJPr91uR3KrdOgiIqFUqXyGQZVbyt3fP9k5Fb9h7p75yNg4SWDk4XQ6oVCpJL8DnnPPO/NHq9NLZkzxfg+e8yG/oABRMfFQqtTuYF7u2zOfmZ2DFukdQuplrtozHxkTi3M19Pi6nyMPIsnefR7HJ6WgpKT6m1BWqxWt23fBqQAzbdQlh90RUidHbZnNZr9TU5vMpqBij8ysbETHu4dsVL02CuTff/9Fm47dJB14JpMJqa3SUFRYWbTRXc1e3+A1vepCyMH8Y489hry8PADA7NmzsXHjRrRp0wbvvvuuZK5Q0viUl5cjIjK6TtPsnRXBfKDKufXF6XQGHGsY8rYcDpT5GasdLM8XWHUV1gWeB8/7XsB4eublSqWYZg8A3S+7Att+3R7WOadLSkrEccgpadIqv4wxOBwOv4G2d1tDKTKTnVM5zs7zg2mzu8fM26qMU7XaKsfuVR1/aDabEZeUitLSirHzZjMSk4NPwzt0+DBapHeE3hAZ9mBeo9GhRXpHScVhd4+5BvFJqYhOSEZ0YopY0d49Bl2GuKQUnDp1ym+avco7mPfTWwa4g4DKavY1/IBWfJwyWeW4c5fLBZvVgsKi8/575iumMhOYEKAAntw9Zr5ibH8gZrMZ8cmpkjGznoJsgXrmS0pKYIhwH6davcHde+MnE8RSMd5TqQwuzV6nN4jfW1WPd88hF5uYUu1c806nU9wnsTX04nuz2+3QaLQQmACL1YLk1um+lXwZg1KjDdtNSu/x8gCgUmlQVFgIZ0XasedGUVKLNrA5OGRmZkp65p0OB1LatENWVlatXl/gBcgVnjHzOknPvFavr/VY9co0Tf+X4XxFL51SHDPv1WMXESEJNj0ZCOHm2YeB9iXHcX4zXrxZrDb3cWLMgclsgtZQkWbv5xit7safNYie+UDfNZExcSg6XySuq1SpK6Y5lK7nyfDR6X175k1mE+KTU2EymdzZMMmpUKpUYb/gttps0BsifH4z3UXwQusp9e5xbte1F46d+EdSsNJsNos3yhQKBbpedgV++umnC3sDqEhVdlW23+V0Ia1jNxwOoWdeEATEJqdCGzDN3n1cZefkuG+ie6rZx8b7Hf7guXnmT2nF8AoAUGt1PkUpBUGAwPOVY+YrbuYqvdLs5VVu6uYajWjZtiNKQ+yZlykqz6eomHgU1DCUw92br0T1k6S6Wa1WqDRaKJSqaq/JrDYbtAYDlBptvYxf92Z3OFAeQi2h2jKbzX4/g7KysqDS7N2dPBXBfGJyzXUILBZAJoc+IlIytMpsNiMqLkHyHetyudwZeA0cn9SFkIP5O+64A1OnTgUAXHrppcjKysKePXuQk5ODSZMmhbt9JEwsFgvUGi3kXsVE/vnnH2zYIC2ktmHDBvz7778oKirCF1/4zpF69uxZfPNN4OlYHA4HtPoIcQy0P9+tWYM77pqKbdu21fLdAIsWLRL//8iRI/jll18kj7uczmorVubk5GDt2rVBvZbn4tbbvNffwJ1T7w7qQrYymK+uZ54Dx/t+0Xl6RDxp9gqlO7hTqtTo0PMy7NixAzabDR9++GHAbb/zzjvi/x84cAC//vqr3/XcF8HuL9GUKhc4ZrMZWkMElCpVwItbT/Evj8ho32A+Ozsb31ZMd3UuLw8x8e4hO56rP5vN7i7G5lMUzl1VV6PT+aTCm81mxCamwFRxw6W8vBxxiclB/VgyxpB39ixiE5Oh0Ruqfc57770n6SWz2+348MOPJOscO3YMmzdvBlD5A5/SWprG6akyH5vcEgkpLSsqn2dXvE/3xVNMYgss/+J/UFX0iniTjJkPVABP6S6Ap1JrAvZybd26FQcPHhQvVTS6yjnMPb3pubm50EX4BvOeXnMhUM98Rcqys4Y0e7PZjPikVPECv7y8DNqKHq9AY+ZLSkqgi4gCABiiYt3fa35SpD2VmINNs9cbIuCoCCDdMy1E+AQV8cktAs67DXjmmXefo5EhzDUv1kdg7ovpxNTWksJyjDEw5h5DXTUAzM7Oxl1334M77pqKO+6aiqn33BtUmrKncrSHSq3G+aIi95zkFVPsefQaPAJrvv/enW5dccHqcDqg1mihj4zyWx+juLgYX3zxP8myP/74Q0xJ9k5jVWu1Yo0T942ViIDB3CeffBLwsf99+RVemfcaopNagDH357Zo0SLJDUCe56FQKqFUq8WAwUMf5S6C9+GHH1YUmXTWqlhSeXk57r73Psx4eqbfxz3nmb+eSsB9PKoqbuQFqp1mtVoRn9wCWZmZ2L93r5jmHJeY4jNvPMdxUKlUfnv5rVabWCehKrvdDo6TZmp4k8lk4k1AZ8VNSsD33PW8H42fImhmcxniK8ZA5xjd2VpRsTUXKKvq33//FYeD+GOz2SrHzHt9pi3bdsDWrVvxwP89FHTNlFKvGgUymQw9L79aEqx7fi89+gwZhdXfrQ3p/fhTVqVnnhd4RETHwmwuk3z/ffLpZ7hjylT8/ffffrcTm9wS2ogY8Vz2ZrW6M4P+2rcPWZlnxDR7nSHSZ9/9/PMveHz6E4hNbCF9gYpzz5ORAVTM717l8/UcI57vNE+HglKlgtVi9Ts1XVl5GWITU4L6fec4DkuWLHEfv3Lvjob4Gqc/FATBfcwH0TXv+d2OS0r1yWj05v5N0qFFWnufWQjy8/MDXls7nU589NFHfh8DpNd4gTgdDghMCHhz3eFwYMmSJTVux5/XXn8Td069G9nZ2RX7xfeaoMxsBl9xjet9/V5VjtGI2Ir6C/FJLXzqEOzevVsypPv48eNITWsPtcY9VMtqteKTTz6pHCJYMfsP4L6u0ehoajq/9Ho9+vbti4SEhHC0h9QRnU6HGS/Pl/RgnTlzBmu+l85AsOO33/HXX3/h2LFj+P6HdT7bOXToENb/uDHg63jmQq+ueuvmn7dAn9QGe/fVvsbCF198Ib7Gn3/uxvYdOyWPu1xctT3z2dnZ+HXHjqBeixd4yLx+CHJycvBbxp/QRCf5nY+9Kncvi6zaivgCLwTuma8o8OUeM195gZ3athOOHT+BzMxMrAzwIyAIAj766CPxC/z3XRnY+fsuv+u6x73FAABapHfEkSNHJY9FRMdUW827agBgiPYtMvP333/jx42bIAgCuIqLaqDy99JuD5Bmb7NBrdVBo9X5pBmXlJoQl5Qqfr6MMegigutlP3fuHGLikyCTyaDVB06zP378OBYtWiR5P4WFhXj77QViphIA7NmzB79n/FHZ5op5ziFTiDchPIHbzfc+hvQuPSWpqp4A9MqbJuHeWQtxzYSpPm3x7IPqe+Yr55kXAmTi/LJ1G3bu/E3cB1pt5Y0Sp8sJpVIFo9EInZ+eeYXKXeeAVTNmXuAFOJyOajMDysrKEJvk7pn3BKyemwPyANXsS0pKoIlwp27e/vgLiE1McWcCVHmfJpO7SJU8iJ55p9MJnb4yo8hoNCIuMVVM9/QEMkkt2yAnJ9en6rn3duQV50BsQvW9+N48N3gY3GOYNTq9pJKv+5hRuQvFVZl+Z8eOHYht3RkjpjyOEVMeh4Mpg7qJ4K4cXRnMK1Vq2GxWseK30qsnNz65BfILCsU0RXfPvBNKlRot0jv6TVPOzc3FW/PfkqRc79j5G/bt/wtAZU0JwJ1mb7d5jZnXGwKm2f+4YUPAz3/jpk244Z7pGDD8RiiV7hTi//3vS8kNQK7iJkJlmr1XYawIdzD/1ddfo6ysDA6nU1JHIViFhYUwWR3Yu2+f38dtNhuUSpWkYJM3z404AJDJfCvAA545wHV47I1P8Ngbn4i1Jryni/NwD13Q+z0PrDZbwAt8u8MBrpoCeIA7O8Zqtbqr2VfcgDBERkl74Cuu7dX6CJ+e+TKzGbEVwfz58+cRER3r90ZwTf7991/8FuC3DQBsVqv7HHc4JEU79RFReOS1j3A2vyioYlsAUFqlcGRCyzZidhUg7ZkHgITUVsgvqN2sD97Ky8uh1njVUan44Uxu2QZnzlTOmrJ+wwbok9r4HUvPAFwy8CoMvelWKJS+afZWq7vQ5uNvfYbpC5aK3wMymQwCY5Jru78PHkT3waMw/JZ7JNvw/D6YyyqDea/miipr5bjPA88NPrmqopq9UinpgPJ0SukMEUGl2dvtdqxfv959DeZ1DRcVG4eiIArgyeTBp9mrNFp06D0A69b/WM167t/3pDYdfLIpzpw5gy1bt/p9Xnl5ecDOJ7vdjiVLltT4G+dwOBAdG+93RgHAPU3pypWBO+qq8/OWX6BPaIXDhw/D4XSINyK9WS0WsY7Jl19+FXB65cLCAjFjLC4pBTlVvssO/P03/ty9W/z76NGjSG7TwZ35YbPh3Llz+GbVanGGj4joGPH3wulyVgTzlGYPnufx6aef4rbbbsPw4cMxbNgwyT/SOMnlckTHxkl6sHie97k7aDKZcSYzC0aj0eckAtwpMDnGatJMXe70zOq+AI25RrTt2vOCKmuWlZeLF2iZ2dnIr5Iy5XI5A1YBBtxfgEFPN8ekPQ2rVn+LPkNGIyouMeAXozeO43wvbqrged5v0FXZM18xZl5d+UMel5SKrOxsGI1G5OXl+Q18LBZ3lWDP2F33Z+X/gqWsrAyaisAtOi4B+fn54g+3++IkAkqVJmAaWdUAQO9nXtqs7Gzk5OSgsLAQ0V4pvjKZTKzwrI+M8kmxtdvcF7dqjW9hrFKTCXFJlXfqGQCtPrhg/siRI0hJ6wDAfaEZ6DkrvlmF6Lh4yfgts9kMpUolmZroTFa2eFx5z9GbmtZOrDXimY/Zk5IaHZ8k7h/PDQClUoWY+ES/P4qeokXV9swrFGLaKxcgmM/OzsGJkyfFuezVWu+eeRciY2LdPfN+pqar7JkPVM2+Ymo6e/U98y6nC/qoGJhMJpSWlsIQUflaygBj5s8Xl0BfkWbvqSngrxffM16zumwSsR2eILXijr3RaER0QgqUKg3KysrEFGWZTIZeVwwLOHe0JM0+KRXZQQbz7p5L97ntqQ8RHZcgnj9WqxUajQ5qjRblVmlv7t+Hj6Bt1174f/beO06Sss4ff1dXru6u7umZ7kk9MxvZvMsGWBaQXYJEySIgyMkh3CkgCh4nv1MU8Ht46mE6PTwMJyqiiKcCgiBJWJe0u8DC5jg5dk7V1eH3R4Wp6q7q7gkb6ffrteJ0qH6q6qnn+YT35/3xNvrhbfRDrGCwGaEoRzfof1M0o1x/gigLzNEMCymjsEFYjkdGUloxORwO+DtmWaozS5IESZLw5z+PB3/3d/foz0e+MJ7t1UQdtWuhGFz2wm92ugHDQ8NonzUXJEkq2f5UCvF4zLQO5VXHlKLMrekAgHeJGB4exvDQkBIwy8qTKmVKp9NwukU4CNIysJ1Op+H22rcZNGbmaYa1pOOn1HnidHt0Rx4ABLeIaDRi+mwRRTAsZ3lN0+m0Lc0+rWbmlXIv67WGUdkiWUMrTcEt6ga0Ym8oawTLl7OfMpkMXB4fQqEQCAcJgiDgnIQzn0qlKpaySVkJrKDUy5aei9PtQVNbsGYmTdSg0g4oWeuI4byi0ShYftyZJwiipgxvNWjPnOHAAIDmTnNZXCwWQ0vHzLLrkc/nQRCEXotO0daZeYbl4BK9cIle03ucYNZDSGcy8PjK9ym9fWpWNum+EIYsKTDuzCeSKUXjRT0OSdHjjr1hH+jr64Mv0KqwBGrY37PZrM4uMd5zRXG/WLHktFIAqxTJZBIUw2HpmtPx52eesT1uJq08s20z5+Cdkva/8Xjcdv5ms1nEbESY+/r6UCgUKnZZAZTAnKeh0dYOjcfjGBwarFgWZwUtCB8IzlA7W5TbA0VVaFXvhiJnLZ81ba3Q5rhT9JbpECSTKfT29ut/v7NFKZOkGRaZTAbxeBz9fb2IRCJgBCdE7zjLR9m/hIrM4aMVk6qZv+2225DP57F48WIsW7bM9K+OIxtGypIsyxgZGjRRMqOxKA709GL/gR5EQmNlRsT+7m4MDw5U7olOEKi0c6VSKXgbAxVp55WQzWZN6vDdPT0YKRFmkWUZmQqZeUmSag8mENDF0fL5PJ56+mksWbMOrEWWwQqyLEP0NlRuTVfIWzpdpWr2xrpHLaPb3dODQj5vKbaljU9zQnt7em3b/kSjUTAG6mBjc6uppysnuCqKiWlCOBrc3vJWNvsP9GBosB89PT06pR8Y3/ylTAZOi8x8sViEw+HQDUfzuGN6qxqttzorOGu6N+++9x4CXbMBKIam1XckScLfXnkFJ535EdMGFIvFsPzkdabWRD09vfoxtGg9YDa2stmsqRaeJEnk8nkUCgW1FRBXccxGATxHhZp5JWhAWworAkB/Xx/2GuryjWr+spyDS/RiZHjIZLTqY1D7Eys0e5ua+VwecrZyn/kixq/76Ogo3IYAT2mtpIaxsRAEt2g+X4vWdDGV4knRTFnZRik050lzvLp7eiE2NYNUs7dGx3b5h87G7x7/P0snzVibrGQVanPmM5mM8r3iuDFtrLlPq0YgzY33Y9ewbes2tM6Yo/9d67o0ODRsqpnXnXkUy2qsKYZBRp1zDMeZaNntM4+zVGeWJAlLVq3Bo4+NC3X29fbq675Rzd5YqqHUzNtn5tMp6/7s+XwehWLRJKoXCoWQy+VMjmFOpYyTNF0mgMe5RGzduhWFQkHtsT65mnmNTksx1oGkVCoFl6fBNjOvsWoAgOWsdRJS6fFgoREEQYAsZVAV7QVJ0+mUrUilEsBRa+ZtAocMqwRNpKykBwYFleEAKFlFLUstuNwIldQ6FwFwThe2bduOhial7ErwNNScJdeQSqUqz/siQJI00um05bnUKrYFALF43BTk5EueuXg8rtPwDT8/5X7zRf1/TK+guWu2XhantEljlQBDSVBHCxZrsKrx1tYaK4hec4A+YwjgGKGVNpWeLcsLpgCWtr6nUimThoderqHaPtrntHa2dA2lU4CmeZFVghikeZ/yqgkLOyiBE2UtqXbftDaoLMejc+4ivPrqq5afk9UypdbOWdixc4fpvWg0aptgkmXZ1n7U9ohqczcrSRUDvdFo1NaOrIRoNArB5UJDoBV79+/X13LjNUsmk3C6Rf0+am0oS6EwJf363wRBgCTNug6pdBoDA+PO/LZt29E6Y7ZexhGNRiHLMnbt3g1ecMFlSCpls7Jpnz+WMGFn/tFHH8Vvf/tb/OY3v8F3vvMdfPvb3zb9q+PIhjEzr1CPHCaKZCwWQ1+v4iA2t3WURft61fZk1TZau2clHldqYhVK8+SUNTXDRlvcBgcGyqK0DMNUzMwrkc7anPliEeBVFd5XX30VnXMXgeMFcE4XwjUYzfl8Hm7Ra7uIFotFEA6HbWaeJCk4SAqJRMK0cWriQ/sP9CBgk1XQflN7b2Rk2Lae1ihWAwDNXeMqv7FYDGyVmvlsNmvKzIvexjLGRG9vLxoa/diyZQvEpnHRHIpRjM9CsQjaQolem040y5Zls6LRKDw+P1LplK5sz/C1KdNvee99tM+cC0AxNMMWm+lzzz2H45athq81qPdU1q6Jt6kFbTOPw2uvKdT6vr5e/XeVDV4xiowieJIk6XXV+rVSa0S1dmqVQBpp9jYGNkGSqoPIWJZvaL3a+3p7wKiGE81yBmc+C5fHi0KhYJmZ16iPRVWwr+x9hwNFFJXsjE1mXhP50q77yMiIKcNoVzM/FgqZ2yApHy77bFw1uJWIfXWavYOi9Hm2v7sbvkALKJpGPB43OfNiQyOc3kbL3tHGgJbgqszGMUIrvXBQJOLxOBiWh6epxJlnOTCsucxEqV3P6srZgOLM15qZN15vimGUln9FrcWewZmnGUX/QFb69GalrH6tmoNdlurMkiQh0NZpaqM50N+nPx/5fM6UZdS6KmQk1Zm3uWfptLUzPzQ0BK9vvNyPYTndWDc587oAnrnPPKBk5t99910A41op2UmIJWmBPJYtLwvS3neJXtv2qUpQUn0uGRtnPpW2dOYBpW6+dO+2c+aljATaML9N45CUMplKbbq0AGs+N142xRsy80ZFc97pMjksWgCOd7qxdetWvVe5VSC4GpKpNGIVBL6KUEQ1U6mU5bqpCAdWFtvSEItFTVoivMtsz5Rm5gFYlohNBMq+QVv58mifOVenbff19aEx0Kpea/M6UO7MU+U0+7T9vHJ5zYwJKWNNqaYZZZ8u3Rs4XjCtTbIsg3A4kEylMWxYjzQ7h1TFTbUx9vT06HZDLe6YLMtKqZfF/G2wKEcxQhcfJq33ISOM+/bytefhkUdt6OrqoGmGRRGEaT7EYjFbgTolKJGxZAn19PSgqbU6qySbleD2+ipm5gHUzE7RMDIyAtHbiMZAK3Zs3wFOEEAz5paHsVgMLtE77nvIcrnAK8y6TRq8Tc3o7x933lOplB5wyGazkNQ9CVAur3YeW7duVdphGso95VyuKnP4aMWEnXmGYTBnzpzqH6zjiIRRITqXy6ElOMNEkcxkMogn4ujt68WcxceXPdhDQ4OYtWCp7QNvfEisol+9vb0KTcrlQmySCvFGZ75YLCIr5wDCYfo9kqQqtrfJZDI10bS0dmu82h/3kUd/i+VrzwUACE5XTYqquVxONU6ts4OFQsG2hZiSfVU2lHiJM08QBAgHif3792P2ouWW9yQajUL0+rD/QLdy3wmH7XWJRKMm5fKWztl6xk1rxVOW8SkZq9EBUFrZmJ350dFRzJy3CH/fsAGeJkNmnmHV/tbjau1GaPfWSgAsFo8phlVxvByAd7pq6kPbfeAAmlqCAABOsL6fjzz6W6xYdy58/lZdqM54TY4/7Rw88uhvUSwWkU5ndBpiKpXSHeXWGbOxbft2AAaxMwN8fiUrZMzm28Gcmbc2sEmtNR1NW7JohoeH0dAUgFv06nOK4cazJnIuB6fYAJphrKn+aq1lsTieXbWCwqKxZgYozrZTv+7Dw8MQDDXctjXzkUiZM28lgCepfa8pmq7a5my8RESZZz1q20SSZhRnvkQJfNlp5+DXFvWF2WxWNxoJggBNszWpcmslEQzDIhqNKDT7pmbsUwU2NTFFJQs6Pv937dqFlo6ZpmNxghNhg7K2HUaGzcETkmbAu9woolz/gqYZZKSMmpnnIRkMNUVMji0LnkmSBJJm9DaaiuowoQtVFvIFk4GtLd9SVgLP2/cCTmfSlk5Rr0E4CVAU8jVn3opmT9I0EomEuWZe9OhBmozaVnEybVY1456xyaqn00pdcsriPWB87irnYU2zT6ftWTzekk4KSltD6571SjDZum2dsTWdXeCQVjPzxv2Xc5qdeU0MjivJYGst3DinCzt37oC7UXHUxBoEykqRTKWq7ukOktL1CkrhC7SWiW3Z/lYiCZYbD6DxJSVa4WgMgtPszPNVBFarQQtUE8S4Poh2xbVgcKFQUPt0t6jOvHk/qyUznzG0oyuFUzQ78xlpXNvBCJpmMDo6atINAJT7b6Tp53I5CKrmwsjICJweLTM/Xj5l0nk60K07e1ppXiVoXTdK+8wDgNhYWaBUq5m3K/cyIqHW8gNA59wF2H+gG6Ojo2WfM1rEbV2z9dI7AAhForZBWO0eWc2f/d09mL1oeVV9llw+D94tVszMu70NE3bmR0dH4fL6IPoa0X1gvxJAL7ETY7EYXJ4G5HIyisUicrmc5Xh7e3vhNiR5gPKuMKlUGol4DOl0Gjt37kRrp3n/085j186d4J0uOD0NemBQ0RmyLn062jFhZ/6OO+7Ad7/73cN2MX7wgx9gxowZ4DgOq1evxhsGIQQrPPbYY5g/fz44jsOSJUvK1NuLxSLuvvtutLa2gud5nHXWWXoW4VgEYchg5XI5dMyZb3Lmi0WAZXlEIxH4gzNN1B1FtKwAb6Ct6gNPWQirANA3GpYTkLRR8q0GzZDTBHPcHi94p3mjrDY7Jam2npu5XA4kTYETXNi/fz/27T+AzrkLASjUwFo2Z30BsXk/n88rdGgLp0vpdaqp2afKHKuGxgC2vr8FM+YvtVwc4/E4grPm4kBPj05hoijrtj+RSFTvVQwo0X5tboQjUfCCGyTFWEaHAYvMfEOjSWRGCSYQ8DS14K0339TbjwDGrFFR+f+GbJhW5wdYZ6m0zE+hWEQ0GgXndNUkkDM6OgrB7dGzg1bGT09PD+KpNJqDM1Ta9Hi2S2tPNHPBUuzYuQsHDhyA2zMefU6n06DUaD3L8chmc3oNXyk10aNuWJrTVgmaAVa5zzyJjCSBpKznVW9vL7yBFjQ1t44788bMfFaGU/SaathLj6+oXFtn5gEoGV4pAzlnTbPXMudaCcvw8Aichhpuuz7zEUMLxfHxlAvgac8bxTAVS24AQyZa/dLQ0BA8jQGQFGOqmdeg9I5+vcypzGZlkIZ76wuUZ0itoDnzFMMgGonoysiaczGemTf3md+6dSsCnbNNx1ICj9XXpVAoBJexZp5iwAsuEAShsjrGnTfFEVR0GhiWV9SADQta24w5Zfor2jlpbTT379+PztnHIa5mMPMFszOvMTHkrAxOcFo60fl8XulUYuHc9vb2QjQGCFkOg4OD8PgaTT2ltZ7TJEUrvawN5ym4RMRiMXgaGhWafTZbMVhlB+05ZrlyjQ/tfafoLWvVpUEJpow/l5bZ/ZS902XlqNixqsqp2+PQ9ol0Om0bOLQan+AWMTqmMMC0chdAYT8Z12Wla4QbgtMFWZZ1R81dQ+uwUij6CNZ7uizLcDhIEKozb3UuVmJbdtDKvjSwvICUQWQxGo2WlSfVKspqB6XUzQlKFZczOrIEQaBRtct6enogNjaDd7rLmAqlZVxWAnilgTwjBE+DKcgiSVlrmj3NYGRkpIzVxfK8yZlXuogoLcWGh4f19V8LdjtKMuM9Pb06e4MTBFsRNQ2yLOsBKaLknnsDrRWZGOM1++WB4lIkDSwZgiCw7NQP4/f/9wfTZ7TEkIZA52y8Z7C9I5EoMumU5Z6n3SOr+dPd3YM5i5eju1qJSBFgbEoJtWO3zZiL/QdqY6doUO6bosflafApOjUlZRBKIEpEPp9X69Y5y7KAfQe60dBkzsyLTebyl1QqhUBrO/r6+vD+++8j0DmeXCYIAqFwGG0z5ipdUZwuiN5G3ZnPVbCZjnbUtEtddtll+r/169fjV7/6FWbPno0LL7zQ9N5ll112UAf7m9/8Brfffju+8pWvYNOmTVi2bBnOOeccW8r33//+d1x99dW44YYbsHnzZlxyySW45JJLTPTIb3zjG/je976HBx98EK+//jqcTifOOeecae9xeqSglGbf2jlLV2TXAjQNgRa4RC8a/C3YZ3iwlZomny0dzRjgoRhrp69b3WgcNaqEWsGYmdd6ChtFLsbHU+EYmYzadqi6AihNKy11Hn74F1h26lm681Kr0aw55IC1KnE+n7cVKhun2atq9iXOvNffDE5woqk1aMoaa4hGo2ibMRe9vX16nbpd259YLGaiDvqa29Db24NisagogzudoBn7+mPFITI7AMbnaGBgAN6mADxNLUilUvqmDCiR/HQ6DRCEqtY+fl/S6bROo6ItDEdN+ZnllEwcq2bmq2kybN26FW0z5up/805XmfHzm8d+h2UfOhsA4PI0IBQab8EVUUWQCILA0pPPwA9+8EM0BFr1eZ1MpnSaPQC0dM7Azp071etU4sw3teBAd09Z5sQKpFqSUEmgZzwzzyBvUTPf29sLT2MLvIEWfU5RBkqwnMvB5fFa1ssDihGYy+Vs1ewBZV7n83ldwbYU0WhUZ1FEYzEMDA2ZargJC+o8MK7fYERpZr5goP/TNFtTZt6h0u8KhQLyalsimrF25pXe0aeW9Y6WshIowzPgbaqt17wkSSApBjTNIBGPg2ZYk3OhlWyUZnrf2fIeWrvMTDmuhrkPoKzdGMUwevtJhYpsrpnPqs6t1sKQpMfP00qdWcvMUzSDuUtW4ec//zkC7TP0lqF5tcxLg3ZuUjYLTnBZ3rNK/dn3d3fDa9ThYFkMDg6iOTgDQ0Pj65123hTDlmfmVcZHc7BLYb+UBChrhRLIYy3XK0AR/HJ5vLZ95pVrp/yulU4IoDAU7NaKhoB57wbsafaAffA7I2X19ox2NHtKDbAaj+F0ezCmlnNFo1Ew/Hhm3pg9j8fj4AWnnrk30uytMpuVkEqlkUzEbQUHGY4DSZJlc1sfs+hFuIaWjsVisex6EQRh+l2tk4YRlbql1IJxQU9GD+Yane6WLkWXZX93DxoCLXqQ1IjSMi6rPvPa+VhBYUwYaPY2NfMkTWNkZMTUng8AWM5Znpl3uZFOpTFkYApp2gulNfP9A/16wIcX3FXLmLLZrK5/UTp/ff6WikwMPehHlpcilCKVSoE2BEmOP/Us/N8f/mCaE6VBEmPpHaCWMnK85RyplJnv6+vD7IXLqgeNCeWa2YnshSNRtM9UEj8TwdDwCJwe5b41BVrACU7QNFvmzLNOF4pQ7nlzazv6+8vH293dY2JXAUpmfv+B8TGl02m0tHeip6dHFb8b3/8YlsPg4JBeOsk7XQpDdNicmQemrl9xpKEmZ97j8Zj+XXrppVi7di2amprK3juYeOCBB3DjjTfi+uuvx8KFC/Hggw9CEAT89Kc/tfz8d7/7XZx77rn4l3/5FyxYsAD33XcfVqxYofc4LBaL+M53voMvfelLuPjii7F06VI8/PDD6O/vr7kH+dEGY5QzK8ugWU6nkmqUN29TC3wB5Z+x3YrmDPoC1ougiRrIWNNL9x/o0TOyk32YUiml/2g0GlMz/c2mWi6FUuUAQdj/hmYoVtsMNEo0I7iwbdtWHH/qWfp7gtONWK3ZfZIsU4LVoDjz1hlUWZZBkA7dmadLNk6xScmu+gLWTkMsFoOnqRmJeFynMLm91krBpU4SQRBqhmQYkaiStVfaV1Wg2ZdG9A3qtVo9lC/QApphy+p1o9EoaJpRKPeGjSCVSunOPGWRmdfamQlOF3p7e8EJrpoCLe+9b85q8k43Yobv5PN5PP3MM1i65nT9ejgc48ZP2FCWsPy0s/H003+Gp6lFN5CSKbNAVUAVwbMygLRnzZjNtwOtCropG5NNZp4iIWnOvAXNfX93Nzz+ZngaWww0ex5xdX7KsgyX2GBZLw+MC2kWCuYMlRGynAVNM8jlrZ15jX7LO92IxaKqMTfuzNtl5gvF8t8sbU2nCFApNNhaxJIUVgkFiqLR19cHsWG8djNWUjOvYfnac/Dob39XchzzM+Buaq5JVEuSJJAMY2hHRpicC0XUSs3MG+b/e++/j7aZZmdecLoQraJHYsUOcVAMOEERDEwkEiYnVi/tyOXAcDwSibhpLWqbOQfvbjE785lMRr8Wy9eeiz/96U8Q/S16kDVfoszNqo5vtkJmXnOMrRzkAwd64DM684ySmQ+0d5rKffJ5JQhGa33mDUa+xvjQnXlZVloGTnCvSqqZeauyIG38LrHB1pk318xb0+yNNeql8PlbzZk6GwE8hfWk3AOrc5QkCbzgVLPZ9jT70vEJbg/C4QgArUuK4tiW7oEKk8qtrzMNqm1Qq8CZEalUCoV8wdLuSKfTYFkODkrJzJMWQVCCIGydWyOMdo4RRoG7WCym6E8YUKswpR209VLrDW9kbwBAy4w52PLe++juUdpqlgbTgXJxOyumXXmoYhxurw9DBi2DrM21oBlWzcybrwFTkpmXZRmCy6W0FLOtmafUwHHRVKPPO6trg2jnlkwmywTwFAFh+7VZsyONpal20AQvNThFDxqa20390EtZEa1ds7FdLb0DgGgshqbmVstzqpSZT6fTcHkakLbR39BRBDin07Y0NBKNoX3m3AnT7AeHhvT71hBoBSu4y57fqGYrFdU2mbyAbFYuW3OUMlxzZl6xjQyZ+XQK/vZO9Pb24v2t28qd+aFxZ55zuhSGqBoYzKk187Xc06MN1qtzCX72s58d7HFURTabxcaNG3HXXXfprzkcDpx11lnYsGGD5Xc2bNiA22+/3fTaOeecozvq+/btw+DgIM46a9xB83g8WL16NTZs2ICrrrrK8rhayx0N2gOWSqX0rAxJkqBphcJkNDIVg1HpAW3KZNu8TtM0SFXMygiGUdpalW54rNrjvXSB5jgODhThIIBEOIxEKIRULA6OaUBwxmxsfuMNtLa0gOcFeJsCoCgSgeYWbH1vC+6//34AwIEDB9DYNhvNzS14cf9+JEIh/G39emzZuhUOh0OvRSOLeVAUhe9973twu93I5/P6OW34+3pcd/J54CgHKIrCaH8/OHWBY2haOSfD2B//wx8Qikbxyauv1hfx0PAwPL5GhEZHEY9E0NAYAEkQ6Nm3D0vnz0chnwdFUiAIYGxwEALPgyAIsAyj1P/l80hEoiApCiN9fXj26afRNzhoyprPP+44XHjuuQgPD4MmKTgFJ5aecDIaGhpAOhyQczKcPIdYNIJEKARabR+nBQl27d4NjmUxe9YsZGIxOBwEBMGJb3zjG3A6naAoCvPmzsUZp52mOLEUhZycRS6ZVKikuRxeePFFuFwukIQDNEUimYiDpZXrCwAggOaWNoz1NcPtdGJkaAjf+uY3cf0118Dj8UCWZQwPDsHZ0AGaYfC7xx/H8jMuRi6dRM++fThuxgxl7pEkKIpCLBqB2ynoxydJEh2z5mLj668jNDqK2YKAdIxCKhSHnEhAUumub27ciNNOPRXpaBQMZRgfALfowXBPD3w+H/bs2AFvox/+QDN8/mbwNIl8oaDfr8G+PrAMCyfPQcpkkFAdmdGBATAMA45ygGNZhEdG9fccBAECAIk8eKcLf/7zn7HgpDMhul3YtWMbvv71r5uep3WnnooTVqzATx5+GE888SQuvekL+nyl3S709fbo8z0UCmHmvMUQnYK+8Dc0NmH3tm04bs4cRENhdKnXy+vxYNGKE9HU3IoewYmB7m7EQiFwLT6QyIOmaHTOnINNm9Zj6cIFoGkKNFEEqVL4mvwBvHJgP46b2QWW48BRyhwz2lUOkgTpcIChKcQjY0hEIiAdBBiHshYaswe0w4FMJgOWoZHPyfr1AgCWYXBg337MWXM2SBSRioVBFvNwOQVEQ/1IhELIZjIQRVGpZ6Uc+n3SQJIkpGQShXweJAr6PXc4HPo5SZkMOJ5HNp1GJhpV1rdsFsViEZvefhu/fuwx8I3t8Lpd6O/txdjoCD502T+CLObV9ZNCXF2rNrz+OhYvWgSvKIIgYJpjAECr49HOc7C/HwLvBEc5wLMMkvE4EqEQnn/hBZxx+ungOQ65XA5ZWcaLL72kqDKTJBiGxvYtW+BragZZzIOlKUTDETibWkATRdMa0dYehJwvYNs776CjowMAkIzFwAUa9fvX1BTA7l1bLdcIDQxNIxWNgiZJUCQJluOUOUnTICkSocFBREZHwTAMBJZBOpWEFIshI0mIR2MQXS5AnWP5QgFOQUAkNIZEKGRa97K5HNKxGBIEgeGhIXgaGkznxNAUSEEATVEIj4yAdDj060zTJAr5HJLRGDiWQ3S0DzRN63Mv2NGF37z1Jr75jW/gU//wD3AKAlLxOBiKBVnMI9g5A11z5qG5pRUEioiPjSEvy6Adyr2kaRosy2J0YADpRByNPA8pk8ZbGzagwetFc7NSRzk2MAAAiEei2P7uu0gkk5g/bx4AoKe7G6dd0aafE80w2N/Xh0WnLsam7e8iGQ7r9ZoUimAZhQVBGc7TyfNwexrg9TYgOjaGQiEPiqIRHhqCy+k07U9joRD27tuHU9esUbVbxp+/yNgYnGIbWJZFaHgYG155Bc1+P5qbm8EyDOLRKNwzu5BOJcvuUy6fR3RsTJkPRBEcxyI6NmZ6himSBIpFkMhbrhFNAT/6enqQCIXU9a8IlmUQGR01HScrSeA4JXgaGR7WhfBYVfE5nUyA5wVERkdBOgCOUhSjjTYNzTCIjo3BQRD6dRRdLoyNjCCXTGJ0cBCCoKyTJAGkUkl885vfRKFQwIEDB8CKAQgsA7enAQ2iqK97BEEgMjwMjmX1tWP7jh149sUX9TWIIAgU8nlcf+21SMRi8PoaMTYwgOZAwGRHjA4MgGU55XkOhZVuBvrcpvVzamgK4N5778VVl1+O+ccdp9+Pl195BSeecAIEnlfUuwWnaQ1yOBxgWRbhoSEwDINoJAye50E5AIpU1gKO5zEyMIBEKIRdu3ZBFEW0tLTo+9PLr7yCU9esgdNZ3smBoWmEhobA8TxIhwORkRGwDAOaosCSSsuvjhmz8PA3f4p0Oo2Lb2oDRRIo5gv6/SYIAolwGCzD6mOnSBKJSAQ5tYVtLp9HsVAEWcyb1nJtjWjwejE8NIhcMqns/6kUWJpSPq/OPTkngyIpvPDCC5ix+ATT/sRyPEaHhtC/fz927d4NQRDA8wKSkTEMDQxgmccLlnLAqdqEtIOAgwDi4TBG+/rgcov62DlewFBfH9qbmvDmxo04afVq/Xrt3bcPDE0jqeo2hEdHQTkcpj3X7XIiHA4hEQ4jm0ziwNgYBoeHsWTRIgBAMhIB6XCAIkkMdHdj85tv4uSTTlLGRVHYt38/8oUCujo7EY/G0MnQYEkCIAjkZBkrTjsbv/rVI5g3cyZYhkFsdBQMzRjWGqVV5De+8Q1ccckliEXCaPI3Y6i3F+1NTcjlcti+cydY9fkCgPDICJ7605/wrsqCyufziu4M5QDL8Rjo7lZV8aNYtmSJac8tFApwCU70jI3q969YLGLHzp1gWRaR0BiOX9WG0ZER/MfXv47rr70WgiDodvlPH34YITUYdeWll6IjGESxWMRAXx+OX7RGsYMaA6AYBnGaRnhkBAmf4uSPDA7C6XQBKCI6MgKHwwGX6MF9990HnudRKBRQKBQwPDQIt9MJssQ26unuRiIUAkWSyKTTaGkP4o9/+hPi8QTcTiccDii2EcNgoL8fqz/SBrfHC4Gh4WQZRFU7XcpkQJMEGJpRAuQlQfpK/lM+nzfZWARBgGVZXVOk2uuT9QntAr6lqMmZN2Lfvn3I5XKYO3eu6fVdu3aBpmnMUB2E6cbo6Cjy+by+qWtobm42RbeMGBwctPy8poSo/bfSZ6xw//3345577il7/fvf/77umM6fPx+nn346XnzxRdP4Vq5ciRNPPBFPPPGEKQK2du1aLFy4EI8++qguHAMAF1xwATo7O/HjH//YNJGuvPJKuFwu/OQnPzGN4YYbbkAikcBvfvMb/TWapvGpT30KxbFujOzdjr/+7yB2PfEEtnb3YN3HrkCxScQj3/seOn0+JMaG0OF24MxLrsLu3Tuw/Phl2L9/PwAgGAziqqsuwebNm9F/YB8evecePL55M1aedhpWrVqFN954A8fN6sLmJ3+OzlY//H4/Ojo68Morr+jR2LlzZmNRE4muFhpyIoZf3nMPXOo1W7NmDTiWxYsvvaSP/X9eeQXHr1iBP/7oR8ipm9KOwUFQRBHDu3chGouBbmpCLBbDXzf/HYX33sOiZctAyBnIkoRHv/Y1CAyDRp8Py1eswN49e7B33z68t307OIbBS7/4JX731ps448Mf1u9HsVjEX59+GssZBi+89BJiw/0ohroxqz0AV2SPep8eRzgcRnRkGI/ecw9WLF8OX2MjXnrxReTyefx240bMbmzELVdfjd6BAYwe2I35ixZhZGQEIyMjOOOMM/Dgf/0Xhl98EalsFpl4FGOpBHpfeAGhsTFs2rwZP331VZw0axZixSLmz5+PRDSKPa/9BeGdb+r345rzzsbLQh5vP/UwViw/Hi+++CLCW7fixssuw9at27D5pRfROGcOFs6bi5aWFlx93qn4j/9Yj7/8YhNG/vY3AMCCBQvR3t6G8PAQtvzl1zrF7oILLsDxx3XhTz/9KfaOjsLnexqRSATuXA7dzz2HF196CftHR/H6/v3of/55CIEARFLG5id/rt+/bDyC955+Go08j5cffxxyYyP2FFM4+4y1WNlCY+vWrXj55ZcxdmAn/vy/u0ERBE4M8vjP8BgeVZ+xTQcOINDWhpUtNF4J9+CNt98GtWsnAKC1vR0ulsbI5ufR6nMjRhVBJobQUgzhzn/5F7zwwgv63AuFQhjatg2tV1yBHz/0EE446SQMvvsKhra8qj9PJ6xaqc93APjC5z8PhyOB3/xBeZ6k6Bge/da38I8f+xhGuvdj72t/wfB765Vne/F8XHP5Objjb3/Gb+6/H+/29qJx9gBa6AwuvPBCDG0K4+kNf0di5w5EBQHJ+Z3qGvEqdm/bhu49u/FGKokZq1djZQuNJ554xnKN+Mued7Bz+3b0CQIGHA4054bUNeLnJtXfVCyGNjeFN4aH9GsJAKevW4eBvXvAMC+C4zj42CLe+8sjWLRwIV76+/t49J57MHRgH0K7NmLVkvmm+6QhPdqP0a1bkYlH8c6ff6HPmfF171Vk00pws3fHdvz9Zz/DrNmzsXnTJoyFQnh22zYQDQ245rzzcEI7hzNOX4doNIpdr/wRBEHgggsuQIB34JVf/xp9f/0r/vj2O1jV1Ymz166Fh2NNcwwA2kQRA5s341FVQ2UoGkVybAgrW2hIe2M4sPU9PHrPPfjf9esxtmEDzli3Dn19/Xhny7v4xWuv4YSuLmQDAQgkgRd/+1skkklsfvLn6Ovrw1D3Aaxqb0Ny54aytXzJcbPxxx/+EE3qGrZlxw6sucCNlS00Hn30cfTu3Ime99/Ho5Fw2RqhYc2aNRh8/330j4wgNDSEQlbC5id/jhtuuAEBj4iffOlL2D44iILfj+0v5CAnE3j/97/Hs6+8Akchh81P/hwNDQ246qqrsHXrVrz2yl/Rs3sXHr3nnrJ1j/Z6IUciKLAs/G7BdE6kJGHFsmXof+9NbHrmGQxls/p1Xrt2LYicjK2vvoIQSWLwwAEwPG+Ye49j5Yrl+MP//R+IXbtw3plnYmTHDvSlUticUPbSFUsW4qPrVuL5Rxx4+O67ERkawLvPPAKO4/CpT30KjxZl/OG738XuA92QczJiY6P41UMPQaRodPDK9R2OxUAQBEZ2bMcjmzehZ2AAp6iCvCP9vThtbhNeeuklbN++HUM7t6Nn/36sPimKgpTGs9/7HsZCISRCY3j7qYfh9XqRjMcROrDNNJ/u+cqXsXvXLvz98ceRDIdA0zR+fe+9WHfaaab9afvAALYPDaHzc59DRpJMSYV3du/G5TfeiMT+LJ7/xS/QHQ7D4XDgkpUrcdKaNRjYsR3hSBixsVHL+/ReXx8GHQ6kdr2GVg+Pt//yF4T//nf9+LNmzgSRlzGy+XkbO+I5jA7049F77lFKRgo5tLpo/O3Xv0b/88/rn5+3eDFcNIneoRB+fe+9YNRkxOnr1iEjSRjYt1dxZn/7W4zm81jZQqO7uxtPPfWUfoxYfz96+ik4cuPzRZZl9O7djd4XXsC2Da8h7RSwOaGIEV568UWYPXs2tmzZgnw+Dw8h4e2nHsatN3/atO7l00n875e/jBOWr0B7exte27ABT739NlI8j6amJqxatQp+vx/ffeAB5HbsQPeuXSCLBWx58klIHR0mO6J7bAytjY1o5fLYuHkTChyHzU/+XLeNtHPqbGnCO2+/jeTOnbjtE59AX18/tm3bip+uX4+PLF2KBV1d4JuawBMF05yZP38+vAKHZ37430hFIxjq7UHv269g4cKFWHniiXjiiWcwvHsLXt60Hsm33sI7o6NYMGMGfMUiEmqt/cMbNiB5ySU4cckSyzXi3VdfRTSdRmy4H//3n/8JiiQhOIqYxY7vTwvmzwNJkljT5UJ3dzeSkRB+/dWvgiAIuJxOpFgWXC6hj71/+3ZgG3BqYyP27tmDXXv2IBUNY/OTPzet5doakc/nMdzTjd4XXsDmTZsw2t+L95/7DUiSNMy9x+Gh8yi0tsJZTJn2p6Gd2xB9PYzdzz+P/mwWH1m9GvHhPoyNjmJsgMDeDX/GWTfdBHpIcYLef/53GDtwAM9vfxv80BD8olMf++iBnfjbr4aR270bDz3xBPYbSp5e6+7BwjmzQanO1MZnn0W+sRHJ44Kmc0pGwvjtvfeic8EC7Nh/ABu3bcWpqg/TGw7DQVEQHAU8+u1v4829+3DlCauUtWz5cnzvqafAyzms7OrE3i3vgOMYnLm0S7XLf45sNou3/r4ej95zD05ftw67e3sRHxnQx0/TNP79a/fiv3/4Q/zgrbcw2H0AgZYWPPfTnyJwySXo6+vHPb/8Beb6/XCyLBiGQfdbb+HpN95A64wZeuLw3LPOwMoWGmQ+i0fuuw894TAGY3HcdP55+p47MjqKdCyCAxtfwOCePeh94QW8tmEDEskknt22DR6WRYwgcOKMBmxdtRLPPP00Mlu3or2hQbfLH3roIaw88UTs27sXkXfewU1XXomMJGHHO2+jocGL4ffWw40Crv3YZfjm/ffjz//933inQdFA+NuuXfiH227D336fxO++8Q3ERgax4oQTkMlksHTpUuzcuRP79+/HiuXH4+2nHi6beyP9vXj0nnswa+ZM5FJJtDkd6G5qwswZM7D5yZ/rc++/QsPo6+3F3g3P4PS1p+lzLxkJ49df/SqGu/dj6/O/g8/jwtu/+x3+ZlDJByr7T6XrnnHPNdpGwWAQF154Id544w1s3LjRtEZMxid8+OGHUQsm7Mx/8pOfxD/+4z+WOfOvv/46fvzjH+MlwwJ6rOKuu+4yZfxjsRg6Ojpw6623QhSV/sdaFOaiiy7CBRdcoH9Wi7ZcddVVllGYG264wTIz/9nPftY0Bi0zf8cdd5heZ1kWgUCg7HWO4+DxeLBi5UqsEASsPu44jD7/ArxeL86/4AL8YvdurFy0CMlAAJ/85CfBsixmzJiBdevW6cfQzqmzsxM/efBBXLxuHf66bx9uueUWBAIBnHPOOZbnVPq6dk7zjjsOJy9fjplqMIUiSYAgcLH6m/F0Gk/v34/zP/IR7HrxRXxSff3pjRuR8vmQTacRz2Twtdtvx65du/Dq44/jrBNPRCSZQoO3AT4HgdNWrUK7z6dQ6EDg+JkzsaSrC7vicWQFAR6XE81+P+68805TtOyyCy+EWASWzJ6NLckk/vVf/9XyPr360ku4eN06kA4SBAhc8KEPYSgSwQPPPYezli9HmyBgkCCwdMkSfOmrXzXdp5/9z//g4nXrMBqL4aW+PhRlGR4QEBt86Fi3Dj977TXM7uyEz+0Gy3HIyDJuuOEGzJo1C8B4BPCjH/2oHgEcHBzEF26+GR4QWD1/Hp7esgWfvOEGLFiwQB/7ueeei3efflq/zqTDAQIERLcbX/jCF0z3KZVKYe9rr8GVyeALX/gC3n//fTz7q1/BR1G4eN06/Orlv8EbjeLidevwt/ffh9/vxz9/5jP6Mb6VyyEbT2BGWztmd3Vh/lln4eyzz9bHftJJJ2HVqlX49re+hbEdO9A6axaamprAsywuXrcOxWIRT/zoR/ivu+6Cz+fDqaeeijfHQvrYh6NR+Lxe2+fpjDPO0F/ftGkT/vSzn0EsAo0+H771rW+VPU/f+MY3yp6nYrGoP0+/+PnP4ThwAB4oSrq333473G636X50dnbihK4uDOXzuPzaa7F8+XKwLItzzz0XT/72McyfNQsNy5fjoosuMq0Rr736Kjrag1i1ahV8Pp/tOV1wwQV4OZHAzKYmHBcMYsmSJWVrxCuvvIJNb7wBn88Hjyjq10s7Ti6XM40dALZs2QKaInHxunV4Zvdu3HbbbRBF0XSfNNz/ta+ByufBc5xpzhjXvW9/61vw+XxoEgQcP3MmCBD40LJlKBaLeDcUwic++1ksW7YMFEXh7rvvLlsjXn7xRcybPx+nL1mC1/v7cfyCBeCKRQT8/rL17bHHHoM8MICLTzkFALBx925kWlvh8/mwcOFCtAYCuHjdOjz29ttYtWiR8py1tqDF7cIv33gDC+fMATN/Pta/9BKyhQI+dtVVOO+88/Dmm2/i3i1b4Ha5LNfybe+9hxk8j9PUbM7+dBpLliyBz+fDDTfcgF27duG/v/71sjXCCIokQRWLuPDCC7H5zTfxzttv44477gDLsjjuuOOw0OnEUC6HS264AStWrMCGV15Bi9OFYHs7Lli0CDf98z+bnqeVK1fi9VdfxcXr1pnWvcVdXYgBEAG8d+AADgCW5/Tcn/+MFqcTnbNn48Z/+if9dQfhQFdrG5YvWYzf/vKXmDN7dtncu+sLX8BJCxeiTRBAFou47LLLcPrpp5ueJ39jI05btQpP7diB22+/HRRFgeM4tDQ3Y3V7OwayWVx//fXYvmULxiIRFFwu3HKe0kFky4EDeGrHDiCfh8flQrG9HRevWwdJlvHHHTvQ2Nion9Mf//hHvPnWWzjllFPw+quv4sSFC8FQFB7bsgV33HEH3nrrLaQyGZxyyikmFh5N0xgbGwM3cya2hkLw+Xw466STEBAE8/708svYFY3CR1EASZqesb3JJAKBALpmzABLksjsP4C3du7AaStWgAcBjmVx/fXXY9fWrWX3aUlXF4qvv46Fs2bhIx/5CH75y19C8Pn0uQ0ojCTS4ahoR7z47LM4Y/VqkA4H/rJ/P7xeL4Jz5uDcFSv0z/eHQhDdbrS3teHDa9bAqyqwU+o5eT0esIKABTNnolkUlfXE48H8+fP1Y/zlL3/Bpj//GQ0NDfpzWSwW8fqrr8IDAk6Bx0c/8Ql9/dDWiPPOO88yQ6Wd09jgIFbOn48FrS0gQOCsE0/EqwcO4PYvfAHz5s3T7YiNr7+OlT4fNg8PIxAMgisW9f1Jw1NvvYV0ezva2trQ6PUiMHu2PlbNNtLO6emnn8bev/1NXyO6fA144NlnsWrRIqyYPRsvbNmCFatW4dM336wfnyRJ/O2FFzCnvQ0zlx+P323ZgquvvhqNjY1gGAZXXXUVvF4vdr7wAi5etw5v/va3QDaLs045Rb9/D61fD8g5eGzWiO7hYXzhnnvw/QcewNqVK8FQFLa+uh6zZs2ytfe0++rkOBAEgSfefAtzjzsO11x7LQDg97//PSKbN8Ojzr0ZgQCe6+7BHXfcYWvDvvbKK/Coa/mPXn4Z//Iv/wKCIGqyYZ977jlse+45zGluxtD774MHsGLFCryuBsLuvPNOsCyrZ0ZvueUWvPvuu3jtD39ALBTGosWL8c/qdf/vH/wAHek0mFwORcB0v198+GE48gUEm5RWlcGWFhy3bl3Znrv+pZdw4dq1SJIkXtuyBbwg6MfZtGcPXhkbw+jwMNq9Xrx24ID+XiiewJtbt+Ka009XbJ+eHnz6059GV1eXbpcXCgW8uX49Ll63DhRFwe1wYOHChaZ7xXEczjv/fPSuXw9xdBSnrV2LYCoFDwikeQ49Y2O4cPVq+N1ubBodRV6SkC8W8b3vfU8PoGv3qb2tDWtXrcLmffsQ37PHtOcmMxk8s38/brnlFtx9xx3wqM9TsVjEM7t3w+f3Izw8jM7OTnzta1/D/zz4IPyRCM5evhwUSaJQLKKpsRHf/va38dCDD6LJsO4xHIe77rpLL13hOA7Nzc1Y7fVipRpofWrnTpx44ongWBZnnHgitqRSuO+++6pmsbX7tPH11/HhNWvgVpkpZ599Nq644gq9TEube/Pnz8fOvXvxhS98ATzP63Ovva0NZ550Ev68axduu+02PPjgg0in0hP2n4zrXqkNW/r6WWedZek/TdQnvO6663DnnXeiGibszG/evBmnGDYVDSeddBJuueWWiR6uZjQ1NYEkSb3VjIahoSG0tLRYfqelpaXi57X/Dg0NobW11fSZ448/3nYsLMuCZcvbcQiCAEEQyj5rBZ63VqG1e730uNVeN4owaXA4HApzgFBoJkVCWWgXL16MfcPDSMsyfI2N+hgYhgFjUQ/FsiwcDhI0RSOZyaCxsREOh2PC5+Tz+ZCRc2BKaje1v0eHlMVlyZIl+POjj+qvy/kC2oJB7N+yBaFYDF1dXchkMoik0zrVlGGVsefyBfPxSQokCeSKRbQ0N2NXfz+CwWDZffJ4PEhk0igUre+rdk40TcPhIPWFjKEZ/OWdd3HG6acjpbZDKRSK4Diu7BhutxtSLg/SQYJjWaRlGQ6CAEjleIlUCrF0Gqzfr7cY83g8Zccx3qdZs2ahwe/H7oF+HNfWjlRWQiAQMH2nra0Nf8tkTNeloNavlh67s7MTQ7EYJFlGY2OjQt/P5+EgHGBoBrtHRyDJMhhaudZ8yXm2BYMY6+4GSZKQi0V4vV7T+9rYXS4XNo+O4tzTT1f0DhzK8bf19KAlGNRpzKIoQi6O39NUNgvR46lp7jU0NEDK5SDncuB53vLZqfY8zZ47F2+8/z4cBIFkRoLf7y+r3/Z6vZBkGdl8Hj6fTx+Dz+dDUsqgUITuJAPja0Rbayt6xkbxYVGs+Dy53W7kABRBQHA6daqYcexOpxMZWQajHrv0GbMau9vtRianPI/5QgGiKNquBTzHIa+KzFldM5ZlQZIkXC4XioUCSLXe1qHWYWdkGX6/Xz+m1bkyNA1AWatAOJDOZhFLpeHz+cp+k6ZpZA3nmZZl+Jqa9OtYKBb185JyOfU5o+BwkEioNDaXywWOZXGgrw/XzJoFQRDUdSADluMs13KO45DPSPrvFopFOJ1O/XcbGhqQzZnXuNJ7AQDZfB6iKMLlckEQnPr5dc6ciaFduzEUi2HOnDkQBAHBtjYMRaPYMzqKtWecYfk8OdTnRwdJgXAUQaEIGgSyuTwEp9PynBiGQUbOocHpNB2bIIAioczvZCYDQRDK5p6vsRHprKIcLqvnVHqvPB4PMiqDxO1260ap0+VCLl9ArliEqD4DI9Eowsmkfi65fAGNjY3IyDKKRSClrj0D4Qja29t1urP2O4Dy3DU1NSGWzqDN54NDnbNOpxP5QsFyLeB5HkmoWhw8jwIIvbZeG8tIMomBsTE4CAdAAIyh9l7K5+F0OuFyu5HK55HMSli5ahX+vm07Prx8OSRZRkNDg+V9IkllHnnUNUIQBCRK9jFJlsEyTMV1r6OzE6OxOFp9PjAMA14Q9OdAgzYPJEkCQZSMhVDaWflFEbJ6nRwOBxwOh4me6vF4EEunwalj1eBwOJR1UlLWmonaRq1tbYim06a1YzAS0Z8DDb6mJqQlCZIso62tDUlJ0vcnDcOJBBar2Uwplyvbj43n1NTUhPf0vZhC92gf8oUC0lkZtDqGGSefXD6vRRFpKQuGZkCo81B7Fnmeh9/vx9vafA2FML+lFbS6HiYzGUTicQzGonCo9lkp+sbGMG/ePJX9SQCEAwxD60Z/KRwOB7xer/Isu5XjSXIWXsMz6XQ6EQL0c80Xi3A6zfZO6X0iCAIOgoCDUujXztIWfBVsWJ/Ph0w+j3RWhpzPo1Aoguc4ZV4yjGlcgLI+zJ49G3+MxeBzhtFluO4ulwv5ZApyLo+EwZ4pFovY2d2N4zs7USgozpGUz0MQhLI9l2VZEIQypwcjEYQMa43DQYKhadA0jVg6jaGxMZAkBdLhwDPvvI11a9fq64+Uy8Hn8+n3QRsjYXi+pVwObre7bN50dXVh01/+AofDgcamJqR274aDIPD02+/g+OOPR75YRAGAv6kJoWQSLMuWXXMAEHgeRRAoFIsYjERK9lwJvOpkJ9Jp/f7lCwX0Dg/DyXFIqaV1DocDM2fPxt6XXtbHnpEkCOo6KXq9yIVC+roHgjAlBbSx5ItQ7cI8Mup6p7RTdpStFZV8DQDo7OjASCwOr9pdR1sXS20vl9sNmqLgUxN4GpoDAcTSGWVd9XjQ2dmJwd6+CftPpbT8SmOvdk6lmKj/VDa+mj5lAEEQlgIN0Wi0avuGqYBhGKxcuRLPGyhihUIBzz//PNasWWP5nTVr1pg+DwDPPfec/vmZM2eipaXF9JlYLIbXX3/d9phHOyiKQj6v1D/lCgU9KwKHA6PRGDxeb03H4XgOmWwWObWt2mQgejyIV6gH6QuNoaOzEzNmzEC3oWNBJiejpaUFkUQCBEnC4XCgqakJY+q8VIxnGhzPI20jZiPJMpoCAezo60Owq6vs/WAwiL6xELJqrZvtObjdujMAKHPyr1u24OqPfxxxVefA7hq1B4PoHxtDvlgEzZjV7LO5HDLZLMKJBCia1hcXrYyjEq74+MfxhErviafTOltEg9/vx2iJkEpKktSaJjNaW1sxGA4DBKFHHCVDBHXv4CBk9W85ny8Twwk0N2NMbUGYlmXbhYnlOOwfGkJ7e7vp9T9t2ogrrr5a/5vned0RAIB4Kl2z8CbP88hks0hnsxBquI5WCAaD6Nd6eBOwFH8TvV7EUmmks1nT+VIUhUKhgEw+Z7mgd3R1YU9/f9XFm2VZZOVcxWePoijIuZzlpmQ3dkEQkFK7COTyefvvQmlzlcvnFQ/PBqTDAacgIGfRwSEpSXC5yudb2W+oSvy5YgHxdBqRZBINjY3ln6Uo5A2/E0+nIarzgqIoyOqzJefzSBo6JeQKeUiyjIQkKTXgLIu9AwN68IhlWSTSaUuRJ0BptSQZ5mM2lzPdE0EQbNcgI+ScMidYjoMgjN//YEcHBmJRjEajCAQCAJR50h8KYWd/PxYsWGBzRLO6diky2Sx4C4MQUPbapJQBXTZHCciqk5pIpSznsFsNggJKgMJqvRI9HiTSaV24UoPgdCKTzUJWr2G+UADhUMQ/M+o1TGezaPT5kM7KSEgZff/oD40hqN4z/XiqoSaKIvx+P8a0ThXqb2rjt5rnLMsiqwpHKs9buZp1fzgMiqJM67+GjPrsC4KATC6HeDqNf/zUp/DE5s36eQiCYNsrO5vP6+PjOA6ZkvaOpWuLFTo6O9EfCkFWn2WW4yCVdJZIZ7MQ1AxW1kIQKqfeQykr264HPM8jojoZRlA0DTmXQ1x1EiaKpuZmjJXsU4lMpsxx0OyIXL6AxqYmxC2EwAYiEQSDQaUGNZutaLOIooiEQaNoR18fmhob9fs8EI3q64Ppe17vuD1jsS6Kooi4uvb0j4yY7ml/KIRGnw/9NgJ5Y/H4ePCHYZDNK0FpK2fBCLcoIp4Zvx4ZNZCtgWEYfW0ExudlJThIctxWqbD+W8HlciGVzSIhZZDN5XSbQcpm0WRY17W5RJIk2tvbMRAKYSAaRTAYHP8Mx0GSZUiyjLhBEHMgHEYynYaUyyGriq+mJMlS9JBhWH397g+FEDGI8+VV+5ikKIQTCeTyeQxHIigUCvjLO+/g2k98Qp8naUmyvW7aOmx3bYPBIPYND4NhGHi8XiQkCYVCAc+9+y4uv/xySPk85FwejU1N2NXfj7a2NsvfYVkWGVmGlMuhb3jYtP5LamBfs4E0dI+MYOnixRiORFHEuE0QDAbRH43onzOuN4LTqe9paUmytI8ZjkNWnd/7h4dMbNJcLm/b+tAO2p6nHsT2c4IgwO1ylXVj8AcCGI3FdJtIsePCNkc5OjFhZ/60007D/fffb3Lc8/k87r//fpx66qnTOrhS3H777XjooYfw85//HNu2bcOnP/1pJJNJXH/99QCA6667ziSQd9ttt+GZZ57Bf/7nf2L79u346le/irfeektnEBAEgc997nP42te+hj/96U/YsmULrrvuOrS1teGSSy45qOdyuECSpG70aosVAMybNw+b9+2FWKMzL7rdyiYxwcXcdAyvF/G0dZ9dQDGWOmbMAEmSED0ehNWFNi3LCAQC2NvXhxaVou/z+RBWnXk5lwPNMIoRZNcTPZdDIBDAzv5+dFg5811d6A+NQZKtHS/9HEQRMYMx9+auXVi6bCna2tr0jT2Xt168NGMrX8iDoWkUDIuv9t1wIqmIs2nCRBXGouGMM87A67t2KU5KOl3mNAUCAT3woSGWTsNjYXDRNI18sagvjgzD6EZfIpNR1FnV97K5XJkD4Pf7MZZU7lumgqHAchzi6bSJIZPJZrFx716sXbtWf40vCdDE0indaasGzbFSNqbKBosdgsEg+nVRKWtoRl3GyuAmCN1xK0VHVxcGxsaqGlMsyyKbzyFXqOzMG/9rhN3YletTmzNPMwwy2SzoCp8hSRKC02nZcjFpMS/LzoGmxwOP+TziUhbRlLUz73A4kDc+P5mMHpiktcADlABmwmDc5tTjh+IJMAyjUP7yeTSqv8GoQTbWzpnnOJMTJBcKZgYDzyNdgyq3pDnzqsGlIRgMojcU0oOWABCcMQN9Y6MYicV0B78UgmAfyAQqG+00TSMlSWWOAkmRkHI5CIKgXBMLR92jBrIANXtsMc/1z5RsHbz6fMr5PBiGQaFQQHNzM+bNm4ddA0ptYyabVTPzWcTSaX3t7QuF0FGi16NdR1EUEWhpKQtgamOzeoZYlkUmn1NYbCxr6egOhSNYcfzx40amAZrxy/M80rKMZDqD5cuXI40iBkIhpCUJPM8rwVGLQEHWsEZwHId0yWcs15YSBLu60BcO6UE/lmUhl7SpTGez4AUnGNXxtgJN0xUdYEEQELVw5hu8XkRTKcRTqUl1Ogo0N2M0Md67PSVJ4C3mrMfrVQLnBOBpaDA5rxr6w2EEg8Gq5wKoTrdhT985NIST1qzRj6sdq+x7alDBbn3VjhtLpRBPJk33tD8UwprVq9EftnYwdvb1YuGixQCUtVfO5SHnqzvzHo/HFNzIyHJZRlQ2BJMyUvV5JbrdpmDHROBWvxtTmRSabcRzHJpUSjxgDrQJgoCMLCv2oCGIwqoOYzYnI54cnyc7+noxf948ZFUnGLB35lmW0ZMTaUlSWiar9y9fKICkKNAUhUgqBbfLhf5QCJv37sX8BQsQDAb1eWIXWNECDoD6rFkEUJubm7Gvvx+iKCpzRJLw1u7dWLxkMfx+P2SVTdjU1IQd3d1lQcuy65HPI53J6PYyML6/EARh6lWwo7cXS44/HoTD/Lpm52gwrjc8zyOjXrOxeBx+w33TwHEcsmrgcHtvHxYvXQpA6Tw0mQSgZo/LuVxFm0MQBFUQ1oyAGhgsqp1wgsEg+kfHLI5w9GLCzvx//Md/4IUXXsC8efNw/fXX4/rrr8e8efPwt7/9Dd/85jcPxhh1XHnllfjWt76Fu+++G8cffzzefvttPPPMM7qAXXd3NwZUtVsAOPnkk/HII4/gf/7nf7Bs2TL87ne/wx/+8AcsXrxY/8ydd96JW2+9FTfddBNOOOEEJBIJPPPMMzVlQI9GGDNYRoN90bJl2Lh7d80RdFEUEUkkbVvj1HQMYyTbAsZI7KLFi7FD7aOZkWWFvlss6gsbSZL6YqQZg6WOnxHZXA5+vx/JTMYyyt7R1YWBcFgxSCvMBbcomjIzT2zehCs+/nFlE1Vf1zaFUgS7utAfDiGfLyjOvGFTTejOfAI0TevBgFqceYZhsO6MM/DSli0oAmWbGGthnMZTKbhF6zZkTY2NugHAsqyuEryzrw8LFizQHX0r48Lv92NUC8JUcCA4noeDcIyXzBAEnn/3HZz14Q9XzHQm0pma2SSaY6UYsLVRl0rhcrmQkiRIOft54fF4kJAky2g9RdNISJK1M9/ZqY+zEjQHIFcoVHXmrQwYuzntdDqRMjiedr2GAUXNN5FOVxyrRrO3agFj5+iZzsGQmc8XCkhIEqLpNHy1ZOYN2UCapvXsUy6XQ9JgiObV448l4qBVNk97c7N+7rrDZ2M0cxynZ38A6Fll4/t2AUUjtEwspzqAGoLBIN7evVsPWgJAR0cH3tq3D53BDtt75Ha7K66tdoYlADAsa+nMswyj0+sB67VIySwp1zdrY+BqmflSCC4X0tmszm5gWRbBjg4sWrZMX/vT2axCq85mkUinkVAzcgPRqP78aNCuo9vthr+lBWNquz7tmml7vF1mPiPLemmaVJIZV1gDBGbMnIm+ULlRqK11CpMopzNhPnrllXhy00bdmbfboySDM1/KRtKOXzUz39GBgWhU3+c5jlPGYjqOBMEplGVodai10Bk5a7vX8zyvOPMla0pDQwMiyYRpzkwEfr8fY6lxJ20gFEK7RVZSFEUk1HXLmP02IppMoqGhQTmXbNb2eR4/hiEz39+P1atXI67+xnAkWiaaDKiBhHQKkiyDs3g2RDVL3jc2hvbWVtM97Q+HsPLEEzFi0zZsx8AAFi5dAkB1wHM5ZHPlTLiyMZXYWKXsOOPaCNSWmVeCEvZJmEpwuVxIZjJIZLTMvMKi5HkeAcM11dYN7dl0u93Y299vuu4cxyFbKCArKyxGzabZMTiIFStXKsdX15KUJFk+55zKukmm03C6XPr4ANVuUzv9hJNJzF+wAP2hkMIWLLHxAOv90mNYhzOS9bUlSRKi260784lMBk9s3oSPXXONUvKUy0Eu5NHU1KTYqzNnWl5bzYHWSraMzng2J+vrHUGMByx2DA1i0ZIlCAQCpjnr9/tNrJh0dtyW0YIrADBqE1BmDWvWrqEhLFyyRP1tIJuvwBi0QUdHB/pjsarrHu9yQXSX27GB1laMJsYTWMFgEH0jw2WfO5oxYWd+4cKFePfdd/Gxj30Mw8PDiMfjuO6667B9+3aTk3ywcMstt+DAgQOQJAmvv/46VhvaUbz00kv43//9X9Pnr7jiCuzYsQOSJOG9997D+eefb3qfIAjce++9GBwcRCaTwV//+lccd9xxB/08DhdIitIN2JwhM79o8WKEE4manXmPx6OL50wWHo8Hccne0DVGwBctW4adanZG25BEl8uUVWdVw1kzInlBsDWktZpdAJZR9mAwiP5YTMmQVHDmtXpBAIgkk9g3OoqVK1cqxrRqaNpFIjs7O9EfiyFfLJYZSrG00jYjkkyaauKqReI1XHHVVTql0womqhyAeCZt6xQHg0Gd2sgZMpE7+/uwSI24Asp8snTm1U2h0kLMsiya/U3j50nT+P2bb+KjV15p+lyp8RvPpGtmk2gUs0zWnhJX03EEAUORiCWTAVCNHSljeb6i241QPG7pCGnzsDaavQw5X92Zt9o04+m05XNbaxYZUJzbas68w+HQ65LLoJZtVAJFUeMZ9Xwe8Uwa0XQaDapCrhGkKtKjISFJJmc+bziOiWavZ+bjembeWOqhGUDllHMFDMOYyk5KHVirMgwraMENpqSW1+VyoVAomLIxwWAQr23dioVL7PdbxdC0N7jTsr3RrtDsy515jmVNjpkVvdLoCBmzy6ax2WRPFSMxC1mWlXvBMOjo6lICuar2TVrOokmtkY6lUvr63q/SqEuPp4kgBQIBjBocQ+08AeuAF8uyCvOIYUzZNQ2j0Sj8fj+CatC3FBoLych2AYDzL7gAz2/Zogcs7Jz5Upp96WdqcbqCwSD6w2Hk8grzS3HYzc68Nk6aZS0z8wRBqCwc2ZYay/M8oolEeWbe50M0mQIIoubnwAi/34+QgUHWHwqVBWwA45wj9LXXCEmdTwRBKJl5taTGDqIoIqZmerV632AwiLgk6UEcqzmj2TN2+5zb7UYilUJ/KITjjjvOdE8HolF0dnaCcBCW6+XOwUEsUkU2GZWZVQvNvrSUsdSZNzLtAI2pUYszn0FB1UyZCDRnOZ7JICvLyOULoGgaAs/Db9C+IggCPMfp86Zd1TUyXnctqK05jdp57hwYwPLlyyHJihPsVoOEdkE7KSdjMBRCRzAIv6Fcs6AmYSiaRjiRwKJFi7BzcAA7BwexevVquFyu8aCkXVDVwNysFEBtb2vTnfne0VHsHRnBqlWr9MSLnM/rzAWr5BOgUNu1soM5s2ebnHlj8NzpFJBU9/mdAwNYtGgRgh0dpvIVh8MBh2H/Nc5pjW0EAKPxGJosAlsaSwAAtg/0Y+HChQCgBtPkqkGoUmhrWTVnXhAESx/G7/cjZCjFcDqdNds6RwsmvsJCEdD693//dzz11FP43e9+h7vvvhs+tZ9gHUc2TDXzhsy8VntZc2be60V/KFRWvzYRGCPqVhiKRPRM7cKFC7FDbReYzipUMdHjQdCwufubmjAaiyGXz+lZNtvMvOrMUxRlGWXv6OhAfyiErCxXZGko0XhlsX5m8yZcfOmlutGgMyAK1pTlYDCIgXAY+YLSV9u4ISTSabS1tiKcVGrmadW4rXXznDNnDmQHYbvZN/p8iBioafGUvVPc0dWl0ySNtNAdQ8NYtHixXp+dVRkRRoiiqGfPKlFDWZY1ZV0YhgXrdGJmSRS61OGMG5y2atAo07UYwpUQbG/Hjt4+298VRRGxTAbFYrHM6BNFEaPRaEVnvtrYlExhDrliwTbCbXLmS+ZMzEJHAShxPKs52jSNRCpVOTPvcKiiZpPTUqFpWq+3z+XzSs18yt6ZNxrBMUNmXpufgJI5T2WNzrwyNt2Z5zjTmlIqmFQKjuMgGc4vZ/EM1FKKpDm+HMeVGXztbW2moGV7ezskWTYF0krhVg1uO5TSbY1gGAYpC4eHUZ15WtXwsLomxjVdslk73W63nuU0QjESczqzSrsX8+fPxy61hVBGFZpKq7X12jwfCIXK9DZ4ntcDbn6/H2MG2ilQnWafTKfBqAyBbElGuy8UQkdHh7JPWNQ5Z7JZ5V7yPCLJlB4QdrlcOH7FCoTVAKdgE3DOlmTmy5x5G8q5Ea2trRiKRCDnc6DU85DyFjR7p0qzt3hOi0Xl+mTkrK0zLwgCkpkMWLYkM+/zIZxMWH6nFgTUOlcNfaExBC1aH4uiiLFEAgxDq5lN89waDIf1OuNaaPasIbCxb0ip91WYLhk9iGMFZV5bB3G1384VChiIhDFvwQLTPe0PK8GoQCCAYU2TxYC9Q0OYPXu2chyGQU6l2duV/2goDZxZ0eyNmibprGTrcGoQPR7EUylk8zldYLVWaHMlns6omktKSSQvCKbMvPZZDR2dnWWsDEU7IK87jZpj3Ts6ijlz5iCbU0T2XG43UpmM5V7JqM/2YCiEYGcnmgxzTkt2UWrN/MKFC/HHDRtw4UUX6UKQ4+Fj6zXeY2AhZXL2a26wowMerxcejwevb92Kiy6+WNco0px5t9sNmqYtk0+AEmyVVJr97DlzTDXhkjx+r9yqzlOxWMSwqsXS0dVVZhO0tSjrBwCkpSwElb4uCILuzI/FYghYCJArpYB5FAoFhBMJ/ZnRWD6VqPJWaGtrw1A4XLW8iOd5y6SUzhA17MUul8tS/+1oxaSc+TqOXlAGAzlfKOoLnMvlQmdHR+3OvMeD/tBYzfXKlscoEWcxolAooFAs6pvunDlzsFd15jNqVkn0eEwLm7b5Z3N5MAxrEuooRVatu29rbbWMsre0tGA4EkEmJyt14XbnoGbAisUi/rx5My657LLxN9WFI5+3drpaWlowGA4jn1foXDBkFmOpNDo6OnTxLYqiVHXv2vHRq66C2+b+NKmBD0ARsXt9507bexns6tIdfSNFf1d/H+bNm6cbm7IFA4EgCECtQ6vkRLMsa3aieA4fNQjfaSgVcIlnMhOrxySIihHyWhDs7MTz77xd0Znf099vWWdbyZl3u93wGBTk7cCogkHVBPAA66xjwsaZB1CzBoaema/gUJAkCUEQyjJNpSJxlb6vZduVzHwGkVTS1pnPGTPzhnNUHH31OLkckob5ozvzsZhO7TaxfSo4fNr7Wk3jgeHhiue2x1ACZsQbO3eib2REV78WSmr+gh2dpmeD4zg0NTbq2Q4reLxekyhUKSqJUdJ2NHvVwaVpGowqqFYKLZAFVKiZ93hsnflMTtapsawgIBgMwuVyqSJQOWRkGV6vVxGkIwhdiDGdzVqqamvBZqNOiDZNqtHsE2rggrWg2feHQgh2dekZozKozBOe5zEcCZtYPFdcfbW+DvI8j5fffw8jJQGBjGyumS91+GsJSGp6J3JuvGZeKlmTMrIMwenUM7QHDNTTXD4PB6koOGcqOMDaPGK5ksx8UxOGIpGKArKVYMx8vr13L97cs8e6Vl0UMRwOK2w9C5vCKI5Iq9oA1TKDhBrY3NHbi0VLlypzNpPWgzhWUD5j78wDSpeCt/bswdy5c033dDCiBByCHR148s030Tc2XroRT6fhNHQt0e6VlUZNKdxuN949cADPbt6MZzdvRvfQUBnNvjQzX5Vmr16LrFw9mFAKLWAcT6eQy+fHM/Nud1mQRDA8z8GurjJWhhbUzubzcDgciKVTGI3FlNa2PK90rlGdYPuaecWeGQyHEezqgr+5WbeL8oU8SIdDEblMpbBw4UJkczlcdsUVpmPIuRwo2to5FT0exNIpbOvpwfs9PbbzoqOrC6LHA7fbDZIkcelHP6qfYzaX07VYPKJYFrTUwKg0e0nOYfbs2RgwrClZtYMDoLFJFYZIsL0dBEEov1+yrwY7O/R5aNQ7Mpb9jKVSljR7pfwsj96xMXQanheSpJCRKpe5WJ6bWgZkp5uhQVPbL0VTUxPGYjFTMqyjs9PU1/1oR92Z/4CBpKhxheiSjPGXvvxlkwBZJXgaGtAfCk1K2EZDY2NjmRCbhrF43CSIwjAMWJ5HwrBZfvrmmzFv3jz9M/6WFozF4+pmTcPj8SBiEyzIF4rw+/340pe/bPm+VoNvl13S0OT3YyyRRDSVAisIpg1Jo7LnCtYCeLQqelcoFso2mng6rRsgWnTYqg6vEj5y4YW47fbbLd8LGDath1/5G5jODpx+xhmWnz3llFN0ujtN08jKMjJyFkW1lZ3RmbdiAricTiQlCVnZnha4bNkyfFztfQsAt91+e1lJDKDelxKhs4mwQwiCUER+ppCZv/iyy7DoQx/CZSUlABo6Oztx1kc+gps+/emy9zxeL8ZsnHkA+I9vfMPS6DBCM0CMZTKlqESzj6VTFXQGKquga6BpGol0GkIlmj1JglN7cxuRymSqit8BGs1+XKwzlkwikkzCazF2o8gdoAo6qmsTQRAAoQQItdp7DVqgQXNezz7vPHz47LNNxyUIe4aL5iDt6u/Hd598wpL66nA4kC8U8Lkf/7gssFEoFHDf47/DuRdfrPTmXb0a5xt60ALAJ2/4R1M5GQDc97Wv2WZoAKDJQuTSiEwlZ56m9Qx86bkm1QwXQ9NgbWj2mgOWVenNpbBb9xsaGhBJpXRH+MabbtLX9+PmzsXewUGk1cxiXm2lKbrdGAyHLeeT1+vFv6j9ebXgZbFY1JNopXW5peeaSKfBqEKqpTT7gWgEHV1daGpqMlHBSyEIAobDYdMatXz5cvz7178OALj8qqtwoFjEH998U3+/WCxiZ1+vfn9FUTSxqADgvd4ezDHsfXYQ3W6EEnFQFIWuri5s6+kxvR9Ry1ZotQ77U//1X+gZHQGgdlhgGFA0jYxsr2av7Y+lTrvP50Pv2ORL8YyB4P/3f7/HirVrsXz58rLPeTweDEci4HleN9qNeHPvXixbuRKAYV2sEkzUntkdw0NYtGSJLl7XPzZWJrSoQRN3y2TtHY5P33ILTjjjDJxwwgk6w6xYLOrrxsc+/nEMMjR+/8br+nd29vVhvqFrhVYzL9dQM79ixQosOvlkDIpuDIpunHPRRSY1dO1YGt7v68OcKmWmnoYGxFNpW22AqiAIfe/QdHY+cd11ehmBhi996Uv6/1+3bh2uMtgHwHj2V2sBG0+lsaOvFwsXLSrLaNs585zajSSSTMLv96O5tRVjCY1mX9Rp9oDSQu5HP/qRicnJsiyGo1E02OynoteLRDqNbz35BOavXo2V6jwsxXkXXIBzzj8fgiDgRz/6kW5HMgwDSZZ12+pr/+//Wbal085FCfLImD17tinImDXo5GiaJdt7e7FIrWU/6aSTcGVJ8iRoUJA3ll8IgoC0Wh47asi6G6ExOLVgmAaappCR5Ul1wGr0+TAQDlW0OZYvX47L1ECI6buNjWXr9CWXXVaTHXK0YPLqZXUclaAoCmkLmj0Ak2p4NYiiiP5wGMtqrFe2gkYDtEJ/KFRmrC5YsAA7+/p0Z/6UU04xve9vbsboli3wiyIYlkVrayvW2wjKAIqqZaVzDgQC6AuF0F5hwwoGg3g+GkV/KITOksix1rYupwrc2UHP3Btp9pKE4CyFYk6qfedrrZfXwHEcTjjhBMv3/C0tGN1/AAAQS2dw+4032mYcmpqa9MCK4hgR2DMwgHnqpi+o2XI7Z16jdYGwF1Xz+/3KgqtuHnbjVgeh/994eoKZeajZhylk5ufPn4/58+fbvs+yLD59882W77k9HkUJ3GZOnX766VV/X6slt2JCGD8DWGfm46k0RH+5Aq0ydqYsC2kFmqaRyGQqZ+YNPb+NSGQytgZJ6W9ogcdCoYACADmVsgzesCxrogkn1WyWERojKSmZM/OCICCVSoFhGMydO9f0HYIgwNr0i9V+V1Kp+/3hsJ5dN0ILdkVTSSQzGYiGazYWj6MzGNTnS2tra1lAdcWKFWW/e4ZN4E1DR2cn3qygmZGuQLNnbTLzHMfpTj5N05ZBTqOGiDEbZISmlFy6FpQqKBvX94VLlmB7X68ehEhLEhoaGuB2u7Hd4PgaYVzfedVo10SttPdpmravmU+nEWAYPeNlRH8kirM7OsqYQhq0c+N5HqORiGmNIghC7/xz4okngqZp/Op739Pff3f/fsycMwc+nw+FQkHvsFBUu4rkCwW8+N57uOX++8t+txSiKCKcSIJmGDQ3N8Pf0oLtvb2Yr14vTWvgHZWiG0+l8MTGjfjMOefq/di1zhV2zrxD7Rtd+qx7vV70jI3CW2OCwAoeUcRYPA6aYfCZW2+1/IzT6UQoFoOgZeYNNeJyLodXtm/Hnd/+NoBxhk01erjL5UIik8bO/n58duFCfQ/rj0aw6KTVlt/xeDyIpVIVs9uXqsy9vFqLDygit1qZ6tKlS3HV1Vfjdw8+qH9nR38fFhmCGIy61hEVyug0BAIB3HLbbbbvG9XsM9ks3tq7F1+tYgeKHg/6MmlFH2QSzrzD4UAul9M7kFAUhRNPPLHsc+vWrdP/f3Nzc1k5pOYwSrKMgN+PeDqNvnAYi9aepr8nFwpwu92QbIJRxrp7nufh9/uxUw2cacLFFEWBU/eA0047zfR9t9uN7b3W6w+gtqnt7kEik7FNrAAw2Y4f+tCHTOPTWBgMw1Rsmc2yLGI5GZKcQ0dHB0YMtrUky2BVgWPR40EslcbOwQEsUxMmPp+vrFS6o7MTm959F4DZZlIy88qaNxaPWzrzSglEATuHBnHCxRfrr2sClJSN2HIlBDs6sHtgALyFer4Go61qBEVRKBSLcBj2nIn4O0cD6pn5DxiUSa058/aZvWoQRRH9Y2M1K4nbj6Vo2We3PzRW1jJu0dKl2NHfh4xNT89AIIBQMqnUGzIM2traMBAubxsEoCY6cTAYxP6hoYqq2wrNMqRQ+UrGq1H+KrUQAwjkCnk4SjPzUkbfILTWdLUo2deKQHMzxtRNK562dpAqYXtfHxYtWwbA3FLK6jw9oohYOjVhsZxaMNGxMwyDaCo5JWd+KvCoVLap3EstoJKvoGavvW7pzKft9RH0qHsN4nSJdLpiuQJpU1edrJFNQVGUuUe9et5W86isxZeF6Jacy8HtdCJlEMjKFfLwqffE7loyDFORZp/N5ZDOZtE/NmZJs+d5Hgm1VWGpwnx/BdruVBAMBtFn0+YKqKxfwbAs0hbOPGOk2dsEOJxOJ5LGlpwW+4vTpvypVEHZiEVLlmDn0JBCs+R5MLRSHy16PNjR22sqQ7CDgyQVSqxRRMvm3mrOvEazLy2Z6Q+NIRgMWgq7KSJpyuuCIKBQLFYsRyul6j+xaSOuKMmSeb1eRNX1+rUd27Fi1aqaAmIejweheFzPLn706qvxxMaN4+cxppwHwzCIJlOY1dWFl7duRS6f14XjlDpX+8w8APAcZ6lm3zMyMiX2nt/vx/vd3WirEBBwOBwQOE4PLPKCoHfleGXr+zj11FNNrc6M/7WDKIqIJlMIJRJoampSSn4KhbJe50ZoDn8tInLGEqLSNaA0ILFjaMiUtdZaJcqTdKaNUJh2ytx+Ycu7OPOss6pmTUVRRCKbrakjiRUEQW1XShCKIv8ksrSAwdHNF+APBBBPp7FjaBALFy7URWKzuZyefbUM2qnPtmRw5rXuO5qWEUVRcNtkcEVRxI6+Xlu2hsfjwWgiMenuWBorqFLnGv1cNKaCWptPqOwSwNwdQ+twsMMgqmiFYDCIATUgkJHNznxKLaUai8VsM/PZnKx0YTCUg+k18xNMTAEKU2Dv4OCkWZXTaT8fiag78x8wkCSp92PO2wiz1QJRFJHJZqdUMw+otSwWNMX+SLTMOV60eDF2Dg3bbiLKQpzU6WeiKCJmUzdai2MZ7OrCvirOvM/nQySRwEA4XBZ8EEUR8VTalA0qBUVTkOTyVh1xSYLf7wetOvIURU24Pq0SjP3fE+n0hKnqO4eGsEjtXlHaH7oUbptWVJMFQRB6AGiiY+d5HqFksmpd+sGCVsc9HRuLXMEQ0uabpZq9JNka2IIgIJFJV1Wf1mn2VWrmLZ15KQNXDffMuFYBitFu1x5LyZArmXm7MoFcIQ+PKOpKvoAS0GzwKs68bfa9SmY+m8shI2WRkSSMhsNlzzrP8xhW6xfLnfnyoOV0oDTLXYpKdb2aoWVFs9fq2Rmb4KLD4agpUOp0OpW6dwMIggBp6DxgxIIFC7Cjv18XP9Xqo0XRgx39/WV7hRV8DQ0YjcdMzwTLsvY0e5WtUdp+EFA6l+jaDSXnm85mdSooTdMgHY6KHTeampoQUoMYiUwG7/X0lLHO2oNBvQXeE5s342Mf/3jV8wWULFw4kdCZYaeffjre3LtHz6wl1MAazTAIJeJo9vux6sQTsWH7dmRzss40sVMD18BzXBnNvqGhAYOhkK1uSy3wNzfj3f37qgZr3C6XbuS3t7VhQJ37f9q0yXStGJu5XQpRFLG1pxszjPOKICwZgxo0gbuMNDHml6a/oEGrvdewZ2DAVE6oMZbkXH7KzrwiCqvM7Sc3b8YVV11V9Tta94DJOvMulxNulwsswyCVrdxZoBLGs9aKmHFCknBAFSw00tM1+8C+Zl7W2RR+v1+3R/MqzZ6macve5YByLbb39duu4W63Gzv7+2zr3Gs5R0kNSlRjYWgCl5L62ebmZl1MMZuT9bIo0evVWyRWCiRrItCAouFh6jOvrt12Np+mZ2AUnwSUe1BNgNJ2PF1d2DM4OOlEjL/JP2l/52hATc788uXLsWLFipr+1XFkgyTJcYXoCjW31aA5JbUK5tnBzujsj4TLNs158+ZhV7/Sb9jKGff7/RiNx5Q6LI5Tal1ZVjdcjKihLBgdXV0Yi8UqRlUJVYRpv5rhMEL0KBHQSlFVjmWRyWZBkqTpnOJqza/odus185Va5E0Uxk3LSnW9Gnb29+sdEBRFZsl2Yfd4vQglEraO2ESh9EtWNpOJjp3neYSTySmp2U8Fxq4AU4VdlwSgNANlflYSWck2AOJ0OhFLpaqqzVIUhWS1zLydM5+RanLmjTXzIAi43W54bRwDzSgDACknlz0rBEFAzuXRoNYwasgX8vA1eAFUFrmrSLOXc3qmubS2GlDrpqMRACgLLloFLacDLpdLz05aoRIVWDvXMpq9asxRah1ppXVRqU23d+rtHKLW5hZLVXWfz4d4JoOklFH6s3M8RK8XngYvdvT11cRu8Pv9GIpEzO2tGMa6llbNiGntCkvbD9IVOosYWQ9aiy2PhWijBi2IIedy+Os7b+PcCy4oG1OwsxP9YyGE4nH0RyJYprKiqkFU114tM0/TNM446yy8tGWLSUyKYVmE4nGIHg8+dvXVeGLzJmTlcZq9VMWZF3i+vDVdQwOKxeKU2HuB5ma8u39/1YCX6Hbr87mjsxP9oRCGwmHEZdlUElVzZt7rxVu7dpfU+9IYiUYtBTiNqCUzb0R/xJwIUJIAyjqRliQ4ShhOihhYAXLBer+dCDQBvO6REYBhyrrHWEFz5u3KaKrB5XLB7Xbbds2oFTrNPpeD3+9Hb2gMLtVe0rR1jM68JQOH5/U+9YIgmGqrtWQXpbKArODxerHDpswHUPb77T09li0VawHDMGoLv+oMBk0sT8pmFUFhgzNuvFeiKGLf8DACfn/FpJbW8x4A0gYlfo1VksvnKwbXu4eH0dLSYvoNepKt6QBlz+gZGZm0eLE/4J/0XDsaUJMzf8kll+Diiy/GxRdfjHPOOQd79uwBy7JYt24d1q1bB47jsGfPHpxzzjkHe7x1TBEURY0rO9vQIGvBdDnznTNmoD80Vva6sce8Bp7nAZK0VAkHxmmasoHqqkTpzXRTZRGq7gBqv19tw2xva8OmXbvKxutp8CKWTiNn6BpQCq2nc1lmXlXjFlV1U7tWUJOFEvhQGRGToL+ns7J+73mnEykpa7ruRni8XoxEo+CnKRhhatU0wbHzPI9wInHYMvNutxuMKqo2FRAEUbuaPWHOVmuBIivoznyVTU/LzFei+pJq9rb0XGul2Rtr5gHl2tkZ0ppTDaiaABbHz+XzcDmdphrnXL6AxsZGABUy8yxbhWavZHaamposgyA8z+tq5YlSpW2LoOV0QTDQjUtRiWZvVM02QguQ0DQNxqJG2vg5KSdXnOMdXV2WTnRHV6etvsjMGTOw68AB8DwPQeAhejxK5jker8mZDzQ3YygSMdPsbe6tnsHVnHlDgGEgHDLRvh0GOiugtvcyXFue46ruk60tLRiMhPHk5s24vEQtG1D3yUgYf968CZdcfnnN64fH60U4ETcZz1dceSX+tGmTwgpR5x7NMAjFE/B4PFiyZAmG4nH0h0JgWLaqAB6gzPHS+cDzPDiGqchKqIaAWuNfzRkS3W7dyO+YORP9oTE8sXEjLv/Yx0yfow1BjYrH83iwcc9uXSAMUNYfTk0S2IGkKCQymZqyhxrDrJS6b+xesGtgoEyfhaZpZPOKivtUnRNNAO+JjW/hozVk5YHxMgBJrtzpxw5utxuiKCqCmtPozG/ctQsLSjp8yDXQ7DOyDEldDxX7eFx0lSRJkBRlu19p60+l0otwPG7ZUrEWaCV1Vm1/S8EwjKLur1LqOwwCdkaavSiKeGvXLiysQLHXwKn6JFZMrrF4HE3q3mk1lq3792Ohyt7UQNE0pAptLishGAyiUCxOOhHjb2mpZ+a/8pWv6P9GRkbw2c9+Fhs2bMADDzyABx54AH//+9/xuc99DkNDQwd7vHVMERRFIV8cX6wOtzMf7OpCv4VIXcimFmdeBaVVrWeq1sYDgBqdNAcLtJr6atAMxGpOdLCjE0OhEFpK+m1qqqGVyhlYtT7VUarSnkqpNFLR0Nd5+jLzbrcbyXS6JvpWKRiWxZw5s/W/BZVmb3cs0evFcDQ6bQ60Jjo1mbELgoDQYXTmPR7PtARlCIcDUgVVWGMGyuFw6PWZgNIBwO65FZxORGvIzNM0jWKxWLnPvE1mPpHJwFnDumE0rADl2jWUiPRoMGbmtUBYKTTlZCMpJ5fPw9tQhWZfNTMvI5OTcdxxx1l+jhcEDEeiSvukVIkzbxG0nC4orKfyQCkw3gfdCnZUZGPdcaXSA4/bjXgqXZH9FLRx5oNdXbbHXbhkiaLTwPN6P2GP1wuHw1FTFxZ/S4uSmTfMbcaGZk8QBBhVG8DYdxwA+sdCJueyVASvNDOrlQRUQkdnJ155fyucXq/lfGhvb8dANIpn3n4bF196adVz1SB6PAgnk6bnedasWSjSNN7YuUunr7Msi7F4DKLHA4IgcOlHP4r/e+21cZq9RcDZCF4QLO+b1+OZUs18U1MTJFmu+oyIoqgb+cFgEH3RKP66ZQs+ctFFps9p51DNgfQ0NKBvbMxU7+sRRT34YTsOlwujiXhN+4vGMCtdAzQHDgB29vdhoYEdAIzbObkaHLxqYFRxw79t24Zzzzuvpu9o5YvZXG5SavYugzOfsOn/XguM4nB+vx99o6O6jg8AgCB0ATzAmo3BcRzkfM4U3NTW9AKUPYyiads5LDY0gGNZPSBcCu17U9VFqdQJSIN2PQoqW7Gjqwv9KiPMGAwQRbH8Wtmgva0N/eFwWStTgiBs6+X1scgyFpf8BkVRSGezk5q3gUAANEVN2nbzNzfXM/NGPPbYY7juuuvKXr/22mvx+OOPT8ug6jh4IElSr0nM5cprtWsFTdOKQTXFmvlgMIgBdcHRkMvnQdpkLxctW1bZgShZLIIzZpTR+LM1tlTx+XxKLWCV6HPHjC60+ANlxqnH60VcylQMmnAsa6hHHD9frSWe6PGMt6bjp8+Z1wyGmEFIpasAAFnBSURBVI06eCVwLGuiH/JqttNOAE8URQzHYtNGbVeCB9Kkxs4LAsLx+GGj2WtGzFTBMozeJswKxsw86XCYRCbtnF0AcLrdiKVSVSPn2n2u9Cw67Gj2WammIKCxJAhQtBcabIwmIxXainngIElk5RxoijKtK/lCAU5BqJh95ziucmZelpHO5TB37lzLzzmdToxEo2hpaUHCIL4H2AsITQc0arYlLAQCNdjR7LWaS4qiQKu15FZwq63UHKS9edHR0WFJ0ezo7LSlYC5eulSnxQuCAI/XC1EU0dzUVJORFmhuxlA0alqnOY6zD7QyzHhm3ujMh0ImwSsTUwhQaqaNmXlBqO7Mz5iBh198wbYWPhgM4qV33tFV7muFR62ZL72mH736ajz84gv6eTAMg1AioVPiL7rkEry5e5d+fQpFe3YZoKzJVvNB6zgwWWg9rKs58x6PR8/MB4NBPLtxI5YsW1r227Vm5j0eD7yiaAoSiR6P3i7WDm5RxHA0WtP+os2bkUikrFe3lrXfYdCm0aAohedty9omApqmsau7GyeuPqlmJ8npdCItSUrN/CQcK5fbDY/Xq3TNsGiBWSt0kTtZ1tfQUkG3rFwlM6+KxknZ8RbEmo6TZrfRNG2r++DxeBBsbbVla2jP/VQDtrm8NevRCI1mr0ERsFMSZdl83tRnHii/Vlbo6OxE/9iYqc88AIAgMBKLwV/SYcA4FgCmYBig2OfVWD52cDgcaG9tnbQzHwgEJkXvP1owYWee53msX7++7PX169dPWrGxjkMHo0L0VGrmAYXaNpWNGlAWnH1DQzgwPKz/27RnD1pLstwaFi5aBKFCb8hGnw/D0ai+8HV0dGCgRCFZkuWahGMIgkB7e3sNmfkOBDvKF2u3242EJFW8zgzH6T1QTfuBqtotiqLemm46a+YBQ0eCCbIrWJ430acEQUAmJ1v22AaUzWMkFpvWzHw6m0UinZ7w2AVBQGgaxzJRiKI4LfdRM4TsjDmjAJ6DJHU2DjDO+rBCrTR7bT5XMlopA83eGExIZbM19Xct7R3v8Xrhq+DMa4JqsXSqTJiTpmlFeIdhQKqt/QC11EitibTNvnNc9cy8LGPu3LmW64rgdGIoqrQAi5vE95TfPhhdHgDFQewr6eaRyWbLet2Xws7h0Rw7TYvEbl30eDwYi8crBkyDwSBIizkWDAZtn4+FCxeOizCp2T1RFKs6WBr8fj+GYjFzzTzH2WpusAyjn+dYPK7vTzuHBk2/qaxH4/dVycyP06wFp7OmzHyBIHDWWWdZvt/c3IxYKlWmcl8Nbrcb4UQCVMn8PffccyHl87qTQdO0UjOvOvMNDQ0447TTwBoCWRWdeafTcj40+HxTVrP3eDxV7QyxocGUmQ/FYpbXaiJq9gvmzzc9m6LHY6tabvzMcKQ2FhrP80hmMiAcjrLAmtPpRCqbVXrMW9Ds5dz00OwdDgcoksQVV9dGsQcUu6gI6AKJE4VLLY9hWbZiQLoaNE0VjWZPkSSOMzI3CQJyPldVAC+Ty6GotioG1BLEWEyn2VMUBa9dH/kq6w/DMOA5btICeBpqFsArceY10UyjWKHb7QbHsjXpIwTVMth01pyZ51gWfWNjCNjY6SzLQhAEdJVoXdDU5PvMa+c0aZq93w/6GPZRJ/wUfe5zn8OnP/1pbNq0Se8N+frrr+OnP/0pvvzlL0/7AOuYXhgVoqdSMw8A1/3DP0w5w9nU1IRZ8+bhRxv+bnr9/AsvtPz84sWLcaGhb2Up/H4/Bvbt0xe+5uZmjJQ684b6oWq4+uMft6VQaViyZAkuVvvHGuF2uxGXJDAOsmL2JxWPQ1A3Gs3p0YyIMz/8YXR2doIkSaw19F2dDvj9fuwbHoJ7gg7xuRdcgOOPP17/WxAEpGUZSZv+4W63G8ORCOZOUzbc6XIjlZFQLGLCY+cFQe8tfjjAcRw+XqMSdSWwLIuR4WHbeUUQhC4ERDocyOcLgLp/VjIMnG43+lKpqutCLZn5M888E62trTpdXjOWEtlsTW21jIFHgiCwes0a2+9p6sWA0uFALOk1S5GUzoBxOZ1IShl41BZJFE3jk5/8pG2m+tzzzrPsXauNC1Cc5K6uLlxpUXfKq5n5k5YvR3xsnPY+GA7bBi2nA8FgENtfftn02s+efgYnzZpV8Xu2mXmW1enaHz777LK+zxp8TU0YDIcrrrFtbW24+JJLyl6fOXMmzv/IRyy/EwgE8I833AAAOG3tWsybNw9OpxOXWKy9VvD7/RiORNBhMMzPO+882/WdYRiwDKO0oBNFfY8iaBqLjcHMksx8KBGHr3E8e37hRReVlWCVYtmyZfjiXXdVDM59/vOfx8knn1z1PI3weDyWWiaCIOCuu+7Sz4NhmDLGzj/feitGR0cRU/fPSmvC6WeeaeqVreH8j3zE8vVa4fP5cOONN1b93MmnnqpnZ1mWxRfuuAOrVq0q+5y2LlZzJubPn1+m7H7K2rW2c16DKHowHAnX5MwLgoDukRHLueF2uxFNJiEZar41aL3hiWnIzAPA7XfcYZrPtcBBkkhJ2Ull5lesWAFZltHb3Y3kFDLzAABCcVQ9Hg8+e9ttputBUxRSklSRZs+ybJngZqClBWOxGPIqXX3hwoW2c3ju3Ln4SAV7FAA+c/PNU0p0KuKt1Z15TSxPg9fr1QVXE5KkzyNRFHHLrbfWJBzc0dGBF9evR0Y218zzPI/eUAgn2zwPgiDg5ptvLttTNf2Nyd7ziy69FDMmqT8we/ZsnGkTLD0WMGFP7otf/CJmzZqF7373u/jlL38JQGkb87Of/QwfKxEbqePIg7EOtVChZVot+NSnPjXl8RAEgW888EDNn3c6nfjEJz5h+76/uRlvb9yoL3wKJdusMD2RiPK1115b9TPNzc24zMKg1GopHSxrn/3hOGTGxhTHS6UzZrJZOJ3KwnvBBRfon73QJsAxWfibm7FvcBCeCdZzXV2S8dBaldgpZHs8HgyHw5PuD1oK0etBPK20/JuoUrKxV+rhAEEQ0/LcsCyLZLqyIUSpjA5jT+OMbF8rDaiZ+UwGdBVnWzOMKl3HK1QhL5IkFbE+9Tspg2FRCVpJkNa3e82aNRXHowXCYql0megWQ1PIqC1xXE4XEmnVmS8obSz/4R/+wfbYV1UThiIIpGUZTqfT8t4KTieGo1F0dHRge1+f/np/KDRpleNaoPQvj5he6w+HcGDUDUeFdd/Omdd6jgPAlVdeafv99s5O7HzxxYrGJ03TuP7668te53nedn0nCAL/9E//BAAmsd2PfvSjtr9jhN/vx1A4jJlz5uivVQqsaTT7QCCA/3rwQdvP8YKAjDTuzO8cGsKpH/qQ/vc111xTdWyBQACXVglKTGbd0Jxzq3XiCoO9pt0rYxZdywg/99xzACo78xeV1Kbrv2Eh5jcROByOmpz5DxmuNwDceNNNtp+l1bK1Sujo6Circ167dm3VcXgavBiORGrOzO8ZHLDM7IoeD/YNDZXR74Fx0ToC1csFasENaoBsImhtaUHP6AjmLFpY/cMl0BIBf/7Tn6buzEMpm2RZVl8bNLAsi3gsVpVmb8xmA4q2xuj7W/XMfKVAR1tbm6n1mhVuqjAXawHDMEim07X1mc/l9AAzQRAK80DOoj8U0tkBJEnW9EwB473m05Jksu14nkdfOGQb5CZJ0nK9IqnxfXgyuPjii1EoFBCq0HbVDh6Pp+K+dbRjUn3mP/axj2H9+vUIhUIIhUJYv3593ZE/SkCSpIlmebAonocLgZYWDIRCpsxhaWs6reXOwYaWsc5Xak2n0uwpilIyqIUCYuk0RHFq5Qu1IBAIYP/Q0JRaBwHKNR6NxWyzpm63G7l8ftItRUrhUduLWdGpq4E/zM78dEFx5itn0LUMlPGZHwyFKxofgiAglk5PS828cRzG2vdEjWr22vdy+bxJgdwW6lqWyGbLqL2USrOnaBoutwtJtXY9n59aqZGGSn3beZ5HTqU0G/vM94fGDkpbOg3t7e0YLKHZD4ZC2DcyYqrpLgWtUv9LjV+O46oKIwKKI7RvZOSIK7sTRbFqizUjOJatSa2bVwVANewcGKipHvVQoJIzb4T2vtVzWato3NECuobM/GTh8XprZn4JTif2Dg5att3zeDy2LRdpmoY8TTXzk0WwowP7hoam9Iwre1h1J7UStKy1XUeKhEFXxy4zn8lmQRgyyIFAAGOp5JSTXdMFhmGQqOE6cRyHZCZj0iJpb2vDYCiMcCJRlWFqhWAwiIFQqGx/EwQBPSMjE9Z7oVVn/lhWlT9cmJQzX8fRi1Jn/lhDoLnZpPIqCALSJe2ZMjXWzE8VgiAgI0kVyxkUNfssHCSpqI4XCgpNeIpdAmqBv6UF+4eH4Z6iMy8IAvYODNiKvGjnMl3UdtHjQTyTUVqQVen5WwpBEGqiWR7pYNU+2BUz8yrN3tg6qz8UqljjN5018xpoQ406ACRrzMxrNfMT7boRl8qDBTRFIZ1V2iC53G4kM8qakKvQaWIiqNS3XTOCgsGg3j8aAPqj0YOamVf6Uef1LhnFYhGhaBR7BwcrBmEYhrFsn2ik2VeCpoMyna00pwMEQaDJ56vZQGcqKPYbITidujNfLBYxHI1aZlQPB5xOp1IXXeW+WWXmNWhrwZHg2EwHDub6rwWXa8rMCwL2DA3pHQVKj7O9t8cy2Kdl5g+nM9/R1YX9w8NTesYZdQ+bjvXXKinFcRyS6bS+19g589FUytQ2t6mpCWPJpE6zP9zQWhXalYFpYFkWsWTS1Kkp2NmJAyMjk9Zm0bL9+RIblud5DIyOTnidmyrNvg57TNiZz+fz+Na3voUTTzwRLS0t8Pl8pn91HNlQDORj15nXIoXGzKExawJA78N5sKH9dq5C9o/hOKSyEiiaBklSamZ+4hnnycDv96M/FJpyRwKe57FvYMDSKAEUI9AlCNOXmfd4kJAkJGpURTeC5/lp63d/OGFsE2YHzZk3UtD7xsYq9rwVBKHm1nRA7Zl5YwDRaGDV8r1atT00YyWRyVhm5jV6n9vtRjKjZuarqHTXBIJASpJsr4Xm5Le2tupBBADoj0QOWls6DT6fD5FkEoDSF3hmVxd6hoYr3jeapi2NrUqK/0a0trZiWO1RfqTB39hY8/1mWfsuBkYIgqC3pusPhRBsbz9iGG8OhwMuQZiWzPyxkk2zm9/TAW0/qtWZ3z84aLkGeLxeJTNv4czTNK23ZDtcTlGwsxMDodCU7ChtH57KOTgMGlClYNWgh8YesKPZRxIJE8MgEAioavZHhjPPMAxyJaUAdp9LpFJmZ37GDGzauwdtNbTutINHbbFshKCuKbVo3xih7cPHylpyJGHCzvw999yDBx54AFdeeSWi0Shuv/12XHbZZXA4HPjqV796EIZYx3TClJk/QgyO6YTmzGsRa2O9sIasLIM7BDRrrf96vlihNR3H6T18SdKBfLGgtNaaYra8FmhR1amyAHieRzaXq0gZdrtc05eZF0XEJAlxSZpwIEIQBAjHhDM/rixuB62locPQmm4gFq3Y89bpdCKWTFZt4aJRsWsx5siSzLxRWbfi99Ra+5pp9iqsWu8Z1exdBmc+l586lZKhacSTyYo0e0DNehpuV//Y2EF35tuDQfSponv9oTF0BINo8Hoq6lfYZaQZhqlafqF9zt/YeMRl5gFlf6j1frMcW1Pmkzdk5nf09WLRkiVTGuN0Q3S7qzpM2nlaOfO1qNkfTaBo+qCdy0ScecHpVPZNizVA9HoxWNJ/XoPGuJFzh5Fmr45rqpl5YGrzimUYZEuSNcbj0+r+R9vcc5ZlEU0mTc78uJr99LC2popag6gEQUDKZk3dQDo6OvDW7t0IdkyeARYMBstYAbzTCb/PN+GgpdZnvp6Zn35M2Jn/1a9+hYceegh33HEHKIrC1VdfjR//+Me4++678dprrx2MMdYxjSBL2lQdayjNzFtBkuVpb/NmBUVdNFeZZq9m7x0Oh9oPvKjQxw9RZh6YujOvOemVVItFt3tanflEJoN4JjO5zPxRXi8PABzPVXVwx9Xsx/u1V8sGC4KAeCoFiqqeyeM4rqbNnCpx5rW2i9VgotnXsPlrQYOYhTOvGREUTcOlzh9Aac85VcNCa7FkZ1jzPA+KovR5p9HeE5OYvxNFR1cX+lWxoP5QCG3BINrb2irWzNs58xxXW6YaAIKtrUdczTyglGFZ9be3AlehJaERgtOJtKrLsmNgAAuPMGfeI4o1OfNul8sy0FFrb/ajBQc7M0+RZM3zxsnzlm3PKvUnH6fZV1c4P1iYDmdeS6hM5V6wFcSFOZYFox6bZaw/x7IsoomEue2a2q/d2IHlcKLW8iZAse+N9yQYDGJXXx86ZkxemyXY1VXGZuQFAX4b8btK0Grmj5W15EjChGfq4OAglqiblcvlQjQaBQB85CMfwVNPPTW9o6tj2kFRFPIlrTiOJTidTvAlRlip4zCR1nRTgfK7xYrOvEkAT6UVxzNpeCZYCz4ZNDQ0gHQ4piUzD1gbHhpEUZw2J1oURcRTKcsMbDXwPD9tqvqHE7Vs8FoGiqRIFNQA3oBB1dYKTqdT1ZyoXjNfa7lCac18rdCCALXS7Fm1PV08lbLNzDNqzXxK7Queq8CaqRUsy0KWZdv3BUGA6HYrTAa1TjQlSXpnhYOJjq4u9EfCAICBSARtwSCCwWDFZ6ASzb6WoAqgCGQdioDpROEPBGqn2dcYvFBEVhUa7I7BwSNG/E6D2+OpqdWknSjlsVgzfzAz87XuczzPI9jWZhnYFEURPMdZlq4eCTR7j8cDt9M5JTtK++5UnXnWJqBhfH5ZlrF15mOJRBlTk3A4IE+xdfN0geM4PShRDSzDmO6JttdPRZulc8aMsuCv4HTaKtlXAkXTyOfzdWf+IGDCznwwGMTAwAAApW/fs88+CwB48803j0haXR1mHOsCeARBwO/3ly0WRQPVPpuTJ9UfdZIDqijgxTAM0tmsSagsns0eEgE8kiTh8/mm/Fscp2SJWyvUZYle77Q687FJOvOCIBy2HvPTCZbjqhvohj7zhYIy/9NVerxr79WiZl9rqQpF0/qaMxEDVK+Zr5HuyDIMsrmcSQDTON6MSrN3u91IqtTMiYrrWf8uW9HY4nlen6ceUUQsnVKCKlVaGk0HgsEgBtSA+0A0itbWVnR0dVV8Buwy8yxbG+0cULI5R6Qz39JSc2aerTUzLwjI5GQUi0X0jY1VLGM5HPB4PDVl5u1Klo61mnmGYQ5qZr7WfU4QBFsxUlEUbbUXtMx8robe4wcLBEGgPRg8Ipx5O20OIz2dZVlbmn2hWCwLbjb6fBiJRo+IAFatNHtAmRvGeyIIAhp9vimVcwWDQfAlGjeC04mATY/5StDsimNlLTmSMOEreumll+L555/H6tWrceutt+Laa6/FT37yE3R3d+Pzn//8wRhjHdMIU5uoY7BmHgBOOfVUuFwuZFQqLadmwzh140tkMmg9BFkxBURNmXm9H3ihMCn6+GSxbt06S5rfREAQBC644IKKhsWSZcsqOvsTgdPpRErKIlcoTPg6NTY2YsmyZdMyjsMJtoY2YStWrgRN03CQCs2+luAHTdOgSLKmmvkVK1bUNFat9h0AUplMTeJ3gLJWybmcIiBZg1HFchyysmy5rtEMg7ik0uxdLt2ZryROWStYlqlowIuiiNUnnQRAcawiySSGwpGDqmSvQek1r2Tm+0IhtLa2gqZpjI2O2n6H4zisWLmy7HVRFLG4Rgr5suXLEYlEJjXmg4njjjvOtsa2FHPnzatJrZnneaRlGaOxGAJ+/xEjfqdh8dKlVbNobrcbK1etsnzvWKPZLzv++IMW0BUEAaecempNn21vb8eJ6rpQitbWVpzyoQ9ZvkfTNAiaRqZQOKwJtFNPPXVS7c401CLiWssxODtn3rBHnrRmjSVlXhtD6frt9/uxc+vWI8OZn0B5E8eyZUHUM848c0oBxjlz5mDp8cebXuvq6ppUckYLpB4ra8mRhAk/RV//+tf1/3/llVeis7MTGzZswNy5c3HhhRdO6+DqmH5omflCoYAjy+SYPnz1q19FoVDQnXme55GSJN2Z3z0yirXHHXdIxkLRlftqsiyLXD5vyswfilpaDV/7f/9vWo7zjW9+s+L7n/70p6fldwC1fIEApGx2wnW5Xq8Xd91117SN5XCBU+uwK+Hee+8FAF3Nvn9sDO1VIvQEQUDg+aqBApIka76OSgBRceaTklSzAq7WeSNfyNdE72ZZVtefsBpDOpsFw7KKM6+2q5wOkSOW4yqWHLAsq9+L9mAQg6EwBsIhBJcundLv1oJAIIAR1akOxWJobGzEvHnzKtaCUhSFr33ta2WvC4KAu+++u6bfPe200yY13oONZcuWYVmNwbzrr7++ps8pNHsZ2/t6sfAIo9gDwE033VT1My6XC1/+8pct39OejyPBsZkOHEyhZoIg8PWvf10XHK2ERYsW2ZZkNDY22q6vBEHgT0dASeu/3HnnlL7PsqyiFTSFecWyrG1Aw6h5cf/991t+hiAIMDRd5pgGmpvxyqvrj4g5XytDCCin2QOwXMsngsbGRnzxi180vXbyySdP6ljHmpjmkYQpX9E1a9ZgzZo10zGWOg4B9DrUaaCXHi0obU+3Z6Af8+bNOzS/zXGIx2IVnXkAegsxTc3+UDnzRy+IY5ZZUgtYtjK12whNzb4/FKopGyzwfNXM/ERA0zTyajvMZKa8B7wdtKBNrdlzlmUxFo9bK3IzDDKyElRzOp3javbTQbNn2ZqzFMEZM9D/7rsYiMZw+kFWsgeUdYVwOCDJMkiaPiIEnY41aF1LdvQPYNHp6w73cKYddQO8joOBiQi72R6jgqPL1lhrzjBMGc2+qbkZsVTyiHDmJ1IzzzDMEVnepIGqZ+YPGia1s//iF7/AKaecgra2Nhw4cAAA8J3vfAd//OMfp3VwdUw/Jtq7+ViAsQ9wWpJA0vQho6fxPK+qg9vXzAMwtBAr1p35GsDzHIgPsGNiVwNoBY2N0xcOVWwfqMHJ8zULndX0+4bMfCKThqtGZ15DzQJ4LIvRWMzy2aFVbQpao9mrmflpceY5rmZnvqOjAwPRGPoj4UNWW93c3Iz3DhxAyyRqHOuoDi1YvGvoyBO/mw7Unfk6DgYmsodVOoadLcfUSE9nabrMmdfqwY+EOV/reQDq9TiCnfljrWTnSMKEreH//u//xu23347zzz8fkUhEV0b3er34zne+M93jq2OaoRn20yH8dLTAmJnf1d+P+fPnH7LfFgQBiQrOvEYT1+hmhUIBiXS65uzlBxVutxvuGmuvj0VMRBRHa0c5EK3cY16DIAjTmplX2EBaZl6akDNPEJU1J4xgOQ6jcRtnXhXAoygKbrcbiXQaAJCfhpp5juMqtnozIhgMoj8SrtpVYDoR7OjAW7t32Ypt1TE18DyPTDaL/UNDmDlz5uEezrRD09E40rQA6ji6MZFWl7bH4HnbUjue52uip7MsW3YMrW3vkcBk4mo8D8D6XI4kaEyHujM//ZjwTP3+97+Phx56CP/2b/9moqCsWrUKW7ZsmdbB1TH9IEkS+Qm0ezoWIDidujO/va8XCw+hABrP80hlMlVp9lpP8HyhgCKOjE3kSIbH44Ho/uCyFybSJkwJEhXRHw7XpGrrdDqndbPV+sUDE6PZA0oXilr7zLMsi7FYHKKFKjdN00ipmXnN+QJQs1J+JTAcV3O7w/b2dgyEQpDz+UPGDuro6sJbu3bXxMqoY+IQBAGDY2MQPZ4jgpY73dDaptZRx3SCYZhpYUXZZuYZxlbp3nQMizIpTfjySLCRJ9JFhGVZMEewM1+n2R88TNhj2LdvH5YvX172OsuySCaT0zKoOg4ePog0e95As985PHxIqZCaaq6dkWesmT/W2wZOJ0SPB6Lng+3M15o9d6jzaigSqamjgDDNzryxNV1CysA5gRISh8OBbC4HiqrBmec4jMVjEC26M1A0jYwkKUrQxLjewnSsgxzL1uzMsyyLRDo95Q4SE0GwsxPv9XQfEvX8DyJ4nkfP0NARKX43HaBp+gNjK9Rx6DARdlmlY9g5r7XukZUy80dCcI5lWdA1Bn5ZjjuiM/P1kp2Dhwk78zNnzsTbb79d9vozzzyDBQsWTMeY6jiI0NpEfdCc+cNFs+cFoSJFsdSZT2ezh6137NEExZm37ov8QcBEDCFN9DJfLNb0HcHpnNa1gVR/HwBS2eyE9CAYlkU6K4Gma6TZx2LwWDjKxpp5ACiqr09HzTzDcRNqddUcCEyp7+9EEQwGkc/nDxmt/4MGkiTB0DQWHQMtL61AkmR9T6pj2jEtznwFmn2levrSz5Vm5t1uN1iGOWKc+VpZXJWYCkcCSNUWPhKu67GGCVsxt99+O26++WZkMhkUi0W88cYb+PWvf437778fP/7xjw/GGOuYRmiZ+Q9SzbzgdCI9OopsLge5WKy5z/V0gHc6K/bILnXmo8lkXfyuBni83mkVaTvaMKHMvIPEaCyGphp7Ajvd7umn2acV9fikJE3o+WMZBqmMBFKoXpPOcRxG4/Y0+7ShRaQW1JyOmnleEMDX2G4PUJzrQ0l51wIHmlNfx/SD53ksXLjwcA/joIAgiIqtF+uoYzKYlpp5jrMVfOM4rjaavUVAgCAI+P3+I8LpZFm2pvMAlPr6I9mZpygKFEXV9TcOAiZsxXzqU58Cz/P40pe+hFQqhY9//ONoa2vDd7/7XVx11VUHY4x1TCMIgkCxqNJLj4CF6lBAcDoRz2axZ2AAc+fOPaS/7ayS5TQ68w6SRDSVhFgXv6sKf3MzhA9wWY/b7Ya7xqAPSTrQMzxcswBaY1PTtAa8jH3mE9lszX3mAbV8S5JA1VBSwXAcQvE4PDbOfNHATHC7XEhKmWmpmXe73cjJcs2fnz137iF15hsaGjBn9mx4PB6EQqFD9rsfJLS3tx/yveVQIlDvhFDHNINlWTT4fFM6htvtRtKmZEkURbhq2CO9Xq9lAqWzs/OIcIxrPQ8A8DQ0HNHJIIqiptyOsA5rTOiq5nI5PPLIIzjnnHNwzTXXIJVKIZFI6GIRdRwlIFThpw9IZpPneQznctjR14fFS5ce2t+uMTOvCeBFkskPNH28Vlx66aWHewiHFW1tbfj2t79d02dJkkTPyCjmn3JyTZ//7Gc/O5WhlcHYZz410cw8yyIlSTU53BzH2TJbSmv1XE4XkhlpWsqNLrzwwgl9/p8+85lDmpkgCAJPPPnkIfu9DyIe//3vj2nR0kcfffRwD6GOYwwMw+CXv/zllI5x3nnn2b63cOFC3H333VWP8e3vfAeRSKTs9Z/89KdHxDN9/PHH4/jjj6/ps7fddtvBHcwUUdffOHiY0EylKAr//M//jExGoUwKglB35I9S5KaBXnq0QBAEZGQZO4eHsGjJkkP+25WUgB0OB2ia1lvTRZJJy5rfOswgCOIDT9Wq9fxJikLP2FjN2eDpvrbGPvMTptmzLFKSfTeI0s8CqOjMa/91uV1IZjLT4sxP9Ho5HI5DPnePBKP0WMaxfn0/6GttHQcHU51XldbeWtdlu2f3SHmmJ7K/HOl2EUmSdSX7g4QJz9YTTzwRmzdvPhhjqeMQIl/If2AeKp7nkZZl7OjrO+QijYIgVHUWWLVFSz0zX8fBgIMk0TM6ckhF14ygaNrQZ35irem0zHwta1UlZ760JY7L5UIik/lAaYfUUUcdddRRx+FCnWZ/8DDhq/qZz3wGd9xxB3p7e7Fy5cqy+selh5jGXMfkkMsXjghxj0MBnueRkiQkJOmQtoTSfrva4qWppuoCeA0Nh2h0dXwQQJIkEun0YXPmaZpGqjDeZ36imfnk6GjNfeaBWjPz7mnLzNdRRx111FFHHZVB0/QHJol4qDFhK0YTuTPWVSqiakUQBFFXyz1KkMt/cGrmBUHAtu5uzJ4585D/Ns/zVZ0FhmV1AbxIKnVEC5jUcfSBIkmwDKP3zj3kv6920ACATDY7IVEhluMwKkk19wt2OByWAnvaM6j91y2KSEaiyOU/OAylOuqoo4466jhc0NTs65h+TJhmv2/fvrJ/e/fu1f97sBAKhXDNNddAFEV4vV7ccMMNSCQSFb+TyWRw8803o7GxES6XC5dffjmGhoZMn9FqTIz/jnWxF4IgPlD0Up7nsau3F4sOA2ukWs08AHCqM09RlFIzX6fZ1zGNcJAk2pqbD1sNoKJmrzjzmGBNH8txSElSTSwilmXhdrksj1+WmRdFJCWFZv9BYSjVUUcdddRRx+FCvWb+4GHC3lzXIWypY8Q111yDgYEBPPfcc5BlGddffz1uuukmPPLII7bf+fznP4+nnnoKjz32GDweD2655RZcdtllWL9+velzP/vZz3Duuefqfx9qKvbhwAeJXioIAorFIhYeYvE7QM3MV1m8WGNmvt5nvo5pBkmSCLa1H7bfV5z5IoCJCx5pznytAnh2z44VzX4wm0WhUKw783XUUUcdddRxkEFR1AeGEXyoMSlvbseOHfj+97+Pbdu2AQAWLFiAW2+9FfPmzZvWwWnYtm0bnnnmGbz55ptYtWoVAOD73/8+zj//fHzrW99CW1tb2Xei0Sh+8pOf4JFHHsEZZ5wBQHHaFyxYgNdeew0nnXSS/lmv14uWlpaDMvYjFR8kmj3P8wCUViWH47erRSJZjtNr5pPpdN2Zr2NaQVIUgp219Zg/GND6zMu53IQDiBzHKX3ma3TmPVWceb01ncuFhCQBR67wbx111FFHHXUcM6BpuqaSuTomjgk7848//jiuuuoqrFq1CmvWrAEAvPbaa1i8eDEeffRRXH755dM+yA0bNsDr9eqOPACcddZZcDgceP311y17Tm/cuBGyLOOss87SX5s/fz46OzuxYcMGkzN/880341Of+hRmzZqFf/7nf8b1119fMYMkSRIkSdL/jsViAIBCoYCCRic9glEEkM3nQTL0UTHeyaBQKKBYLKJQKIBlWfj9fjQ1NR3y89Wc+Uq/y3EcHA6HToN2uVzH7H2pBOM9q2P6QFIUgl1d03pdJ3KvSJJELp9HIpOB0+mc0DgYlkUqo9Dsq32PZVmIHo/l57TsO0VRKBQKEAQBCUn6QM23+vN19KF+z45O1O/b0YP6vTp0oCgKDMNM6Vp/0O5Xrec5YWf+zjvvxF133YV7773X9PpXvvIV3HnnnQfFmR8cHCzrZ09RFHw+HwYHB22/wzBMGWW+ubnZ9J17770XZ5xxBgRBwLPPPovPfOYzSCQSJoG/Utx///245557yl4Ph8PI5XITOLPDgyKAWD6HXI5AKBQ63MM5KCgUCojH4ygWi3A4HPif//mfw3aud3/lKxV/+67/7/8DSZKQslkAQC6XO2bvSyWU3rM6pgfr1q0DQUzvsz6Re5XP5xGRJAxk0mA5bkLjyBcKSEoZZDKZqt8TBAF3/uu/Wn4ulUoBUAKvkurERzIZFIrFD8yzVn++jj7U79nRifp9O3pQv1eHFv/2b/82pT33g3a/4vF4TZ+bsDM/MDCA6667ruz1a6+9Ft/85jcndKwvfvGL+I//+I+Kn9Go/AcLX/7yl/X/v3z5ciSTSXzzm9+s6MzfdddduP322/W/Y7EYOjo60NDQcFRQpBmaBinLcDU0wOfzHe7hHBQUCgUQBIGGhgY4HI7Dep7Vflt73+VygWUYtLa2HophHXEovWd1TA8OxtyfyL1qb2+HJGXhyCitIScyHp/Ph7RU+/caGxstX49GowCAQCAAkiTR1tYGWZZBHua14VCi/nwdfajfs6MT9ft29KB+rw4tprrfftDuV62liRN25tetW4dXXnkFc+bMMb3+6quv4kMf+tCEjnXHHXfgk5/8ZMXPzJo1Cy0tLRgeHja9rmUv7WrdW1pakM1mEYlETNn5oaGhivXxq1evxn333QdJkmxbKLEsa/mekSp9JIMiSWTlHBiGOSrGO1kQBHHU3BNAqSdyO51HzXgPBo62e/ZBRq33yuv1IpHJICVJEEVxQveW4zjkC4Upr1Xa97XaebfaZx7AB2qu1Z+vow/1e3Z0on7fjh7U79XRhQ/S/ar1HCfszF900UX413/9V2zcuFGvO3/ttdfw2GOP4Z577sGf/vQn02crwe/319T7eM2aNYhEIti4cSNWrlwJAHjhhRdQKBSwevVqy++sXLkSNE3j+eef16n/O3bsQHd3t17rb4W3334bDQ0NE+qFfLSBJClIsgzhAyKAd7TAQZIQ3e7DPYw66phWiKKIeDqFpJSBa4LzW1uHp6o4zzAMaEOE2+VyIZFOT+mYddRRRx111FFHHYcbE3bmP/OZzwAAfvjDH+KHP/yh5XuA2ss8n5/i8BQsWLAA5557Lm688UY8+OCDkGUZt9xyC6666ipdyb6vrw9nnnkmHn74YZx44onweDy44YYbcPvtt8Pn80EURdx6661Ys2aNHoR44oknMDQ0hJNOOgkcx+G5557Dv//7v+MLX/jCtIz7SAVNkZByMsS6M39EgaKoujNfxzEHzXFOZjJwT5Bix6jKt1Nto1naEkcQBGSyWWCCrfLqqKOOOuqoo446jiRM2EI6XAqCv/rVr3DLLbfgzDPPhMPhwOWXX47vfe97+vuyLGPHjh260BEAfPvb39Y/K0kSzjnnHFMAgqZp/OAHP8DnP/95FItFzJkzBw888ABuvPHGQ3puhxokpWTmPyh95o8WOBwOuOvOfB3HGEiSRLFYVNTsJ6gpwnEcgKk78zRNmzLzBEEABDHhvvd11FFHHXXUUUcdRxKOGm/O5/PhkUcesX1/xowZKBaLptc4jsMPfvAD/OAHP7D8zrnnnotzzz13Wsd5NIAkybozfwSCoih4Srov1FHHMQGCQCqbRccEnXmNZk9PkUVUmpkHFKX8qR63jjrqqKOOOuqo43CiZvWADRs24MknnzS99vDDD2PmzJkIBAK46aabTL3X6zhyQZEkpFyubsgeYXA4HBA9nsM9jDrqmHYwDINwMgmXyzWh701XzTxN02BK1juapkFN8bh11FFHHXXUUUcdhxM1O/P33nsv3n//ff3vLVu24IYbbsBZZ52FL37xi3jiiSdw//33H5RB1jG9ICkKuwcG6pn5IwwkSdYz83Uck3C73RiMROB0Oif0Pc2Zn46a+dLgpcvprK+BddRRRx111FHHUY2aLZm3334b9913n/73o48+itWrV+Ohhx4CAHR0dOArX/kKvvrVr077IOuYXnzyxhuxfds2nHDCCYd7KHUYsGbNGixduvRwD6OOOqYdHlHEQE/PpDPzU2UREQSB+772NdNrLpcLiURiSseto4466qijjjrqOJyo2ZkPh8Nobm7W/3755Zdx3nnn6X+fcMIJ6Onpmd7R1XFQsGbNmort+eo4PDA+X3XUcSzBLYrYHApN2pmfjgx6aRtTt9uNjNprvo466qijjjrqqONoRM00++bmZuzbtw8AkM1msWnTJr3FGwDE4/F6DXYdddRRRx1l8Hi9GItGJ9ytYbpq5q3gcrnqNPs66qijjjrqqOOoRs3O/Pnnn48vfvGLeOWVV3DXXXdBEAR86EMf0t9/9913MXv27IMyyDrqqKOOOo5eaMKOh4tmbwWX21135uuoo4466qijjqMaNVsy9913Hy677DKsXbsWLpcLP//5z8EwjP7+T3/6U5x99tkHZZB11FFHHXUcvRAbGkCRpO6c1wqKouBwOA6K0+2sO/N11FFHHXXUUcdRjpotmab/v737jorqTt8A/gxtQKqiIAiCCiIaVBTFEiUaFOtP42rMagwaIHaxZaPJRkVJ1FhJNfYN1rVk7QVNbIki9kYRECQGO4oFpb2/PziMjKJiAgzXeT7neI5zZ+bOe+fhzp33lu9UrYoDBw7g7t27sLCweOa0x3Xr1r3yURciInr9WVlZwbxSJahUqld6nkqlgtrEpEyabksrKzbzREREpGiv/E3G+jm/g12lSpW/XQwREb1+rK2tYfGKP0tXSK02LZNr5i2trGDEcV6IiIhIwUp8zTwREdFfYWlpCYtXHPyukFptUjbXzFtYcNBWIiIiUjQ280REVKasra3/RjOvLpPT4S0sLGD0itfwExEREVUkbOaJiKhMOTo6onefPn/puYM+/BAGBqW/qXJ3d8fb/v6lPl8iIiKi8sLRf4iIqExVqlQJvXr1+kvP7devXylXU8DOzg4dOnQok3kTERERlQcemSciIiIiIiJSGDbzRERERERERArDZp6IiIiIiIhIYdjMExERERERESkMm3kiIiIiIiIihWEzT0RERERERKQwbOaJiIiIiIiIFIbNPBEREREREZHCGOm6gNeBiAAAMjMzdVwJFcrPz8e9e/dgZGQEAwPus1ICZqYczEp5mJnyMDNlYm7KwayURd/yKuwrC/vM52EzXwru3bsHAHB2dtZxJURERERERPQ6uHfvHqytrZ97v0pe1u7TS+Xn5+PPP/+EpaUlVCqVrsshFOzNcnZ2RlpaGqysrHRdDpUAM1MOZqU8zEx5mJkyMTflYFbKom95iQju3bsHR0fHF56JwCPzpcDAwABOTk66LoOKYWVlpRcr/OuEmSkHs1IeZqY8zEyZmJtyMCtl0ae8XnREvtDrf8EBERERERER0WuGzTwRERERERGRwrCZp9eSWq3G5MmToVardV0KlRAzUw5mpTzMTHmYmTIxN+VgVsrCvIrHAfCIiIiIiIiIFIZH5omIiIiIiIgUhs08ERERERERkcKwmSciIiIiIiJSGDbzRERERERERArDZp4UieM2EhE9cf/+fV2XQK+I2zEiIm03btzgZ+MrYjNPinPz5k3cuHEDeXl5APiFSAny8/MBQJMZVVxpaWnYuXOnrsugEkpNTUVAQAA++eQTAE/WNarYuB0jKjt//PEHVq5ciYyMDF2XQiWUkpKCLl26YMiQIVCpVNyWvQI286Qow4cPh5eXFzp27IiAgAAkJiZCpVLpuix6gbFjx+L9998HABgaGuq4GnqRixcvwsXFBb169cLFixd1XQ69gIhg8ODBcHNzw5EjR7B//37k5+fDwICb9YqO2zFlys3NBcAdZhVdWloavL29MWDAABw7dkzX5dBLFG7L3N3dcebMGRw8eBCPHz/mtuwV8J0ixRg/fjwOHz6MNWvWYNy4ccjOzkavXr1w8OBBXZdGxTh58iQ6dOiAFStWYO3atdi1axcAHp2vyHJychAQEABbW1uEh4fruhx6jrlz58LGxganTp3CiRMn8OWXX8LY2BjXrl3TdWn0EtyOKVNoaCi6du0KAGwyKjgTExO0bt0aNWvWxJw5c3D9+nVdl0TPMWfOHM22LCYmBgsWLEC1atVw7tw5XZemKPxEogpPRPDw4UMcOHAAPXr0gJ+fHwYMGIBdu3bByMgICxYsQFJSkq7LpKfExMSgRo0aWL58Ofr164fx48cDKDg6z1NKK6bTp0/DxMQE69atw4oVK7Bv3z5dl0RPuXjxIjZt2oSIiAhER0fDy8sLXl5eOH36NE/ZrsC4HVOm2NhYdO3aFZs2bUJUVBRWrlwJgEfnK7KTJ08CAA4cOIDdu3dj+/btePz4MQB+NlYkDx48QFRUFObPn4/o6Gg0btwYNWvWREJCgiYnrmclw2aeKjyVSoWMjAykpaWhSZMmAIDs7GyYmZlh4sSJOHv2LLZt26bjKulpPXr0wLhx49ClSxcEBQXh1q1bmDdvHgB+QFckRbMwNjaGi4sLWrRogbfffhuTJ08GULDRpYrBxcUF+/btw8CBAwEUfDm1sbFB7dq18euvvwIAT9mugLgdU6bY2Fg4ODhg2bJlCA0Nxfjx45GTk8Oj8xVA0W1X0f9bW1vDyMgINWvWRL9+/fDVV19BRHD//n1+NupY0ZzMzc2xY8cODBo0SHOfk5MT3NzcsHfvXgA8C6ak+C5RhbNx40ZkZmZqbosIatSoAVdXV6xZswbAkxW8T58+mi+xN27c0Em9BEyfPh1jxozBjz/+iOzsbACAvb09vLy8AACNGzdGYGAgZs6ciXv37sHQ0JANvY48nVXRjeWZM2c0697KlStx+PBhdO7cGX5+fjh16pSOKtZvT+dlYmKiNTiQSqVCtWrV8PjxYx59qkC4HVOmp7dLfn5+GDduHNq1a4fQ0FAYGBhg0qRJxT6Wys/UqVMRFBSEadOm4datW1rbsZiYGGRlZQEAVqxYgZSUFPj7+6NevXr47bffdFWy3ns6M6Bg+1V4RllhhpUqVeKvs7wiNvNUYezbtw/16tVD7969NV92igoJCcG6detw8eJFGBkZ4dGjRwAKBhPauXOnpomk8hMfH48GDRpg9erVSE9Px8SJExEQEIDo6GgAT5oKGxsb9O3bF9WqVdOcbk/l62VZAQUjAPfo0QMAEBUVBbVajaioKISGhqJx48Y6qlw/vSyvwi8++fn5cHBwgKurKw4dOqTLkgncjilZcc2Gra0tPD09AQDOzs6YOHEi5s6di8uXL8PAwIA7zspZWloamjZtivXr18Pc3Bzff/89OnXqhPXr12sec/fuXXTu3BkAsGXLFhgZGeHIkSMYPXo0WrduravS9dbLMiu6LatSpQqcnJxw4sQJANwxXVJs5qlCiI2NxYIFC+Dv74+QkBB88cUXSE9PB/DklNF27drB19cXw4YNAwCYmpoCAFxdXaFWqxEfH6+b4vXYtm3bYG1tjRMnTmDNmjW4cOECMjIyMHfuXCQlJUGlUmlGAPb09MSQIUOwevVqXLhwAQYGBti/fz9/OqacvCirwnXHyMgI69evR9u2bTFixAiMHz8etra2SE5O1nH1+udl6xYAzej12dnZqFu3Lm7cuMFTSXWI2zFlelmzUdhQGBoa4r333kPDhg0RGhoKgJe0lLdffvkF+fn5OHjwIL799lskJibC0dERERERmgbw9u3b2LhxI/z8/BAYGIhp06bB2dkZ8fHxuHv3ro6XQP+8KLPTp09rjs4XNvU+Pj74888/cfPmTa5fJcRmniqEKlWqoEOHDhg+fDhmz56NvLw8zJkzR+sxrq6u+PTTT3Hw4EHMmjVLczrivn374O7ujmbNmumidL2Vm5uL8+fPw87OTvOTc9WrV8dnn32Gy5cvY8mSJQAKGkQRgVqtRpcuXfDmm2+if//+ePPNN9GlSxeONFsOXpZVZGQkAODhw4fYtm0bPDw8cPLkSUyePBmTJ09GWFgY4uLidLkIeqWk65aBgQHy8/NhYmKCqlWrIj09HRYWFjyaoSPcjilTSZqNwp3SVatWxeTJk7Fp0yYcOHAAALB7924kJCTochH0RkpKCoyNjWFubg6g4LrrcePGQa1WY/bs2QCAypUr4+zZs6hbty5iYmIQGhqKb775BkuWLNE6E43Kx4symzlzJgDtgZEtLS2RlZWFvLw8bstKSogqiLy8PM3/ly5dKmq1Wk6dOvXM4xYtWiT29vbi6ekpvXv3FrVaLeHh4ZKfny/5+fnlWbLe69+/v3Ts2FFyc3MlNzdXM3348OHSvn17OXHihIg8yTYhIUF8fX1FpVJJUFCQZGZm6qRuffSirNq2bSuXLl2S+Ph4OXXqlNZ6lJeXJ1999ZU8fvxYF2XrrZKuWzk5OSIismvXLjEwMJDExESd1EsFuB1TnilTpkizZs0065KIyP79++Xtt9+Wf/7zn5pphbk8fPhQ3nvvPXF1dRVfX18xMzOT6Ojocq9bH02YMEFatmwpV65c0Zo+e/Zs8fLykiNHjsi1a9fk/Pnzz6xH33zzjTx69Kg8yyV5cWaNGzeWqKgoEXmyLYuNjRWVSiWnT58u91qVikfmqcIoev3ZoEGD0LhxY0yaNEmzR7xQcHAwNm7ciKFDh8LJyQnR0dH47LPPoFKpeEpOOSkcsCQ4OBh79uzB2bNnYWhoqMmqT58+uHz5MhITEwEUZHvs2DF069YNjx8/xrlz57B48WJYWlrqbBn0RUmy+uOPP3Ds2DHUrVsXjRo10lqPDAwM8PHHH8PExEQn9eubV123jIyMAAD37t3DoEGDYGNjw6MZOsTtmPI8evQIRkZGWmeJtW3bFp07d0ZsbCz27NkD4Mnp9leuXMGtW7eQmpoKLy8vXLt2Dc2bN9dJ7fqicLDBwMBAHDlyBEePHtW639/fH2ZmZti/fz/s7OxQv359zXpU+Jk6YsQIqNXq8i1cj5UkM7VajePHjwN4si27c+cOQkJCYGdnx21ZSel0VwLpjZSUFElLSxMR0TrKJCJae8NFnuz9PnDggBgYGMjmzZs1z7t+/Xo5VEsiImfPnpUDBw4Ue19hZllZWeLn5yf+/v4iIlp7wuvUqSNTp07V3L5586YcOnSoDCvWX6WZFY8Klr3SzKvw85S5lb2S5FaI27GKr/AsisIjgT///LPW/adOnRJfX1+ZMWOGZlpcXJw0a9ZMGjRoIOfOnSvPcl97CQkJsmTJEvnjjz+eua/o+tWnTx/x9vaWGzduaD3G19dXRo4cKSL8PCwvpZlZ0bOa6NXwyDyVuU2bNqFWrVoYOXIkAGiuAS3cW2pkZITc3Fxcu3YNwJMBZdq0aYN//vOfCAsLw969e9G1a1d8/fXXyMnJ0cFS6I/s7GwEBwejYcOG+OWXX7TuK5pZXl4e7t69i7CwMOzfvx8LFizQ7EXNyMiAubk5qlSpAqDgiIatrS1Hki1lZZEVjwqWnbLIq/DzlLmVnZLmxu1YxXPx4kUsXboUV65ceea+wiOHhb8+MHXqVNy8eVNzf6NGjQBA67mOjo5YtGgRzp07hwYNGpRx9fohNzcXQ4cOhZeXF44ePYqrV69q7ivMyMjICNnZ2UhMTMTs2bMRFxeHefPmaQa0y83NhVqtRuXKlQHw87CslUVm/E35v0G3+xJIH3z66afSokULadKkiaxfv15EtI/OR0REiFqtlmXLlj2zN/X3338XlUolKpVKAgIC5Pbt2+Vau7755ptvxNzcXFq1alXsdZ6FIiIixMTERJYvXy4iIuHh4WJnZyfBwcFy4MABGTNmjNSqVUtiY2PLq3S9w6yUhXkp06vkxu1YxZGTkyNDhgwRtVotgwcPlmPHjmnuK3oE8PHjx3Lx4kVJTU0VMzMz+fTTT+XOnTuaebRt21YmTZpU7vXrkwkTJkjr1q3l6NGjWtOLrkcRERFSqVIlmTlzpoiILFy4UNzc3CQgIEA2bdokY8aMEQcHh2fmQWWDmVUsbOapzBRuMIcPHy4jR46UoKAgadOmjWRnZ4uIyJ07d6R///7i6OgoP/30k9aHQG5urvznP/8RY2Nj8fX11Qz2RGUnLi5OTE1N5d1339VMS0xMlBs3bmgGP3vw4IG899574ujoKP/5z3+0Mvv666+lTZs24uXlJY0aNeKAQGWIWSkL81KmV82N27GKg81GxZefny/Xrl0Tb29vzWUoMTExsnbtWjl37pw8ePBARESCg4PFzs5OIiMjtXbEbNmyRbp06SItW7YUHx8fOXLkiE6WQ58ws4qJzTyVqfz8fAkICJAjR47I1q1bpX79+hIRESEiBc18TExMsSOaP3jwQObPny8//vhjeZestx49eiRTpkwRR0dHiY2Nlffee088PDzE3d1dOnfuLL/88ouIiERHR8vdu3c1zyv6QZ2XlyfJycnlXru+YVbKwryU6a/mVojbsfLHZkMZCneqHD9+XKpVqyaZmZkydOhQqVGjhjRp0kQcHR01vyQQHx//3M9FEZGrV6+WX+F6jJlVXCoRDhVIf9/69ethY2ODBg0awMHBAUDBtYSGhobo2rUrJkyYgPr162P+/PnYvHkz3njjDXh5eWHs2LEcJVtHisssNTUVHTt2xMWLFzFo0CD06dMHt2/fxtKlS3H79m38+OOPaNasGfLz83l9UzliVsrCvJSJuSmbiEClUuHEiRPo1KkTkpKS8Mknn2Dz5s2wt7fH1atX4efnh1WrViEhIQHVq1eHlZUVADyT37Vr12Bvb6+rRXktFbd+xcXF4f3330ezZs1w5coVzJ49G5UqVcLp06fRq1cvzJo1C6NGjdJ8n6TyxcwUQsc7E0jhfvrpJ7Gzs5PmzZtLtWrVpHXr1lojwt6+fVuqV6+uOSVxzJgxYmpqKmZmZlrXsFH5KS6zDRs2iEjB9YObNm2SadOmae1VPXr0qLRv316GDx+uq7L1ErNSFualTMxNudatWydRUVHy559/aqbFxsZK06ZNZciQIdK9e3eJj4+XtLQ02bp1q5iYmGjODnz6l3WobLxo/UpOTpbOnTtL5cqVNaOaF5oyZYpUr15dFyXrPWamLNyNTH9Jbm4uIiIiMH36dHz55Zc4ePAg/ve//6FOnTpYuHAhHj9+DADIysqCn58fNm7ciIYNGyIyMhL+/v5wcXHRjM5cOBowla0XZbZ48WI8evQIJiYmaNeuHUaPHq05YgEAzZo1Q15eHh49eqTDJdAfzEpZmJcyMTflioyMhL29PWbNmoV+/fqhT58+2LhxIwBArVbDzs4Oa9euhaurK+rWrQsnJyd07doVn376KaZPnw4APGpYxl62fmVlZaFWrVrw9/fHnTt3NOtS4Wjo9evXh7m5ORITE3W5GHqFmSkTm3n6Sx48eIAbN24gMDAQgwYNgomJCVq1aoX69esjMzNT87M7eXl5+O9//4sPPvgAbdu2xcWLFzFz5ky4urpizJgxALhBLS8vyyw3NxcAYGlpCQsLC63n3rp1C/fu3UOdOnV0UbreYVbKwryUibkpD5sN5Sjp98QPP/wQPXv2xNatW3Hy5EnN5Q5nzpyBl5cX3NzcdLkYeoWZKZORrgsg5bh48SLc3NygUqlgbW2N3r17w8vLCwYGBprrzZydnfHgwQPNdfDOzs5YvXo1atWqhebNmwMAbGxs0LNnT9y7d09zdJ6/CVo2/kpmRT169AgZGRn497//DRFB7969dbAU+oFZKQvzUibmpmxPNxsGBgZo1aoVDh48iC1btiAnJwdmZmb48MMPcejQIU2z4e3tDYDNRll7lfXL1NQUQMF3wn/9618ICwuDn58fBgwYgMzMTOzatQvz588H8GQ8BCp9zOw1oLsz/Ekp1q5dK66uruLh4SHNmzeXxYsXa91fdJTKfv36ycCBA0VEND9BV1ThaJi8Vq1s/dXMiuaydu1aGTJkiNja2spbb70lSUlJ5VO8nmFWysK8lIm5KVdCQoLWT8qdPHlSk0thbitXrpTGjRtrxucRETl8+LB06tRJLC0tZdiwYfL+++9LtWrVZOXKlSKi/TN19PeUxvfEnJwcmTp1qnz00UfSt29fiYuLK5/i9RQze32wmacX2r17t7i6usp3330nO3fulLFjx4qxsbEsXLhQsrKyRKRgg5ifny9ZWVnSsGFDiYyMfGY+bN7LT2lldubMGZk2bZrs2rWrvBdBbzArZWFeysTclInNhjKU1vpViDtZyh4ze72wmadiFa6YYWFh0rRpU62N47Bhw8THx0c2btyo9ZwrV66Iq6urJCQkiEjB3vQxY8aUX9F6rrQyGz16dPkVraeYlbIwL2VibsrFZqPi4/dE5WFmrycOgEfFKrzO5cKFC6hTpw6MjY01A1+Eh4fD1NQUmzZtwtWrVzXP2bNnD5ydneHg4IDQ0FDUr18fqampyMnJ0VwbT2WntDK7fPkyMytjzEpZmJcyMTflKXyPDx8+DFtbW4SEhCAgIABz5sxBSEgIFi5ciB07dgAoyFelUuH27dvIzMyEr68vgIJrgMeOHas1X167W/r4PVF5mNnric08AQCioqIwatQozJ8/H0ePHtVMf/vtt7Fjxw7k5eVpVvrKlSvjgw8+wOHDhxEXFwegYAO8detWnDt3Dq6urti7dy8OHz6MDRs2wNjYmBvSMsDMlINZKQvzUibmpnxsNiourl/Kw8z0RPmfDEAVyZ9//indunUTOzs76d+/v3h5eYm1tbVER0eLiEh8fLzUqFFDPv/8cxERrcFlqlevLvPmzRMRkQcPHki3bt3EyclJ1qxZU+7LoU+YmXIwK2VhXsrE3JRr9+7dMnLkSJk3b54mLxGRhQsXiqWlpWa8ncLTgRcuXCh169aVX3/9VUQKThvu06ePVK5cWWxtbaVBgwYSExNT7svxOuP6pTzMTL+wmddjDx48kMDAQOnbt68kJydrpjdv3lwzkExmZqaEh4eLmZmZXL58WUSeXHPj5+cnwcHBmucdO3asHKvXT8xMOZiVsjAvZWJuysRmQxm4fikPM9M/PM1ej1WqVAlqtRoDBw5ErVq1kJubCwDo0qULYmNjISKwtLREv3790KRJE7z77rtITU2FSqXC5cuXcf36dfTs2VMzv6ZNm+poSfQHM1MOZqUszEuZmJvyPHz4EBMnToS5uTmOHDmCFStW4MyZM/Dw8MAPP/wAAHBwcMDQoUMxe/ZspKWlwcTERHPKvIeHB86fPw+gIP8pU6YgLS0Nffv21dkyva64fikPM9M/KhFeUKTPcnJyYGxsDADIz8+HgYEB+vfvD3NzcyxcuFDzuCtXruCtt95Cbm4ufHx88Pvvv6NevXpYtWoV7O3tdVW+XmJmysGslIV5KRNzU57BgwfjnXfeQadOnZCbmwsjIyOEhYVhx44dOHz4MFQqFS5duoQBAwYgLy8Pa9asgYuLCy5fvoxOnTph1qxZ6Nq1q64XQy9w/VIeZqZf2MzTM958802EhIQgMDAQ+fn5AAADAwMkJibi+PHjiI6ORqNGjRAYGKjjSqkQM1MOZqUszEuZmFvFxmZD2bh+KQ8ze32xmSctycnJaNWqFbZt26Y5tSY7OxsmJiY6royeh5kpB7NSFualTMxNmdhsKAPXL+VhZq83XjNPAJ78tuuhQ4dgYWGhWdnDwsIQGhqK69ev67I8KgYzUw5mpSzMS5mYm3IlJycjMTERb7zxBoCCJr7wWl83Nzf07dsXc+fOZSOvQ1y/lIeZ6QcjXRdAFUPhb0UePXoU//jHPxAVFYWPPvoIDx8+RGRkJOzs7HRcIT2NmSkHs1IW5qVMzE15RAQqlarYZuPq1asICwtjbhUE1y/lYWb6gafZk8ajR4/g5eWFpKQkmJiYICwsDJ988omuy6IXYGbKwayUhXkpE3NTphEjRsDc3Bz+/v5azUbHjh11XRoVwfVLeZjZ64/NPGnp0KED3N3dMXfuXJiamuq6HCoBZqYczEpZmJcyMTdlYbOhLFy/lIeZvd7YzJOWvLw8GBoa6roMegXMTDmYlbIwL2VibsrDZkM5uH4pDzN7vbGZJyIiIiKdYbNBRPTXsJknIiIiIiIiUhj+NB0RERERERGRwrCZJyIiIiIiIlIYNvNERERERERECsNmnoiIiIiIiEhh2MwTERERERERKQybeSIiIiIiIiKFYTNPRER6b9++fVCpVLhz546uSymxKVOmoHHjxjp7/ezsbLi5ueH333/XWQ1l7a233sLo0aN1XUaFcuHCBTg5OeHBgwe6LoWISO+xmSciogpt4MCBUKlUmn+2trbo1KkTzpw5U2qv0apVK6Snp8Pa2rrU5lkRqVQq/O9//yuVeS1YsAC1atVCq1atAAApKSlQqVQ4depUqcy/NJS0puftzNm4cSOmTZtWdgWWQEXboVC/fn20aNECc+fO1XUpRER6j808ERFVeJ06dUJ6ejrS09Oxd+9eGBkZoVu3bqU2fxMTE1SvXh0qlarU5vk6ExF8++23CAoKKpP55+TklMl8X1WVKlVgaWmp6zIqnEGDBuGHH35Abm6urkshItJrbOaJiKjCU6vVqF69OqpXr47GjRtjwoQJSEtLw40bNwAUf2T11KlTUKlUSElJAQCkpqaie/fuqFy5MszNzdGgQQNs37692OcvX74cNjY22LVrFzw9PWFhYaHZoVDU4sWL4enpCVNTU9SrVw/ff/+95r7s7GyMGDECDg4OMDU1hYuLC6ZPnw6goBmeMmUKatasCbVaDUdHR4waNeqF78GMGTNgb28PS0tLBAUF4dGjR1r3x8TEoEOHDqhatSqsra3h5+eHEydOaO53dXUFALzzzjtQqVSa2wCwadMmNGnSBKampqhduzbCwsJe2KgdP34cSUlJ6Nq1q2ZarVq1AADe3t5QqVR46623SlQXUHDGwA8//ID/+7//g7m5Ob744gsAQHh4OOzs7GBpaYng4GBMmDDhmUsLXpTB82oqKiUlBe3atQMAVK5cGSqVCgMHDgTw7FFxV1dXhIeH44MPPoCFhQVcXFywefNm3LhxAz169ICFhQUaNmyIY8eOab3GoUOH0KZNG5iZmcHZ2RmjRo3SOk39+++/h7u7O0xNTWFvb4/evXsDKDgrZf/+/YiIiNCcmZKSkoK8vDwEBQWhVq1aMDMzg4eHByIiIrRec+DAgejZsye+/PJL2Nvbw8bGBlOnTkVubi4+/vhjVKlSBU5OTli2bJnWe6FSqbBmzRq0atUKpqameOONN7B//36teXfo0AG3b99+ZjoREZUzISIiqsACAwOlR48emtv37t2TwYMHi5ubm+Tl5YmIyK+//ioAJCMjQ/O4kydPCgC5dOmSiIh07dpVOnToIGfOnJGkpCTZsmWL7N+/v9jnL1u2TIyNjcXf319iYmLk+PHj4unpKf369dPMf8WKFeLg4CAbNmyQ5ORk2bBhg1SpUkWWL18uIiKzZs0SZ2dnOXDggKSkpMjBgwdl1apVIiKybt06sbKyku3bt0tqaqpER0fLwoULn/serF27VtRqtSxevFji4uLks88+E0tLS2nUqJHmMXv37pXIyEiJjY2VCxcuSFBQkNjb20tmZqaIiFy/fl0AyLJlyyQ9PV2uX78uIiIHDhwQKysrWb58uSQlJcnu3bvF1dVVpkyZ8tx65s6dK/Xq1dOadvToUQEge/bskfT0dLl161aJ6hIRASB2dnaydOlSSUpKktTUVFmxYoWYmprK0qVLJT4+XsLCwsTKykprmV+WwfNqKio3N1c2bNggACQ+Pl7S09Plzp07IiLi5+cnoaGhmse6uLhIlSpVZMGCBZKQkCBDhw4VKysr6dSpk/z3v/+V+Ph46dmzp3h6ekp+fr6IiCQmJoq5ubnMmzdPEhIS5LfffhNvb28ZOHCgiIjExMSIoaGhrFq1SlJSUuTEiRMSEREhIiJ37tyRli1bSkhIiKSnp0t6errk5uZKdna2TJo0SWJiYiQ5OVlWrFghlSpVkrVr12pqDQwMFEtLSxk+fLjExcXJkiVLBIAEBATIF198IQkJCTJt2jQxNjaWtLQ0ERG5dOmSABAnJydZv369XLhwQYKDg8XS0lJu3ryp9b75+vrK5MmTn/s3QkREZY/NPBERVWiBgYFiaGgo5ubmYm5uLgDEwcFBjh8/rnlMSZp5Ly+v5zaoxTXzACQxMVHzmO+++07s7e01t+vUqaNpzgtNmzZNWrZsKSIiI0eOlPbt22uauqLmzJkjdevWlezs7BK9By1btpRhw4ZpTfP19dVqbJ+Wl5cnlpaWsmXLFs00APLzzz9rPe7tt9+WL7/8UmtaZGSkODg4PHfeoaGh0r59e61phY3gyZMnX7gsz6tr9OjRWo/z9fWV4cOHa01r3bq11jK/LIOS1lTc349I8c38+++/r7mdnp4uAOTzzz/XTDt8+LAAkPT0dBERCQoKko8++khrvgcPHhQDAwPJysqSDRs2iJWVldbOjRfV8DzDhw+Xf/zjH5rbgYGB4uLiotnhJSLi4eEhbdq00dzOzc0Vc3NzWb16tYg8eb9mzJiheUxOTo44OTnJzJkztV7vnXfe0eyQICIi3eBp9kREVOG1a9cOp06dwqlTp3D06FEEBASgc+fOSE1NLfE8Ro0ahfDwcLRu3RqTJ09+6QB6lSpVQp06dTS3HRwccP36dQDAgwcPkJSUhKCgIFhYWGj+hYeHIykpCUDBac6nTp2Ch4cHRo0ahd27d2vm1adPH2RlZaF27doICQnBzz///MLT2mNjY+Hr66s1rWXLllq3r127hpCQELi7u8Pa2hpWVla4f/8+Ll++/MLlPH36NKZOnaq1HCEhIUhPT8fDhw+LfU5WVhZMTU1fON9XrcvHx0frdnx8PJo3b641rejtkmRQFho2bKj5v729PQDAy8vrmWmFfyunT5/G8uXLtWoMCAhAfn4+Ll26hA4dOsDFxQW1a9fGgAEDsHLlyue+70V99913aNq0KapVqwYLCwssXLjwmfe0QYMGMDB48lXP3t5eq1ZDQ0PY2tpqai1U9G/LyMgIPj4+iI2N1XqMmZlZieokIqKyY6TrAoiIiF7G3Nwcbm5umtuLFy+GtbU1Fi1ahPDwcE3DIiKaxzw9iFpwcDACAgKwbds27N69G9OnT8ecOXMwcuTIYl/T2NhY67ZKpdLM//79+wCARYsWPdNkGxoaAgCaNGmCS5cuYceOHdizZw/effdd+Pv7Y/369XB2dkZ8fDz27NmDqKgoDBs2DLNmzcL+/fufed2SCgwMxK1btxAREQEXFxeo1Wq0bNkS2dnZL3ze/fv3ERYWhl69ej1z3/Ma9qpVq+Ls2bOlWpe5uXmJ5le0buDFGZSFovkUDphY3LT8/HxNnYMHDy52TISaNWvCxMQEJ06cwL59+7B7925MmjQJU6ZMQUxMDGxsbIqtYc2aNRg/fjzmzJmDli1bwtLSErNmzUJ0dPRzay2srbhphbW+itu3b2vt7CIiovLHI/NERKQ4KpUKBgYGyMrKAgBUq1YNALQGqCvu58icnZ0xZMgQbNy4EePGjcOiRYv+0uvb29vD0dERycnJcHNz0/pXOOgaAFhZWaFv375YtGgR1q5diw0bNuD27dsACo5sdu/eHV9//TX27duHw4cPP7dB9vT0fKZRO3LkiNbt3377DaNGjUKXLl3QoEEDqNVq3Lx5U+sxxsbGyMvL05rWpEkTxMfHP7Mcbm5uWkd1i/L29kZcXJzWzhMTExMAeGb+JamrOB4eHoiJidGaVvR2STJ4Xk1PK+nj/oomTZrgwoULxb6/ha9rZGQEf39/fPXVVzhz5gxSUlLwyy+/aGor7j1t1aoVhg0bBm9vb7i5uZXq2QhF/7Zyc3Nx/PhxeHp6aj3m3Llz8Pb2LrXXJCKiV8cj80REVOE9fvwYV69eBQBkZGTg22+/xf3799G9e3cAgJubG5ydnTFlyhR88cUXSEhIwJw5c7TmMXr0aHTu3Bl169ZFRkYGfv3112calFcRFhaGUaNGwdraGp06dcLjx49x7NgxZGRkYOzYsZg7dy4cHBzg7e0NAwMDrFu3DtWrV4eNjQ2WL1+OvLw8+Pr6olKlSlixYgXMzMzg4uJS7GuFhoZi4MCB8PHxQevWrbFy5UqcP38etWvX1jzG3d0dkZGR8PHxQWZmJj7++GOYmZlpzcfV1RV79+5F69atoVarUblyZUyaNAndunVDzZo10bt3bxgYGOD06dM4d+4cwsPDi62nXbt2uH//Ps6fP4833ngDAGBnZwczMzPs3LkTTk5OMDU1hbW1dYnqKs7IkSMREhICHx8ftGrVCmvXrsWZM2e0lvllGTyvpqe5uLhApVJh69at6NKlC8zMzGBhYfHSGkvik08+QYsWLTBixAgEBwfD3NwcFy5cQFRUFL799lts3boVycnJaNu2LSpXrozt27cjPz8fHh4eAAoyi46ORkpKCiwsLFClShW4u7vjp59+wq5du1CrVi1ERkYiJiZGa0fS3/Hdd9/B3d0dnp6emDdvHjIyMvDhhx9q7k9JScGVK1fg7+9fKq9HRER/DY/MExFRhbdz5044ODjAwcEBvr6+iImJwbp16zQ/NWZsbIzVq1cjLi4ODRs2xMyZM59pRPPy8jB8+HB4enqiU6dOqFu3rtbPmL2q4OBgLF68GMuWLYOXlxf8/PywfPlyTUNlaWmJr776Cj4+PmjWrBlSUlKwfft2GBgYwMbGBosWLULr1q3RsGFD7NmzB1u2bIGtrW2xr9W3b198/vnn+Ne//oWmTZsiNTUVQ4cO1XrMkiVLkJGRgSZNmmDAgAEYNWoU7OzstB4zZ84cREVFwdnZWXNUNSAgAFu3bsXu3bvRrFkztGjRAvPmzXvujgUAsLW1xTvvvIOVK1dqphkZGeHrr7/Gjz/+CEdHR/To0aPEdRWnf//+mDhxIsaPH6+5ZGHgwIFap/6/LIPn1fS0GjVqICwsDBMmTIC9vT1GjBjx0vpKqmHDhti/fz8SEhLQpk0beHt7Y9KkSXB0dAQA2NjYYOPGjWjfvj08PT2xYMECrF69Gg0aNAAAjB8/HoaGhqhfvz6qVauGy5cvY/DgwejVqxf69u0LX19f3Lp1C8OGDSu1mmfMmIEZM2agUaNGOHToEDZv3oyqVatq7l+9ejU6duz4wr8RIiIqeyopeo4cERERUQmcOXMGHTp0QFJSUqkdxX6ZDh06oHr16oiMjCyX19M3KSkpqFWrFk6ePInGjRsX+5js7Gy4u7tj1apVaN26dfkWSEREWniaPREREb2ywjMgLl26pDVCeml5+PAhFixYgICAABgaGmL16tWaAQNJdy5fvoxPP/2UjTwRUQXAI/NERERU4WRlZaF79+44efIkHj16BA8PD/z73/8udtR9Kh0lOTJPREQVB5t5IiIiIiIiIoXhAHhERERERERECsNmnoiIiIiIiEhh2MwTERERERERKQybeSIiIiIiIiKFYTNPREREREREpDBs5omIiIiIiIgUhs08ERERERERkcKwmSciIiIiIiJSmP8HbrCxnNswrDcAAAAASUVORK5CYII=", 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", 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"dtype": "f8" } } @@ -3122,7 +3127,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "2e1da155", "metadata": {}, "outputs": [ @@ -3148,7 +3153,37 @@ "# Shared hyperparameters for the Darts regression models.\n", "LAGS = 5 # autoregressive lags (and past-covariate lags) for the regression models\n", "NUM_SAMPLES = 100 # empirical-quantile sample count for the probabilistic Darts models\n", - "LGBM_KWARGS = {\"num_threads\": 1, \"n_jobs\": 1, \"verbosity\": -1}\n", + "#LGBM_KWARGS = {\"num_threads\": 1, \"n_jobs\": 1, \"verbosity\": -1}# original\n", + "LGBM_KWARGS = {\n", + " \"num_threads\": 3, #use num of physical cores-1\n", + " \"n_jobs\": 1,\n", + " \"verbosity\": -1,\n", + " # ── Base loss ────────────────────────────────────────────────────────────\n", + " # NOTE: DartsLightGBMPredictor always calls LightGBMModel(likelihood=\"quantile\", ...)\n", + " # (see darts_regression.py), and Darts' quantile-likelihood setup *overwrites*\n", + " # both \"objective\" and \"alpha\" per quantile sub-fit (darts/models/forecasting/lgbm.py:\n", + " # \"likelihood ... will overwrite any `objective` parameter\"; self.kwargs[\"objective\"] =\n", + " # likelihood; self.kwargs[\"alpha\"] = quantile). So through this predictor, objective\n", + " # and alpha below are silently ignored at fit time — kept here to record intent /\n", + " # for use with a non-quantile LightGBMModel setup.\n", + " #\"objective\": \"huber\",\n", + " #\"alpha\": 4, # Huber loss delta (NOT the quantile level Darts substitutes here)\n", + " # ── Regularisation ──────────────────────────────────────────────────────\n", + " # LightGBM has no single elastic-net \"mix ratio\" the way sklearn's\n", + " # ElasticNet(l1_ratio=...) does — setting reg_alpha AND reg_lambda together\n", + " # *is* the mixed L1+L2 penalty (both default to 0, i.e. off, if unset).\n", + " \"reg_alpha\": 0.2, # L1 (sparsifies leaf weights)\n", + " \"reg_lambda\": 0.2, # L2 (shrinks leaf weights) — nonzero alongside reg_alpha => mixed L1+L2\n", + " # ── Hardware / execution ────────────────────────────────────────────────\n", + " \"device_type\": \"cpu\", # \"device\" is the older alias for the same setting\n", + " \"force_col_wise\": True, # skip row/col-wise auto-detection — faster for wide covariate panels\n", + " # ── Histogram cost controls ─────────────────────────────────────────────\n", + " # Safe to shrink given the data size here (<=9 covariates x <=3650 daily\n", + " # rows over 10y — well under bin_construct_sample_cnt, so every row is\n", + " # used to build the histograms regardless).\n", + " \"max_bin\": 127, # coarser bins => cheaper histograms + a mild regularising effect; try 127 for more split precision\n", + " \"bin_construct_sample_cnt\": 200000,\n", + "}\n", "\n", "# ── Naive floor + classical (univariate) ──────────────────────────────────────\n", "naive = LastValuePredictor()\n", @@ -3226,7 +3261,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "0aa4e777", "metadata": {}, "outputs": [ @@ -3235,45 +3270,695 @@ "output_type": "stream", "text": [ "Running last_value_naive ...\n", - " baa10y_change_1b mean CRPS = 0.02000 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.06500 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.08500 (6 preds)\n", + " baa10y_change_1b mean CRPS = 2.50980 (51 preds)\n", + " baa10y_change_5b mean CRPS = 6.80392 (51 preds)\n", + " baa10y_change_21b mean CRPS = 10.07843 (51 preds)\n", "Running darts_ets ...\n", - " baa10y_change_1b mean CRPS = 0.01111 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04438 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.07292 (6 preds)\n", + " baa10y_change_1b mean CRPS = 1.33290 (51 preds)\n", + " baa10y_change_5b mean CRPS = 5.04884 (51 preds)\n", + " baa10y_change_21b mean CRPS = 8.75713 (51 preds)\n", "Running darts_kalman ...\n", - " baa10y_change_1b mean CRPS = 0.01065 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03516 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.05780 (6 preds)\n", + " baa10y_change_1b mean CRPS = 1.26021 (51 preds)\n", + " baa10y_change_5b mean CRPS = 3.73180 (51 preds)\n", + " baa10y_change_21b mean CRPS = 6.48867 (51 preds)\n", "Running darts_autoarima ...\n", - " baa10y_change_1b mean CRPS = 0.01157 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03274 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06658 (6 preds)\n", + " baa10y_change_1b mean CRPS = 1.29769 (51 preds)\n", + " baa10y_change_5b mean CRPS = 3.82159 (51 preds)\n", + " baa10y_change_21b mean CRPS = 7.91629 (51 preds)\n", "Running darts_linreg ...\n", - " baa10y_change_1b mean CRPS = 0.00897 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03262 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06779 (6 preds)\n", - "Running darts_linreg_cov ...\n", - " baa10y_change_1b mean CRPS = 0.00851 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03204 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.06542 (6 preds)\n", + " baa10y_change_1b mean CRPS = 1.17599 (51 preds)\n", + " baa10y_change_5b mean CRPS = 3.47794 (51 preds)\n", + " baa10y_change_21b mean CRPS = 6.71238 (51 preds)\n", + "Running darts_linreg_cov ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " baa10y_change_1b mean CRPS = 1.13610 (47 preds)\n", + " baa10y_change_5b mean CRPS = 3.58121 (47 preds)\n", + " baa10y_change_21b mean CRPS = 6.75355 (47 preds)\n", "Running darts_lightgbm ...\n", - " baa10y_change_1b mean CRPS = 0.00920 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.03514 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.05764 (6 preds)\n", - "Running darts_lightgbm_cov ...\n", - " baa10y_change_1b mean CRPS = 0.01018 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04262 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.07540 (6 preds)\n", + " baa10y_change_1b mean CRPS = 1.27137 (51 preds)\n", + " baa10y_change_5b mean CRPS = 3.52841 (51 preds)\n", + " baa10y_change_21b mean CRPS = 6.74502 (51 preds)\n", + "Running darts_lightgbm_cov ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=1`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=1`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=5`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=5`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "predict() failed at origin 2025-01-20 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-01-14 00:00:00 until 2025-01-20 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-01-17 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "predict() failed at origin 2025-02-17 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-02-11 00:00:00 until 2025-02-17 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-02-14 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "predict() failed at origin 2025-05-26 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-05-20 00:00:00 until 2025-05-26 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-05-23 00:00:00.\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 1/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 (attempt 2/3): The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00. — retrying in 2s\n", + "ValueError: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n", + "predict() failed at origin 2025-09-01 after 3 attempt(s) — skipping origin: The `past_covariates` are not long enough. Given horizon `n=21`, `min(lags_past_covariates)=-5`, `max(lags_past_covariates)=-1` and `output_chunk_length=21`, the `past_covariates` have to range from 2025-08-26 00:00:00 until 2025-09-01 00:00:00 (inclusive), but they only range from 2016-01-15 00:00:00 until 2025-08-29 00:00:00.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " baa10y_change_1b mean CRPS = 1.24964 (47 preds)\n", + " baa10y_change_5b mean CRPS = 3.77175 (47 preds)\n", + " baa10y_change_21b mean CRPS = 6.60834 (47 preds)\n", "Running llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " baa10y_change_1b mean CRPS = 0.01098 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04472 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.05657 (6 preds)\n", - "Running llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n", - " baa10y_change_1b mean CRPS = 0.01371 (6 preds)\n", - " baa10y_change_5b mean CRPS = 0.04126 (6 preds)\n", - " baa10y_change_21b mean CRPS = 0.02856 (6 preds)\n" + " baa10y_change_1b mean CRPS = 1.54469 (51 preds)\n", + " baa10y_change_5b mean CRPS = 3.92759 (51 preds)\n", + " baa10y_change_21b mean CRPS = 8.45459 (51 preds)\n", + "Running llmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview] ...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14601.606266066), (, 14601.671521627), (, 14601.681623467), (, 14601.692290558), (, 14601.707210408), (, 14601.738401278), (, 14601.820073319), (, 14601.832908281)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14602.65782509), (, 14602.66652965), (, 14602.699234731), (, 14602.752376901), (, 14602.863321935), (, 14603.104421603), (, 14603.124895916), (, 14603.154902438)])']\n", + "connector: \n", 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14647.126304261), (, 14647.128446701), (, 14647.513666232)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14648.092288293), (, 14648.093967783), (, 14648.103966654), (, 14648.140482427), (, 14648.146078547), (, 14648.167368636), (, 14648.187515396), (, 14648.212256748)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14648.756047912), (, 14648.80921241), (, 14648.832164751), (, 14648.833953542), (, 14648.842249962), (, 14648.874648672), (, 14648.931442803), (, 14648.942901664)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14650.239991865), (, 14650.262606126), (, 14650.286694447), (, 14650.288800836), (, 14650.298455867), (, 14650.342841887), (, 14650.43350993), (, 14651.128982659)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14649.552407369), (, 14649.562320719), (, 14649.574113519), (, 14649.58785329), (, 14649.59861036), (, 14649.623588122), (, 14649.634384141), (, 14649.645307071)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14652.350820489), (, 14652.359091258), (, 14652.371516257), (, 14652.373200617), (, 14652.407489646), (, 14652.430227028), (, 14652.452424948), (, 14652.473670317)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14651.70007002), (, 14651.70240758), (, 14651.732656462), (, 14651.754050351), (, 14651.765271691), (, 14651.787336742), (, 14651.790124602), (, 14651.791591702)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14653.876079803), (, 14653.884907002), (, 14653.900641982), (, 14653.902679062), (, 14653.904082702), (, 14653.911351002), (, 14653.942226101), (, 14654.709606091)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14653.061182442), (, 14653.071995932), (, 14653.074429332), (, 14653.075803382), (, 14653.082151503), (, 14653.142504417), (, 14653.154601627), (, 14653.235417807)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14656.018219092), (, 14656.020089202), (, 14656.098307275), (, 14656.118836666), (, 14656.129322305), (, 14656.139434666), (, 14656.141573645), (, 14656.169921006)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14655.313818216), (, 14655.334921757), (, 14655.345309637), (, 14655.347081157), (, 14655.356540037), 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14658.373217898)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14658.95325184), (, 14658.99725165), (, 14659.019629973), (, 14659.021397642), (, 14659.022804753), (, 14659.032046073), (, 14659.081169093), (, 14659.092074214)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14660.402827816), (, 14660.404681315), (, 14660.412700456), (, 14660.436768145), (, 14660.475086008), (, 14660.485958338), (, 14660.563822949), (, 14660.56546026)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14659.676869007), (, 14659.71077949), (, 14659.724226709), (, 14659.746538911), (, 14659.748277881), (, 14659.77012649), (, 14659.780710231), (, 14659.828742029)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 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client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14668.809708044), (, 14668.845675616), (, 14668.866526946), (, 14668.890868987), (, 14668.914040038), (, 14668.927098168), (, 14668.929101338), (, 14668.951989979)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14668.116379916), (, 14668.125967457), (, 14668.16427879), (, 14668.186345859), (, 14668.188882459), (, 14668.221881852), (, 14668.223954072), (, 14668.265380961)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14669.521553201), (, 14669.543859792), (, 14669.556678653), (, 14669.578486353), (, 14669.580621123), (, 14669.598833153), (, 14669.634855514), (, 14669.702533686)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14670.27707848), (, 14670.32094021), (, 14670.32273401), (, 14670.34370542), (, 14670.34576494), (, 14670.391605064), (, 14670.418855423), (, 14670.430632603)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14671.003519157), (, 14671.025317747), (, 14671.027413947), (, 14671.038023077), (, 14671.072524068), (, 14671.084131709), (, 14671.107344958), (, 14671.161626133)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14672.493450512), (, 14672.495692662), (, 14672.496851252), (, 14672.518422912), (, 14672.520537302), (, 14672.551468323), (, 14672.597405523), (, 14672.668695365)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14662.096522495), (, 14662.137690164), (, 14662.169560977), (, 14662.171801737), (, 14662.174315027), (, 14662.222750218), (, 14662.25538897), (, 14662.344289682)])']\n", + "connector: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14663.633529822), (, 14663.644731922), (, 14663.657179163), (, 14663.667300222), (, 14663.669410193), (, 14663.701957754), (, 14663.711589364), (, 14663.726087483)])']\n", + "connector: \n", + "Unclosed client session\n", + "client_session: \n", + "Unclosed connector\n", + "connections: ['deque([(, 14671.772801483), (, 14671.781074784), (, 14671.812524846), (, 14671.814651286), (, 14671.822346096), (, 14671.856249577), (, 14671.866083367), (, 14671.888340699)])']\n", + "connector: \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " baa10y_change_1b mean CRPS = 1.54204 (51 preds)\n", + " baa10y_change_5b mean CRPS = 4.23031 (51 preds)\n", + " baa10y_change_21b mean CRPS = 7.79899 (51 preds)\n" ] } ], @@ -3322,635 +4007,635 @@ "text/html": [ "\n", - "\n", + "
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008516600.0000.0000.0000.5000.667601baa10y_change_1bLightGBMFalse0—darts_lightgbm0.801686600.5000.6670.5710.5000.4446
11baa10y_change_1bLinRegFalse0—darts_linreg0.008976600.0000.0000.0000.5000.333611baa10y_change_1bLinRegFalse0—darts_linreg0.834466600.0000.0000.0000.5000.8336
21baa10y_change_1bLightGBMFalse0—darts_lightgbm0.009206600.0000.0000.0000.1670.000621baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.892446600.0000.0000.0000.5000.3336
31baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.010186600.3330.3330.3330.3330.111631baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.928926600.3330.3330.3330.3330.3336
41baa10y_change_1bKalmanFalse0—darts_kalman0.010656600.3330.3330.3330.3330.556641baa10y_change_1bKalmanFalse0—darts_kalman1.057196600.3330.3330.3330.3330.3336
51baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.010986600.5001.0000.6670.5000.667651baa10y_change_1bETSFalse0—darts_ets1.085016600.5000.3330.4000.5000.5566
61baa10y_change_1bETSFalse0—darts_ets0.011116600.2500.3330.2860.1670.000661baa10y_change_1bAutoARIMAFalse0—darts_autoarima1.143306600.3330.3330.3330.3330.2226
71baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.011576600.5000.3330.4000.5000.222671baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]1.149666600.0000.0000.0000.5000.6676
81baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013716600.5001.0000.6670.5000.167681baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]1.335486600.0000.0000.0000.1670.2226
91baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.556691baa10y_change_1bNaiveFalse0—last_value_naive2.000006601.0000.3330.5000.6670.5566
105baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.032046600.0000.0000.0000.3330.8756105baa10y_change_5bAutoARIMAFalse0—darts_autoarima3.312896600.6670.5000.5710.5000.6256
115baa10y_change_5bLinRegFalse0—darts_linreg0.032626600.0000.0000.0000.3330.6256115baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov3.541136601.0000.2500.4000.5000.5006
125baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.032746600.7500.7500.7500.6670.6256125baa10y_change_5bKalmanFalse0—darts_kalman3.593596600.6670.5000.5710.5000.5006
135baa10y_change_5bLightGBMFalse0—darts_lightgbm0.035146600.5000.5000.5000.3330.5006135baa10y_change_5bLinRegFalse0—darts_linreg3.600166600.0000.0000.0000.3330.3756
145baa10y_change_5bKalmanFalse0—darts_kalman0.035166600.6670.5000.5710.5000.5006145baa10y_change_5bLightGBMFalse0—darts_lightgbm3.635956600.5000.5000.5000.3330.2506
155baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.041266600.8001.0000.8890.8330.5626155baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov4.312076600.0000.0000.0000.1670.0006
165baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.042626600.0000.0000.0000.3330.2506165baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]4.491206600.6671.0000.8000.6670.6256
175baa10y_change_5bETSFalse0—darts_ets0.044386600.7500.7500.7500.6670.5006175baa10y_change_5bETSFalse0—darts_ets4.600866600.7500.7500.7500.6670.6256
185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044726600.7500.7500.7500.6670.7506185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]4.888306600.5000.2500.3330.3330.3756
195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256195baa10y_change_5bNaiveFalse0—last_value_naive6.500006600.7500.7500.7500.6670.6256
2021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.028566600.8331.0000.9090.8330.40062021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]5.068606600.8000.8000.8000.6670.6006
2121baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.056576600.8000.8000.8000.6670.80062121baa10y_change_21bKalmanFalse0—darts_kalman5.404956600.8000.8000.8000.6670.2006
2221baa10y_change_21bLightGBMFalse0—darts_lightgbm0.057646601.0000.2000.3330.3330.40062221baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]5.459076600.8000.8000.8000.6670.2006
2321baa10y_change_21bKalmanFalse0—darts_kalman0.057806600.5000.2000.2860.1670.20062321baa10y_change_21bLightGBMFalse0—darts_lightgbm6.026286600.6670.4000.5000.3330.4006
2421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.065426600.0000.0000.0000.1670.80062421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov6.268486600.0000.0000.0000.1670.4006
2521baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066586601.0000.4000.5710.5001.00062521baa10y_change_21bLinRegFalse0—darts_linreg6.386406600.0000.0000.0000.1670.8006
2621baa10y_change_21bLinRegFalse0—darts_linreg0.067796600.0000.0000.0000.1670.00062621baa10y_change_21bAutoARIMAFalse0—darts_autoarima7.011536600.7500.6000.6670.5000.4006
2721baa10y_change_21bETSFalse0—darts_ets0.072926600.6670.4000.5000.3330.40062721baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov7.137146600.0000.0000.0000.1671.0006
2821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075406600.0000.0000.0000.1670.80062821baa10y_change_21bETSFalse0—darts_ets7.389286601.0000.6000.7500.6670.6006
2921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.20062921baa10y_change_21bNaiveFalse0—last_value_naive8.500006600.6670.4000.5000.3330.2006
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_eval
01baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.008516600.0000.0000.0000.5000.667601baa10y_change_1bLightGBMFalse0—darts_lightgbm0.801686600.5000.6670.5710.5000.4446
11baa10y_change_1bLinRegFalse0—darts_linreg0.008976600.0000.0000.0000.5000.333611baa10y_change_1bLinRegFalse0—darts_linreg0.834466600.0000.0000.0000.5000.8336
21baa10y_change_1bLightGBMFalse0—darts_lightgbm0.009206600.0000.0000.0000.1670.000621baa10y_change_1bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.892446600.0000.0000.0000.5000.3336
31baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.010186600.3330.3330.3330.3330.111631baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.928926600.3330.3330.3330.3330.3336
41baa10y_change_1bKalmanFalse0—darts_kalman0.010656600.3330.3330.3330.3330.556641baa10y_change_1bKalmanFalse0—darts_kalman1.057196600.3330.3330.3330.3330.3336
51baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.010986600.5001.0000.6670.5000.667651baa10y_change_1bETSFalse0—darts_ets1.085016600.5000.3330.4000.5000.5566
61baa10y_change_1bETSFalse0—darts_ets0.011116600.2500.3330.2860.1670.000661baa10y_change_1bAutoARIMAFalse0—darts_autoarima1.143306600.3330.3330.3330.3330.2226
71baa10y_change_1bAutoARIMAFalse0—darts_autoarima0.011576600.5000.3330.4000.5000.222671baa10y_change_1bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]1.149666600.0000.0000.0000.5000.6676
81baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.013716600.5001.0000.6670.5000.167681baa10y_change_1bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]1.335486600.0000.0000.0000.1670.2226
91baa10y_change_1bNaiveFalse0—last_value_naive0.020006601.0000.3330.5000.6670.556691baa10y_change_1bNaiveFalse0—last_value_naive2.000006601.0000.3330.5000.6670.5566
105baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.032046600.0000.0000.0000.3330.8756105baa10y_change_5bAutoARIMAFalse0—darts_autoarima3.312896600.6670.5000.5710.5000.6256
115baa10y_change_5bLinRegFalse0—darts_linreg0.032626600.0000.0000.0000.3330.6256115baa10y_change_5bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov3.541136601.0000.2500.4000.5000.5006
125baa10y_change_5bAutoARIMAFalse0—darts_autoarima0.032746600.7500.7500.7500.6670.6256125baa10y_change_5bKalmanFalse0—darts_kalman3.593596600.6670.5000.5710.5000.5006
135baa10y_change_5bLightGBMFalse0—darts_lightgbm0.035146600.5000.5000.5000.3330.5006135baa10y_change_5bLinRegFalse0—darts_linreg3.600166600.0000.0000.0000.3330.3756
145baa10y_change_5bKalmanFalse0—darts_kalman0.035166600.6670.5000.5710.5000.5006145baa10y_change_5bLightGBMFalse0—darts_lightgbm3.635956600.5000.5000.5000.3330.2506
155baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.041266600.8001.0000.8890.8330.5626155baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov4.312076600.0000.0000.0000.1670.0006
165baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.042626600.0000.0000.0000.3330.2506165baa10y_change_5bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]4.491206600.6671.0000.8000.6670.6256
175baa10y_change_5bETSFalse0—darts_ets0.044386600.7500.7500.7500.6670.5006175baa10y_change_5bETSFalse0—darts_ets4.600866600.7500.7500.7500.6670.6256
185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.044726600.7500.7500.7500.6670.7506185baa10y_change_5bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]4.888306600.5000.2500.3330.3330.3756
195baa10y_change_5bNaiveFalse0—last_value_naive0.065006600.7500.7500.7500.6670.6256195baa10y_change_5bNaiveFalse0—last_value_naive6.500006600.7500.7500.7500.6670.6256
2021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]0.028566600.8331.0000.9090.8330.40062021baa10y_change_21bLLMP + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bllmp_sampled_trajectories_baa10y_v1_cov_h48_n8[gemini-3.1-flash-lite-preview]5.068606600.8000.8000.8000.6670.6006
2121baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]0.056576600.8000.8000.8000.6670.80062121baa10y_change_21bKalmanFalse0—darts_kalman5.404956600.8000.8000.8000.6670.2006
2221baa10y_change_21bLightGBMFalse0—darts_lightgbm0.057646601.0000.2000.3330.3330.40062221baa10y_change_21bLLMP (target)False0—llmp_sampled_trajectories_baa10y_v1_target_h48_n8[gemini-3.1-flash-lite-preview]5.459076600.8000.8000.8000.6670.2006
2321baa10y_change_21bKalmanFalse0—darts_kalman0.057806600.5000.2000.2860.1670.20062321baa10y_change_21bLightGBMFalse0—darts_lightgbm6.026286600.6670.4000.5000.3330.4006
2421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov0.065426600.0000.0000.0000.1670.80062421baa10y_change_21bLinReg + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_linreg_cov6.268486600.0000.0000.0000.1670.4006
2521baa10y_change_21bAutoARIMAFalse0—darts_autoarima0.066586601.0000.4000.5710.5001.00062521baa10y_change_21bLinRegFalse0—darts_linreg6.386406600.0000.0000.0000.1670.8006
2621baa10y_change_21bLinRegFalse0—darts_linreg0.067796600.0000.0000.0000.1670.00062621baa10y_change_21bAutoARIMAFalse0—darts_autoarima7.011536600.7500.6000.6670.5000.4006
2721baa10y_change_21bETSFalse0—darts_ets0.072926600.6670.4000.5000.3330.40062721baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov7.137146600.0000.0000.0000.1671.0006
2821baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.075406600.0000.0000.0000.1670.80062821baa10y_change_21bETSFalse0—darts_ets7.389286601.0000.6000.7500.6670.6006
2921baa10y_change_21bNaiveFalse0—last_value_naive0.085006600.6670.4000.5000.3330.20062921baa10y_change_21bNaiveFalse0—last_value_naive8.500006600.6670.4000.5000.3330.2006
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"AutoARIMA", "LightGBM", - "Kalman", - "LLMP + cov", "LightGBM + cov", + "LLMP + cov", "ETS", "LLMP (target)", "Naive" @@ -4719,20 +5404,20 @@ "showlegend": false, "type": "bar", "x": { - "bdata": "YZqbvBY+nT/FIKxtr/asP+GMidKNgq0/e0Msk1+YrT+jJ5tsCr+wP9zgaMd5C7E/9Z/ujPNasT95WFWrkqqyP5XGyV65TbM/yPUoXI/CtT8=", + "bdata": "MD97lT9GFEBL5Ahvqp4VQGCThm0V1hVASw1VPugaGEBpLwfB7BIZQEktVcCrixlAlT/HNM4LHEBb6Ub9bowcQJM7vqWejh1ABAAAAAAAIUA=", "dtype": "f8" }, "xaxis": "x3", "y": [ "LLMP + cov", + "Kalman", "LLMP (target)", "LightGBM", - "Kalman", "LinReg + cov", - "AutoARIMA", "LinReg", - "ETS", + "AutoARIMA", "LightGBM + cov", + "ETS", "Naive" ], "yaxis": "y3" @@ -5681,7 +6366,7 @@ "outputs": [ { "data": { - "image/png": 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32HC1t99+2zwE7ubNm8ycORMwJaaeZdOmTeZ/t2vXji+//DLD8pO4u7vz3XffYWtrS0BAgDnZlf5Yfn5+nDhxgvnz53P9+nWSkpIy9FJ62jXl7+9vjsHNzY0jR45k2HdUVBRnz54FTHWOZsyYQcGCBWnSpAmhoaHmWNK8zDWaNvwwM6pXr87hw4fNyyVKlDBfq5cvXzZ/mfbw8GDq1KnY2NjQuHFjrl27xo4dO9BqtezcuZMePXpk2K+joyPz5s3L0M7Lly83J/latmxpHqLapUsXtm3bRnJyMn///TcTJkwgISGB/fv3A6brcu7cuebhmg0aNMhwrNq1azN//nyWLVvG3bt3SUlJybA+ra0ye30HBARw9epV83KRIkUy1WMzMDAQHx8fbt++za5du5gwYQIqlcrcHgUKFDAnbtPf5yVKlMDf35+CBQsCMGzYsOce69/HTXsvSjN9+nTzv99++23zkOYuXbqYr79NmzaZk11169Zl3rx5HDlyhAcPHmToLQqmc/gyya7MvO+k9RQE03WWlkAqV64c9erVY9euXcTGxnLw4EFatmxpTqABDB06lC5dupiPkfajQmakFaYHU9s8jY2NDTNnzsTDw4PXXnuNNWvWkJyczK1btzJs97whw9klfazp/84IIYTIeZLsEkIIYXFNmjShQoUKXL58mXnz5uHi4vLYNumHCgYFBT11GEpoaKg5kVCwYEEmTJjAyJEjzet79er1QsPIIiIinnisCxcuUKJECWrUqMHJkyfZuHEjGzduxN7eHn9/f5o3b857772Hra0tN2/eND9v//795i+L6RmNRq5fv46bmxtdu3Zl/vz5BAcH88Ybb6BSqShRogQNGzakT58+eHp6Ym9vT9u2bdm8eTOHDx+mc+fOqNVqypYtS5MmTejVq9cTz2Oa9DFVqVLF/G83Nzd8fHwIDQ3FaDQSFhb22PlKXwMrfYIkISHhmecy/TGf1QMsvapVq5p7yKUlGP59rGnTppl7cDzJ0+JK/zqetO/0w818fX0zbFO1atXHkl0vc41ml/THrlChAjY2NublypUrm3sspd8uTWBg4GMJzfTbrVu3ztybLL2EhAQePHhARESEueeTj4/PY3XJ0uj1evr3759hSNuT9glk+fp+HoVCQbt27Zg/fz7R0dGcPHmSQoUKmXsQtWzZ0nzddejQwZz8++ijjwBTEjYwMJC3337b3MMwM5503ac/12k9TP8tLa7ExMTHhhD+W/ohtC8iM+87YWFh5kRyZGTkM69xyHgPpb/mszKU99/DbdMrVaoUHh4egKmgfIECBUhOTn7snLxMza7sjlUIIUTOkmSXEEKIPGHgwIGMHDmSPXv2PLMmy/MkJydnWE4//AUw97zJDkqlkp9//pk///yTw4cPc+3aNe7evcvZs2c5e/Yst27d4rPPPsv0/tJiHzZsGGXLlmXXrl1cuXKFW7duERoaSmhoKIcPH+bPP/9ErVYzadIkatasyb59+7h69Srh4eEEBQURFBTE+fPnmTdv3ku9rucNy0rfWyF9r5ec+GL3vGNptVpWr15tXj9s2DAqV66MWq1mxIgRxMTEPDWu9PtWqVSP7Tu9lxl2+DT/vkYtLStf5l/ktZw+fdqc6PL09OTDDz/Ex8eH+/fvmwuWp6+LlVPXd5oOHTowf/58wNTDLv15SN8zqly5cvzxxx/8+eefnD9/nuvXrxMdHc2uXbvYs2cPv/zyC4GBgZk65sue67TzvHPnTnOiq1SpUnzwwQd4eXlx8eJFc4+xl70PM/O+k1lpNbOe5kXvp/RF95+VzPt3r6/093V6uVWzK32s6ZPlQgghcp4ku4QQQuQJLVq0oEyZMuZhXv9WsmRJ87/TzyCWXnJycoZaVVeuXDHXnFKpVOj1evbv38+6desyfIFJXysq/ZdtgGLFij11yIvRaMTR0ZFevXrRq1cvwFSM/J133jEPj/rss88oUaKE+TlPm+Hx37G3a9fOPLQvNTWVcePGsX37dkJCQrh58yZlypRBrVbz5ptv8uabbwLw8OFDBg8ezJkzZzh06BBJSUk4Ojo+MfYSJUpw4MABwFTYPG12ttjYWPOQH4VC8cz6OC+qRIkS5l5t+/bte6mhVv8WGxtLamoqYBrO2K9fP8BU9D4uLi5L+/b19TX/+9atWxmGdKYNL0vvZa7R7JL+2EFBQeh0OnNy8Pz580/cLs2TEg/pt3varIZpryUuLg6lUonBYOD27duEhoZSunTpx7Z/8OCB+d/t2rWjc+fOAGzZsuWJrymz13f6+F8k0VO6dGkqVqzIpUuX2LlzpzmhUrhw4QzF741GI2XLlmXcuHHmx3bs2MHIkSMxGAzs3r0708mup53rtKT84sWLn1hfLi3Zlf4cdu/e3TxzaGZ7Kj3P8953ihcvbh5e6uvry6ZNmx5LKGm1WvO/fXx8uH79OmAaXpnWo+vcuXMvFFf66yksLOyZE5nkJemHWz+tx6MQQoicIckuIYQQeYJCoWDAgAGMHTv2iev9/f0pV64cISEhnDhxgnHjxtG6dWvUajXh4eGcP3+e3bt3m2cp0+l0jB8/Hq1Wi1qtZu7cuYwZM4bo6GimTZtGvXr18Pb2BjL2Bjh9+jT79+/HycnpuUNY7t+/z4ABA2jdujWlS5fGw8OD27dvm+tzpdXSqVevHu7u7kRHR7NhwwZcXV2pV68eer2eO3fucPr0aYKDg81Fqt977z0qVKhA5cqV8fLyIjExMUNx47T9tm3blhYtWuDv74+XlxdRUVGEh4cDpi/o6b90/lu7du3MQ/9+++03vLy8KF68OCtWrDDvv0GDBo8Nb8uK9u3bs2LFCsBUM8zBwYGmTZuSnJzMnj17ePPNNzMMLcyMQoUKYWdnR2pqKiEhIfz5558UKlSIuXPnPpa4fFHu7u5Uq1aNM2fOkJqayujRo3n33Xe5fPky27Zte2z7F71GwVRI/s6dO0DW6giVL1+e0qVLExoaSkREBJ988om5QP2uXbsAUz2jtBn1nqdly5bMnDkTjUbDokWLUCgUVK1alZSUFMLDwzl27BgpKSksXLgQV1dXGjZsyL59+9Dr9QwZMoSBAwdSuHBhc/J6ypQpGQrG79y5k+rVqxMfH2+u+fZvmb2+01+jBw4coEaNGtjZ2VGuXLnnDnXs0KEDly5dIjIyksjISMB0naZPSi1evJjjx4/TuHFjihQpgoODAwcPHjSv/3fNrBfVvn17du/eDcC4ceMYOHAgJUqUIDo6mrCwMPbt20fDhg15//33M8xWunbtWnx8fAgLC8tyLzfI3PtOWlvv37+fW7duMXToULp27YqTkxN37twhKCiInTt38uuvv1KsWDGaNGliTnDPmTMHe3t7HB0dmTVr1gvFlj6ZePnyZTp16pSl1/qy91paTbegoCDzYzExMebHy5QpkyGplX7IbmYTokIIIbKHJLuEEELkGW3btuWnn356YqFzhULB5MmT6d+/P/Hx8eYaWU8zf/588xeNXr16UbduXT799FNGjRpFfHw8n3/+uXlGvbREVWRkJLdv32bIkCFA5oawXL9+/YkzFqa9HjAVAJ80aRIffvghGo2GZcuWsWzZsgzbpv8SGx0dzapVq1i1atVj+yxTpgx+fn4A3L1794m9h+D5iaqqVavSt29fFi9eTGpqKtOmTcuw3sPDgwkTJjz1+S+jcuXKDB482Hy+Vq9ebR6CCNCtW7cX3qdSqaRr16789ttvaLVavvjiC8DUiywtwZgVo0ePpnfv3mi1Wg4dOmROVPn5+WWYZRBe/BrNTumPnZiYyLZt2zIk5BQKBZ988skzZyhMr3Dhwnz66ad8+eWXaDQafvrpp8e2SZ+YnDhxormWVHh4eIbhu2nbValSxXzewsPDGTFiBGBKAjypnTJ7fafVdUubTCKt8PnTekml17ZtW6ZPn45erzc/9u8ehzqdjgMHDph7QqanVCrNvSJfVuvWrfnnn3/YsGED9+/f56uvvnpsm7RC/02aNMHT05OIiAguX77M+++/D5jO4enTp7MUR2bfd/773/+a2/ppNQjTdO3alT/++IPg4GBiYmLM7ynpe7tmRpEiRcy98NImkrCEtJpt6V27ds38+L97QaZNKOHh4ZFveqMJIYS1eHyObyGEEMJCVCoV/fv3f+r6ihUrsnr1at566y18fHywsbGhQIEClCtXjrfeeotFixYBpl/d02rx+Pr6mpNXbdq0oVmzZoCpUPyaNWsA03CpH374gerVq+Pk5JTpeF1dXRkyZAg1a9bE09MTtVqNvb09fn5+DBs2jE8//dS8bePGjfn999/p2LEj3t7eqNVq3NzcKF++PL169cowI1v//v1p1qwZRYsWxcHBAbVaTbFixcyvMW3Y0IgRI2jQoAHe3t7Y2tpia2tLqVKl6NOnDzNmzHhu/B999BHTp0+nZs2aODs7m4/TvXt3/vzzT3x8fDJ9LjJr6NCh/PTTTzRs2JCCBQuiVqvx8vKiRYsWL328UaNG0aNHDzw9PXF0dKRp06YsXLgQe3v7LMdbtWpV5s2bR6VKlbCxsaFYsWJ88skn5lnlgAzDEjN7jeaEypUr88cff9C5c2fzNVagQAEaNmzI/Pnzefvtt19of2+88QZLly6lRYsWFCpUCLVaTaFChcxJy4kTJ5q3LVKkCKtXr6Zv376UKlUKOzs7HB0dKV++PK1atQJM9/dPP/1Es2bNcHFxwd3dnffee8+coPy3zF7fbm5ufP/991SoUOGF29zT0zNDQszPz8+c1EnTqFEj3nzzTcqVK0eBAgVQqVS4urpSv3595s2bl6nZH5/n66+/5uuvv6ZmzZq4uLhgY2NDkSJFqFu3LuPGjTO3nZOTEwsWLKBOnTo4Ojri7e3N0KFDGTp0aJZjyOz7Tlpb9+nTx9zWTk5OlCpVik6dOvHjjz+aZ5S0sbFhwYIFtG/fHmdnZ5ydnWndujVLlix54fjS7rm0IZV5XWJiojkx16FDh6fWDxNCCJEzFBqNRqYJEUIIIYR4AqPR+MQ6S6NGjWLr1q0AzJo1K9PDA4UQLycpKYnWrVsTExND3759n9jLKi9ZtWoVkyZNwtbWls2bN2e6V6UQQojsIT27hBBCCCGe4s6dOwwePJjdu3dz69Ytrl27xk8//WQeIphWf00IkbMcHR3p06cPYBoC/bwZHy3JaDSa6xN269ZNEl1CCGEB0rNLCCGEEOIpwsPDn1qTycbGhu+++47mzZvnclRCCCGEEOJZpEC9EEIIIcRTuLq60q1bN06dOsX9+/fRarV4enpSs2ZNevXqhb+/v6VDFEIIIYQQ/yI9u4QQQgghhBBCCCGE1ZCaXUIIIYQQQgghhBDCakiySwghhBBCCCGEEEJYDUl2vQCj0Uh8fDxGo4z8FEIIIYQQQgghhMiLJNn1AhISEvDw8CAhIcHSoYgXEBsba+kQRDaTNrU+0qbWR9rU+kibWh9p05wl59f6SJtaH2lT6yXJLmH1DAaDpUMQ2Uza1PpIm1ofaVPrI21qfaRNc5acX+sjbWp9pE2tlyS7hBBCCCGEEEIIIYTVkGSXEEIIIYQQQgghhLAaaksHYC2MRiM6nQ69Xi8F7PMYjUZDcnKypcMQL0mhUGBjY4NKpbJ0KEIIIYQQQggh8gFJdmUDjUbDnTt3SExMtHQo4imioqIsHYLIAqVSia+vL87OzpYORQghhBBCCCFEHifJriwyGAxcu3YNtVpN8eLFsbOzs3RIQlgVo9HIgwcPuHXrFn5+ftLDSwghhBBCCCHEM0myK4s0Gg0Gg4HixYvj5ORk6XDEExiNRhQKhaXDEFng5eVFfHw8Wq1Wkl1CCCGEEEIIIZ5JCtRnUVp9LkmmCJFz0u4vqYcnhBBCCCGEEOJ5JNklhBBCCCGEEEIIIayGJLusWEJCAi4uLvTr1++52+7du5etW7dm+Zg3btzAzc0ty/vJ60qVKkX58uUJDAw0/6dWqwkMDKRSpUrmfwcGBvL2228D8OWXX1K5cmWqVatGhQoVGD16tIVfhRBCCCGEEEIIYX2kZpcFbTgYxpRfz3L1djxlfQow9t2qdGpQPNv2//vvv1OjRg3Wrl3L7NmznzmT3d69e4mNjaVNmzbZdnyLi7sFSVFgBBSAYyFw9c223a9atYpq1ao99viNGzcIDAzk9OnT5sdWr17N1q1bOXbsGA4ODuh0Oi5evJhtsQghhBBCCCGEEMJEkl3ZzGg0otEanrvdpsO36DtlPwoFGI1w6UYsPSb9w+KxjehQ7/kJGVsb5XPrhC1evJgJEyYwf/58fv/9d/r160dcXBwff/wxR48eRaVSUb16dYYPH868efPQ6/X8888/dOnShZ49exIYGEhMTAwADx8+pECBAhgMptf23nvvERwcjEajwdfXl4ULF1K4cOFMnKFsEh/+7PXaFJhbH4UulbSzZFTZQu/NUKDo49sXKJbtIaZ3+/Zt3N3dsbe3B0CtVlO1atUcPaYQQgghhBBCCPEqkmRXNtNoDXh1Xpnp7dPqbaf9v++U/Zl63oP172Bn+/RZ6S5dusStW7do3bo1Op2OqVOn0q9fP0aOHIm9vT1nz55FqVQSERGBp6cngwYNIjY2llmzZgGm3knPMnPmTDw9PQGYMmUKn3/+OXPnzs1U7NlBMbPSM9cbO81BoUvN+By9Bha1fPL2n8W+cAxvv/02Dg4O5uVDhw5lWP73tgsWLKBMmTI0atSI1157je7duz91eyGEEEIIIYQQQrycPJXs0mq1LF++nIsXL/Lw4UPc3Nxo164djRs3fuL2ycnJLF26lDNnzmBra0uLFi3o3Lmzef2qVavYt28f7u7uDBkyhGLFTL13Hjx4wA8//MDEiROxtbXNldeW2xYtWkSPHj1QqVS0a9eOwYMHc/nyZTZt2sSRI0dQKk3l2tISVi9q5cqVrFixgpSUFFJSUvDw8MjO8POFpw1jfJLChQtz7tw5jh49ysGDB/n555/58ccfOXLkiNVeg0IIIYQQQgghhCXkqWSXwWCgYMGCjBkzBi8vL65du8b06dNxc3OjcuXKj22/fPlyEhMTmTFjBgkJCUydOpVChQrRsGFDQkNDOXXqFNOnT2f//v388ccfjBw5EoBly5bRvXt3q00yaLVaVqxYgY2NDb/99hsASUlJLFq0CDD1Pot9qEFvMKJSKnC0e/wyUKvV6PV683JKSor53wcOHOCHH37g0KFDeHl5sWHDBj777LMcflUZGUc+p95V3J0nP++dP8G7Yg5E9HwqlYr69etTv359hg8fTuHChblw4QLVq1e3SDxCCCGEsLyNh27x7W8XuBoeT9liBRjdPYCO9bOvxqgQ1uZObDIxSVrzspujDUULymgJIURGeSrZZWdnR9euXc3LZcuWpUKFCoSEhDyW7EpNTeXo0aNMmDABJycnnJycaNmyJfv27aNhw4ZERERQsmRJHBwcCAgIYM+ePQAcPnwYV1dXKlbMmYSHrY2SB+vfee52/67Zlfb/JWMb0T6TNbueZsOGDZQuXZrDhw+bH7t8+TJNmzalfYeOfPvtd0z5diZKpZL7Dx7gUcgTJycXwsLCzNsXLlwYo9HIpUuXqFixIsuWLTOvi4mJwcXFhUKFCqHRaJg/f/5z4812z6uxZTRgVNtlGMpoVNuBV/kcr8/1JCdOnMDNzY0yZcoAEBQUhFarxddXPswKIYQQr6qNh27R+5sD5uWLN2Lp/c0Blo5rKAkvIZ7gTmwyLWcfRKN7VCPZTq1k+4gGkvASQmSQp5Jd/6bRaAgNDaVevXqPrbt37x46nY7ixR/NXli8eHE2btwIQLFixfjrr79ITEzk4sWL+Pj4kJiYyMaNG/n0008zdXytVotOpzMvJycnP/c5CoXimbW00nR7rSQ2aiVTfz1HyO04yvm4MvbdKnTMhtkYFy9ezDvvZEy4VahQgWLFilGnbgMOHNhP4wY1UattCKxeg5nf/0Srth347bdfCQwMpEuXLvz3v//l+++/p2PHjhQqVIhu3bqZ99WmTRt+/fVXypcvT6FChWjevDnh4c8pGJ/bXH1h6AmMSVHmZGJ2z8b475pdM2bMoGnTpk/cNioqimHDhhEbG4uDgwMqlYpff/31pYeRCiGEECL/m7ry/BMfHz77KF4F7aldweO5ExIJ8SqJSdJmSHQBpOoMxCRpJdklhMhAodFojJYO4kmMRiPz5s0jJiaGTz75xFxjKk1wcDDTp0/P0KsoNDSUr776iiVLlgCwY8cOc82unj17sm7dOsqXL4+Hhwdr165FoVDQpUsX/Pz8nhjD2rVrWbdunXlZq9WyevVqQkJCcHFxAUwJuaioKMqVK5cvio1HJ2jgSS2uAHcX6xzWaTAYHrt+RP6SnJxMSEgIhQoVwtbWlvj4eAoUKGDpsEQ2kja1PtKm1kfaNPv5996G4RmfxCuXKkCvliVoW6cwturs/ywjbZqz5Pxmv0PXYxm57upjj//yTgXKezvl+PGlTa2PtGn+5O7u/txt8mTPLqPRyC+//MK9e/cYM2bMExMV9vb2aDQa9Ho9KpWpJ1VycjL29vbmbVq2bEnLlqbZ94KCgoiKiqJevXp8/PHHjBs3DqPRyNSpU5k+ffoTfzXr0KEDbdq0MS/Hx8ezevVq3NzczDdEcnIyUVFRAPnilzeVUoHeYHws4aVUKPJF/C9DqVRa7Wt71bi6upqTypl5gxP5i7Sp9ZE2tT7Sptln5c7QZya6AM5fj2fU/PN8+2cIfduVo1ebsngWtH/2k16QtGnOkvObvdZsvP7Ex11dXXF3z52EhbSp9ZE2tU55rruL0Whk2bJlhIaGMnr0aBwdHZ+4XeHChVGpVBnqTN28eRMfH5/HttXpdKxcuZJevXqRkJCAXq/Hy8sLb29vdDodCQkJTzyGjY0NDg4OGf7L7xzt1KZE179yPwajEZ0+T3byE0IIIYSwKhevxzDm5xOPPZ7221xN/0IolY8+rN2PSeGbX89Tte96hs0+woXrMbkVqhB5hk5veGIPR6XCVKReCCHSy3M9u5YvX05ISAhjx47FyenpXVHt7OyoXbs2a9asYciQIcTHx7Nz584MBe7TbNy4kVq1auHt7Y3BYECn05mTZDqdDmdn5xx7PXmNrY0SF0cbklJ1ph5eYEp+GSEhSUtBZ1ukE5QQQgghRM6IT9LSZ8pBkjWmWa8LFbDFs6AD1+8mUNanAGO6B9Chni+3HiSycNMVlm2/Rnyiaea5VK2BlTuvs3LndRpW9mJQJ39a1yqKSpXnfr8WItupVUrmvhvI3uAIPJxtCX7wkINXoxjUqJTU6xJCPCZPJbsiIyPZtWsXNjY2fPTRR+bH69evT+/evfnuu+/w9/enY8eOAPTs2ZMlS5YwcuRIbGxsaNGiBQ0bNsywz7t373LmzBkmTpwImIa09erVi+nTpwPQp0+fV66ek62NElsbU30und5IXKKpjpfBYORhshYX+WVECCGEECLbGY1GPvz+KNfumEYV2KiV/PZZE2r4FXpsW18vJ77oG8jo7gH8vvsG8zcGczX80WiEA+cfcOD8A0oWdmZAh3K807IMBeQznHgFNPE3TfAUUMyVboG5P8u6ECJ/yLMF6vOi+Ph4PDw8iIyMzFCzKzQ0lHLlyj11yGVel6LRk5j8aNZJR3s1DnbPn1EyvzAajVKzK59LSkoiJCSE0qVL4+DgQHR0tIyttzLSptZH2tT6SJtm3bwNwXy64JR5eergGvRv/+SJkv7NYDCy+9Rd5m4IZs/pe4+td3ZQ806L0gzo4Efpoi6Z2qe0ac6S85s9ElN1/H3xPl2qFUWlfPpn+mSNHgfbnP0OI21qfaRNrder1aVJPJG9rQq7dH8YklJ1aP81pa8QQgghhHh5x4Mi+e/i0+blro2L069duUw/X6lU0KJmUVZ/2ZSDc9rRu03ZDF/sHybrmL/xCrUHb+Ldr/ax7+w9jEb5TVvkf/P332Dc2ou8u+j4E6/pFK2e73df5bXp+7gbl2KBCIUQeZEkuwQATvZqVKr//1JiNH1gMsgHJCGEEEKILIuKS6Xv1IPmyYDK+RRgxge1X7rnefnirkz/oBbnl3bmv72qUtTj0egCoxG2Hguny4Q9NB7+Nyu2XyM5VfeMvQmRd4XHJrPo4A0AGpfzeOI9YzTC6lN3iEnSMm3blVyOUAiRV0myy4LCY5O5EB5n/i88Njnb9l2qVCkqVKiATvfow02tWrXYu3fvE7dXKMDF0YYPhw/hwP5/TPW7kvL3B6O7D+9yKeoSl6MucynqEncf3s22fZcqVYozZ8489nj//v3Zs2fPc5//+eef8+GHHz5x3ZkzZ1i1alWGx7RaLV988QUVKlQgICCA6tWr06VLF3MMe/fuxdHRkcDAQAIDAwkICGDBggXm56fVpjt9+tEvygkJCbi4uBAYGPj8FyyEEEKIl2IwGBk84xB3IpMAcLRTsXRsw2ypkermYseINypyakFHFo1pQK3yHhnWX7oRx4gfjlG17wYmLz/H3aikLB9TiNz07bYQUnUGihW0p2+DEk/cxsFWxZhWpl6Sm87f41RYbC5GKITIq/JUgXprcjfu2YmrFK2etrMPkJpuuKCtSsmqgXUo7Gr/2PZFXF98hpHU1FQWLVrEoEGDMrW9Sqlg0cKFJCSZZvzR6gwkpehwtM97l8m9xMdrVaSn0WvouqErGoPG/JiN0oYlrZfg5ej12PaFnQpnS1wLFy7M8j7OnDnD+vXrefvtt82P9e3bl4cPH3Lo0CHc3NwA2LlzJ8HBwVSrVg0Af39/czLr9u3blC1blrfffhsXF1Pdjho1arB48WJ++OEHAH7//XcqVKiAVqt9bkxLly7lxo0bfP7551l+fUIIIcSrZMYfF9l96tHnlu/er0X5Eq7ZegwbtZLXGxXn9UbFOXklivkbgll3IMzckywqPpUZf1zk+78u8XrD4gzq5E/1JxTFFyIvOXEzhs0XTPfOJ639sLd5ej2u9pULs+LoLU6GxTJ5SxB/DqyD8hn1vYQQ1i/vZTGsRP0pz+7d8223yhkSXQAavYGuPx9+4vbXv2n3wjF89tlnTJgwgR49emQonr9y5Uq+//57NBoNBoOBr776yjzDZetWzRk0ZCgVK1WlRdMGnL14FbXaCVu1kj59+lCtWjVGjBjB8ePHGTt2LPHx8ej1esaNG8ebb775wjG+rFZ/tXrm+q/qf5Uh0QWgNWh57+/3nrj9uZ7nsiWupk2bMmLECF5//XXu3r1L7969uX37Nj4+Pri7u+Pv729OGN27d49OnTpx7do1ChcuzJ9//olOp+Ozzz4jLi6OwMBA6tSpw8cff8zatWsJCwszJ7oAWrRo8dQ44uPjcXJywsbm0a/GXbp0YeHChXz33XfY2dmxdOlS+vTpw/z587PltQOEh4fz4YcfEhwcjEKhoFOnTnz11Vc8ePCAIUOGEBISgtFoZOjQoQwaNIhff/2VVatWsXHjRsA0mUDZsmVZs2YNVatWzba4hBBCCEvYe+YeU1aeNy/3blOW/zQrlaPHrOFXiHmj6vN5n2os3nKVX7ZeJSo+FTDNwr36n5us/ucmtSt48G7TYrzdqiBqlQz2EHmLwWBk8pZgAGqWKEibSt7P3F6hUDC+nT9d5x7lXHg868/epUtg0dwIVQiRR8lfNitWtWpVmjZtysyZMzM83rp1aw4fPsypU6dYt24dAwcOJDU11bze3lZFiZLFCahcha1/b+Jhkpb4+AQ2bNjAe++9R2xsLIMGDWLFihUcP36c7du3M2rUKMLDw3P7JeZpI0aMoG7duly8eJFffvnlsSGkR48eZcmSJVy8eBEvLy/mzZuHl5cXX3zxBU2bNuX06dPMnTuX06dPU7Zs2efOEhIcHExgYCCVKlWievXqfPPNN9jbP+ol6OjoSIsWLVi3bh1BQUEYjUYqVKiQra+5R48eVK9enXPnznH27FmGDx8OwPDhw/Hz8+PcuXPs2rWLyZMnc+TIEbp27cqRI0e4d8/0q93evXtxc3OTRJcQQoh8LzwyiYHfHiKtBGrVMm5MHlA9145fpJAj43tU4eziTsweVpuKJTP2Jjt2OZIRP52lev+NzF59iZiE1KfsSYjct+bMHS7ciUehgAntymeqvl3lYq50/X+C67sdISRKrTohXmnSsyuHHBrb9JnrnzZTyJLeNfEvnLnpojPjyy+/pE6dOgwePNj82PXr13nvvfe4ffs2arWa6Ohorl+/Tvny5c3buDjY0P29Hvz263I6de7Kit9W0axZMwoVKsSWLVsIDQ2lXbuMvc2Cg4MpVqxYtsX+LNu7bX/m+gdJD574+JxmcyjnlvmZj7Ji165dfPvttwAULlyY9u3bZ1jfunVrChUyDSGoW7cu58+ff2wfT3Lt2jXeeOMNkpOTqV+/PosXLwYeH8bYsGFDatasSfXqjz5Y9+3bl88++4wqVarQu3fvZx5n9erVTJ48GTBNyavRaFi/fj1gqgGWlshK8/DhQw4cOMDWrVvNj3l6egKmIZcnTpwAwMvLiy5durBz507q1q1L165dWb58OaNHj+aXX355blxCCCFEXqfVGeg/7aC5R5Wrkw1LxjbE3vbpw7ByioOdmvdaleHdlqXZf+4+8zZcYdvxcHMSLjwyiS9/Ocu3v13gP81KMbCTH/6+2TvMUogXteuy6bN8t8BiVCpaINPP+6hFWbZevM+DhFTm7b/ORy1y53O/ECLvkWRXDnlejS2DEezUygxDGe3USsp5u7xUfa6nKVmyJN27d2fSpEnmx7p3784333zDG2+8AUChQoVIScmYfFMqFbz95ht8OmYU9+7d5dcVy/no41GAaahZpUqVOHjwYLbF+aKeV2PLaDRiq7TNMJTRVmlL2YJls60+14v69y9S6XtdqVQq9Hr9E58XGBjI1atXiYmJwc3NjTJlynD69GmWLl1qTj79m4+PD3Xq1GHXrl0Zkl1169blzp07XL58mYsXL3Ly5MmnxvvGG2+Yr5HsrtmV/lz07duXvn37MmTIEDZt2sSMGTOy5RhCCCGEpXz5yxmOXY40L88ZWZcShZ0tGJHpb2/jqoVpXLUwoXcSWLDpCr/uuEZiiunzR7JGz9KtV1m69SrNqhdmUEd/mlUvInWPhEXM6V6NDefu0qDMi9WW8y5gz+DGpTh3O45ugbnzI7wQIm+SZJeFFCvowK6PXyMm8VEyxs3JlmIFsy/RlWbChAlUrFjRXL8pJiaGUqVM9SJWrFhBTEzME5/n4uxIl27d+HbKZG7cCKXRay1I1RqoX78+169fZ+fOnea6UWfOnKFixYrY2tpme/wvo4hzETa+vpGY1BgwAgpws3OjiHORXIuhWbNmLF26lM8++4z79++zefNmBg4c+NznFShQgLi4OPNyuXLl6Ny5M/3792fRokUULFgQgMTExKfuIy4ujpMnT/Luu+8+tm7WrFlERkaaC9dnF2dnZxo3bsz06dMZN24cABEREXh6etKiRQsWLFjA5MmTiYiIYO3atfzxxx8A1KlTB4BRo0bRokWL5w7XFEIIIfKyjYdu8dO6YPPysG4VaFvHx4IRPa50URe+GViDwe18+PtkDPM3XuHm/UefK3afusfuU/coW8yFgR39+U+zkjg7ZH32SCEyS6lU8Hq1l6u5NahRKUnSCiEk2WVJxQo65Ehy6988PDwYNmwYn332GWBKdrz55psULFiQpk2bUrx48ac+d0C/vtSpU4dhIz5GpVKRmKzF1bUgmzZtYvTo0YwaNQqtVkvx4sVZu3Ztjr+WF1HEuQhFnItgNBozNc7/RbVp0yZDAfjDhzNOLjBr1ix69+5NpUqVKFq0KHXq1DEnqp6lefPmTJ8+napVq1KvXj3mzp3LkiVLmDx5MnXr1kWtVuPm5oanpydjxowxPy+tZheYZuJ899136dSp0xP3n1OWLVvG8OHDCQgIwMbGhk6dOvHFF18we/Zs3n//fapUqYLRaOTTTz81J7kAevfuzSeffMKWLVtyLDYhhBAip4XeSWDY7KPm5fqVPJnQo4oFI3o2F0cbBncuz4AOfmw7fod5G4I5cP5RKYir4QmMmXuCycvP0rN1Gfq198PXy8mCEQtrF3QvgfJZLOkiiS4hBIBCo9EYLR1EfhEfH4+HhweRkZEUKGAaO56cnExoaCjlypXLMOOhNTEYjcQ91GIwmC4VlUqBq5MtOZA/yhE5lex6nuTkZGxsbFCr1URFRVGvXj2WL1+eIckjMicpKYmQkBBKly6Ng4MD0dHR0gPMykibWh9pU+sjbfpsyak62ozewYXrsQB4FbRnz+w2FHbP+R82X9aT2vTC9RjmbQjmr39ukqrNOHO4UqmgQz0fBnX0p05FD4t8vspP5J55MUevR/Pe4hM0L+/J9Dcq42SXtX4ZBoORjefusuXCfX5+p1q2JMGkTa2PtKn1ktkYxXMpFQpcHNXw/78Per2RxBStZYPKB0JCQqhVqxbVqlWjUaNGDBkyRBJdQgghhJUaO++kOdGlVCqYP7p+nk50PU1AKTd+GFGXs4s7M+7dyni7PaoxajAY2XDwFu3H7qT5R9v5ffd1NNon1xwV4kXoDUYmbTEN/01M1eGYDZM5XItIZPSaC+wOjuCv03eyvD8hRP4iyS6RKWqVEsd0v66kagykaOTDzbNUqVKF06dPc+bMGS5dusTIkSMtHZIQQgghcsDKnaGs2BFqXh73bmUaVfG2YERZ51nQnlFvB3BmUSd+/qguVctm7Plw9mo07888QrV+G/j2twtExD55pnEhMmP1qXCC7iWgVMD4duWzpddgOW9n3qhuKlI/Y2cICSm6LO9TCJF/SLIri9LeiI1G6x8N6mCnwtbm0SWTmKJDp7f+1y3yDhkuIYQQIq+5eD2GMT+fMC+3rFmUD9+oaMGIspetjYq3mpZi14xWbJnagk4NfDMMB7sfk8KUleep0mc9Q2cd4Xzokyc+EuJpElK0zNgZAsBbNX2yXLMrvY9alMXJTkXkQw3z9l3Ptv0KIfI+SXZlUVqB8mfNjGdNnB1sUKr+/wHHCAlJWl6BPJ+wsNTUVABUqqx3aRdCCCGyS3ySlj5TDpL8/97uvl5O/PxRXasskK1QKKhT0ZMlYxtyakFHhnUtj6vTo4l6NDoDv+26TpMRW+n86S42H76NXm94xh6FMJmzN5ToRC3Odmo+bFYmW/ft4WzHkMalAVh86AZh0UnZun8hRN4lszFmkUqlws3NjXv37gHg5ORk9b1P1MBDjRb+n+SK0qbgaC+XksgZRqORe/fu4eTkhFot15kQQoi8wWg0MuL7o1y7kwCAjVrJ4k8a4OZiZ+HIcp6vlxOf9wlkdPfK/L77OvM2BHM1PMG8/sD5Bxw4/4AS3k4M6ODHuy1LU8DJ1oIRi7zqRlQiy46EATC0aWkKOWf//dO7fgl+P3GbWzHJTN12hTndq2X7MYQQeY98c8wGRYoUAeDu3bsWjiT36PRGNOlm6LG1UaJWWXeST1iOUqmkRIkSVp9IFkIIkX/M33iFDQdvmZcn969Odb9CFowo9znZq+nbrhy925Rlz+m7zN0QzO5T98zrb95PZMKi03yz8jzvtijNgA5+lC6afUPURP43f/8NtHojJQs50qNO8Rw5hp1aydg2fnzw21m2X3rA0evR1Ckls+8JYe0k2ZUNFAoFRYsWxdvbG61W+0rU7zIajXzz63m2Hg0HTLMOzR5W+7HipXlBXFwcrq6ulg5DvCSFQoGtrS1KpYy6FkIIkTccD4rkv4tPm5e7Ni5O33ZlLRiRZSmVCprXKErzGkUJvhXH/I1X+GP3dZJSTcM7E5N1zN94hQWbrtC6VjEGdvKjcRVv+RFLMKGdP0UK2FPZpwC26pz7rNeyghd1S7kRm6TN0eMIIfIOhUajsf7MTDaJj4/Hw8ODyMhIChQoYOlwLC4pRUfr0du5dCMOAG83e/bMboO3W96aZjs6Ohp397yXhBMvT9rU+kibWh9pU+sjbWoSFZdKkw+3cifSVPunnE8BdkxvhYujzXOemffkZJvGJKSyfPs1Fm4KITzy8TpJFUq4MqijP280KYGDnXX+/i73TN4SlaihoIMNqizU1JM2tT7SptZL0tripTnaq1kytiHODqYPKPdjUhj43SF0UoxUCCGEEFZIrzcwaPohc6LL0U7F0rEN82WiK6e5udgxvFtFTi3syOJPGlC7gkeG9ZdvxvHhj8eo0mc9k5ef426UFA5/laRN6pCbCjnZZinRJYTIXyTZJbKkbLECfD+8jnn5wLkHTPn1vAUjEkIIIYTIGTP+uMSe049qUs0YWpvyJaRUwrOoVUo6NyzO39NasmN6K95oUiJDndfoBA0z/rhItX4bGPTdIU5eibJgtCI3xCVraT5zP1//HUxCitYiMZy8GcOvR8MscmwhRO6QZJfIss4NizOwo595eeafl9h+PNyCEQkhhBBCZK89p+8y9bdHP+j1bluWN5uUtFxA+VB1v0LM+7g+ZxZ14uP/VKJQgUcz7+n0Rlb/c5NWH2+nzegdrN1/E61ORgtYox/3XCPioYa/ToWj1ed+RZ3dwRG8vfA4k/8O5kZUYq4fXwiROyTZJbLFF32qUcP/0QxEQ2Yc4dYD+eMhhBBCiPwvPDKJQd8dJm0Ooqpl3Zncv7plg8rHihRy5NP3qnB2cSe+H16biiUz9o47HhRJ/2mHqD5gI7NXXyImIdVCkYrsdi0ikRVHTbOYDmtWBncn21yPoWGZQpQq5IhWb2TK1iu5fnwhRO6QZJfIFrY2KhaPaYC7i+kPVuxDDX2mHCBVm/vj8YUQQgghsotWZ6D/tINExZsSLq5ONiz5pAH2tioLR5b/OdipebdlGfZ935Z1k5vRpk4x0k/QeCcyiS9/OUvl3uv5eM5xgsLiLBesyBbfbA1GZzBS2sORd2v7WiQGW7WSsW39AdgVFMHBazJ0VghrJMkukW18vJyY+3E984eU0yHRTFx0+tlPEkIIIYTIw7785QzHLkeal+eMrEuJws4WjMj6KBQKGlXx5tcJjTk+rwODOvmZJ0ACUzHzpVuv0uCDLbzx3z3sOHEHg0EmlM9v9oVE8s8V0700rq0/NirLfRVt6udBw7KmUSmTtwTJBFtCWCFJdols1bxGUUb9J8C8vGhzCH/9c8NyAQkhhBBCvKSNh27x07pg8/LwbhVoW8fHghFZv1JFXPh6QA3OL32drwdUp+S/Eot7Tt/j7S/+oe77m1m4+QoPky1T4Fy8GK3ewOQtpnupcblCNPHztGg8CoWCT9v4o1IqCHmQyO8npd6wENZGkl0i241+uxKvVfM2L4/88TjBt6TbuRBCCCHyj2t3Ehg2+6h5uX4lT8b3qGLBiF4tBRxtGNTJn2Nz27NiQiMaVvHKsP5aeAKfzD1J5T7r+WzxaakVm8f9dvw2oZGJqJQKxrXxt3Q4AJTzdqZ7LVPyevauq8RJ4lQIqyLJLpHtVCol8z6uT2F3BwASU3T0+eYAiSk6C0cmhBBCCPF8yammzy4JSaYvv14F7VkwpgFqCw67elWpVEra1vFh/eTm7Pu+De+2LI2dzaN2iE/U8uPaIKoP2Ejvbw5w5GIERqMMccxrWlX04vWqRXivji9lvfLOMODhzcrg6qBGqVAQGiEJUyGsiUKj0chfg0yKj4/Hw8ODyMhIChQoYOlw8ryjlyLoOG4X+v/XVHijSQnmflQPRfrKo7kgOjoad3f3XD2myFnSptZH2tT6SJtan1epTUd8f5QVO0IBUCoVrJ3UlIaVvZ/zrPwnv7ZpZFwKS7deZfHmEO7HpDy2vmoZNwZ28qdLo+LY2VhuIoH8en5zksFgRKnM3e8Cz3PyZgx+3i642Kufu620qfWRNrVe8vOUyDF1KnryeZ9q5uXVe2/yy9ZrlgtICCGEEOI5ft0Zak50AXz6XmWrTHTlZx6u9oz6TwBnFnVi7sf1qFY24xfVs9di+GDmEar13cC3v10gIvbxhJiwjLyW6AKoUcItU4kuIUT+IskukaOGdPanfb1HhVzHzT/JmavRFoxICCGEEOLJLlyPYczPJ8zLrWoWZUS3ihaMSDyLrY2KN5uUZOeMVmyZ2oLODXxRpUumPIhNYcrK81Tps56hs45wPjTGgtG+eoxGIx/9eY4VR8PyzWyHt2OSuRAeb+kwhBDZQJJdIkcpFAp+HFGHUkVMY/M1OgN9vjlA7EONhSMTQgghhHgkPlFDn28OkKLRA+Dr5cRPH9XNkz1RREYKhYI6FT1ZPLYhpxZ0ZFi3Crg62ZjXa3QGftt1nSYjttJp3C42H76NPp8kX/KzvVci2XjuHl9sCuJkWKylw3mu9Wfv0vr7g4z66zxauT6EyPck2SVyXAEnW5aMbWguJhr2IJEPZh7BYJBycUIIIYSwPKPRyPDvjxF69yEAtmolS8Y2wM3FzsKRiRfl4+XE572rcX7p63z3fk3K+WSss3vwwgN6fr2fWoM28fO6IOIT5QfYnKDRGfjm72AAmvp7UKdU3q+JFFC0AAaDkWsRifx2/LalwxFCZJEku0SuqFzajWmDa5qXtx4L58e1ly0YkRBCCCGEyfyNV9h46JZ5eVL/6gSWK2TBiERWOdmr6dO2HIfmtOPPL5rQvHqRDOtv3k9kwqLTBPRZz9h5J7l2J8EygVqpX4/d4npUEjYqBePa+Fs6nEwp4+nEu3V8Afh+91VikiQRKkR+JskukWvebVma7s1LmZcnLTvHwfMPLBiREEIIIV51x4Mi+e/i0+blbo1L0LddWQtGJLKTUqmgWfUi/PFFEw7/1J4+bcviaPdohsbEZB0LNl2hzuBNvPPlP/xz9h5Go4w+yIqoRA0/7DFNStWjTnFKeThZOKLMG9a0DAUdbIhL1vHDbplYS4j8LM8lu3bs2MFnn31Gv379mD179jO3/eabb+jXrx8DBw40/xcT86jw5KpVq3j//feZMGEC4eHh5scfPHjAxIkT0WgkW5+bFAoF0wbXpGJJVwD0BiMDvj3I/ZhkC0cmhBBCiFdRZFwKfaceRKc3JTf8fAswY2gtFAqp02WN/HwL8N37tTi/9HU+71ONYh6O5nVGI2w7foeuE/bQaNjfLN92jeRUnQWjzb++332VhBQdbo42fNCktKXDeSGuDjaMaF4GgJXHbxNy/6GFIxJCvKw8l+xyc3OjU6dOvPbaa5na/q233mL+/Pnm/9zc3AAIDQ3l1KlTTJ8+ncaNG/PHH3+Yn7Ns2TK6d++Ora1tjrwG8XSO9mqWjG2Is4Npet/7MSkM/PZQvpmhRQghhBDWQa83MHj6Ye5EJgHgaKf6/2cUm+c8U+R3BZ1tGda1AqcWdmTJ2AbUqeCRYf3lm3F8+OMxqvRZz6RlZ7kTlWShSPOf4HsJrPp/vauRLcpSIB/eT2/X9KGclxN6g5GvtwZLTz8h8qk8l+yqWbMmNWrUwMXFJUv7iYiIoGTJkjg4OBAQEMCDB6bhcocPH8bV1ZWKFZ8/jbRWqyU5OTnDfyLryhYrwA8j6piXD5x/wJRfz1swIiGEEEK8aqb/cZE9p++Zl2cOrU354q4WjEjkNrVKSacGxdkyrSU7Z7TizSYlsVE/+noUnaBh5p+XCOy3gYHfHuLklSgLRps/KBRQzbcg5b2deauGj6XDeSlqlZJP25YH4NiNGG5IslOIfElt6QCyasOGDaxfv55ChQrRunVrGjZsCECxYsX466+/SExM5OLFi/j4+JCYmMjGjRv59NNPM7XvTZs2sW7dOvOyVqsFICYmBp1OujVnRcMKzvRqVZxftocBMPPPS1TwsadpNc9sP1Z8fHy271NYlrSp9ZE2tT7SptbHmtr0wIVIpv12wbzcvakvzaoUIDo62oJR5T5ratOsKlFIwdd9/PmwS0lW7g5j5e5bxCSYPvvr9Eb+2neTv/bdpFoZV3q3LkGrGt4ZEmNP8iqeXw8b+KlrGWKSdcTFxjz/CXlURXcFQxsWo7mfO67KVKKjU4FXs02tnbRp/uTu/vwZXhUajSZP9stcu3YtYWFhjBgx4qnbXL16laJFi2Jra8vly5eZM2cO/fv3p2ZN06x/O3bsYN++fbi7u9OzZ0/WrVtH+fLl8fDwYO3atSgUCrp06YKfn98T96/VajMkteLj4/H19SUyMpICBQo88Tki8zRaPR3H7eJEsOlXsoLOtuyZ1Zri3s7Zepzo6OhM3Qwi/5A2tT7SptZH2tT6WEubhkcm0XTEVqLiTV9eq5Z1Z8vUFtjbqp7zTOtjLW2aE1I0ev7ad5N564O5eCP2sfVFCjnQv70fPVuXwb2A3RP3IefX+kibWh9pU+uV54YxvoiyZcvi6OiIWq2mcuXKNG3alGPHjpnXt2zZkq+++oqRI0cSERFBVFQU9erVY+7cufTt25fevXszd+7cp47DtrGxwcHBIcN/IvvY2qhY/EkD3F1MtdNiH2roM+UgqVq9hSMTQgghhDXS6gz0m3rQnOhydbJhyScNXslEl3g2e1sV77YozT/ft2H9181oW6cY6ectuBuVzFfLzlKlz3o++vEYQTfjLBdsHrD29B32hURaOowc8zBVR3yy1tJhCCFeQL5Odv3b02bO0el0rFy5kl69epGQkIBer8fLywtvb290Oh0JCQm5HKlIU8zTiXmj6ps/PJy5Gs2Ehaef/SQhhBBCiJfwxdIzHA969IX8p4/qUaJw9vYoF9ZFoVDQsLI3KyY05vi8Dgzu5G+eaAkgWaPnl23XaDB0C90m7mHHiTsYDHly4EyOiXyYyhebL9Nv2Sn+PBlu6XCy3Zbz92g16wDf7gixdChCiBeQ55Jder0ejUaDXq/HYDCg0WieWB8rMTGRs2fPkpqaisFg4OLFi+zZs8c8hDG9jRs3UqtWLby9vXFxcUGn0xEWFkZYWBg6nQ5nZ/mQY0nNqhdh1H8CzMuLt4Tw1z83LBeQEEIIIazOhoO3+Hl9sHl5xBsVaFO7mAUjEvlNqSIuTB5QnfNLX+frAdUpVSTjd4i9Z+7x9hf/UPf9zSzcfIXElFejxu/MnVdJTNXj4WxLm0relg4n28WlaIl4qOGPE7cJiZBi9ULkF3muZtfatWszFIUHKF++POPGjeO7777D39+fjh07Eh8fz8yZM7lz5w4AHh4etG7dmsaNG2d47t27d5k7dy4TJ05ErTb9CnP06FFWrlwJwHvvvUetWrUyFVt8fDweHh5SsysH6PUG3vx8L/+cuQ+Ak72aHTNa4e+b9VmRZBy29ZE2tT7SptZH2tT65Oc2vXYngeYjt5GQZBqG1CDAizWTmqJW5bnffXNVfm7TvECvN7Dj5F3mrg9m/7n7j613cVTTs1VZ+ncol+01afOKS3fjef3nIxiN8PXrFXkzn87A+Cx6g5HXfzpM0P2H1PR1YeWAuk8dUSTyH3kftF55LtmVl0myK2dFxqXQZMRW7kYlA+DnW4Ad01vh7GCTpf3KG5j1kTa1PtKm1kfa1Prk1zZNTtXRetQOc5Fxr4L27JndhsLuUos1v7ZpXnTxegzzN17hz703SNUaMqxTKhW0q1OMwZ39qVvR02oSJUajkfcWn+DYjRgqFnFhzeC6qJTW8dr+7UhoND2WnADgp+7VaFnRy8IRiewi74PW69X+OUvkKR6u9iwa0wC1yvRH8sqteD7+6fhTJxAQQgghhHieT+adNCe6lEoFC8bUl0SXyHaVSrkxe3gdzi3pzKfvVcE73TVmMBjZdPg2HcbuotmH21i1+7pVTMi07dIDjt2IAWBCu/JWm+gCqFvanZYVTAmuKduCSdUZnvMMIYSlSbJL5Cl1Knryee9q5uXVe2+ydOtVywUkhBBCiHzr1x3X+HVHqHl5/HtVaFjZ+moKibzDw9Wej/9TiTMLOzJ9cGWqlc3YY+RcaAwfzDxCtb4bmPbbeR7EJFso0qxJ1eqZutVUA69tJW9qlXSzcEQ5b2wbP2xUCsKik/nl8E1LhyOEeA5Jdok8Z3BnfzrUezTe/9P5pzhzNdqCEQkhhBAiv7lwPYYxc0+al1vXKsrwbhUsGJF4ldjaqOhUryg7Z7Ti72kteL1h8Qw9nx7EpjB15QWq9t3ABzOPcO5a/vqseysmGSNgq1YyprWfpcPJFcXdHekeaEqW//RPKAkpWgtHJIR4FvXzNxEidykUCn4YUYeLN2K5fvchGp2BPt8cYM/sNhR0trV0eEIIIYTI4+ITNfT55gApGtNQMV8vJ+aMrIvSiodZibxJoVBQu4IntSt4Eh6RyMLNISzbdo3YhxoANDoDq3ZfZ9Xu69Sv5Mmgzv60rV0MVR6fPKGslzNbhzfg7O04fNxenWHBvesU4WacjkGNS+Fin7W6wkKInJW330XFK6uAky1LxjbE3lYFQNiDRN6fcRiDQep3CSGEEOLpjEYjw78/Rujdh4Cp58mSsQ1wc7GzcGTiVVfM04nPelfj3JLOTH+/Fn6+GSe8OnQxgl5fH6DmwE38tC6I+ESNhSLNHHsbFXVKvVqFvZ1sVSzsWf2VGLYpRH4nyS6RZ1Uu7ca0wTXMy9uO3+GHNZctGJEQQggh8rp5G4LZeOiWeXnygOoElitkwYiEyMjJXk3vtmU5NKcdq79oQosaRTKsD3uQyMRFpwnovZ5P5p3g2p0EywT6BCEPHhLy4KGlw8gz9AajTKYlRB4lyS6Rp73bsgzvtChlXp60/BwHzz+wYERCCCGEyKuOXY7gsyVnzMtvvFaCPm3LWi4gIZ5BoVDQtHoRfv+8CYd/ak/fduVwtFOZ1yem6Fi4KYTagzbR/Yt/2HvmnkUTK0ajkYnrL9FxzmGWSoF2jl6PpsvPR9h68b6lQxFCPIEku0SeN3VQTSqWdAVMUzcP+PYg9/PpzDVCCCGEyBmRcSn0nXoQnd6UDPDzLcD0D2qhUEidLpH3+fkW4NshNTm/9HW+6FMNH0/HDOu3n7hDt4l7aDj0b5Ztu0pyqi7XY9xy4T4nw2LRG4xULlrg+U+wcr8evcXlewlM23aFVK3e0uEIIf5Fkl0iz3O0V7NkbEOcHUzzKdyPSWHgt4fQ6Q0WjkwIIYQQeYFeb2Dw9MPcjTL9GOZop/r/ZwcpIC3yl4LOtgztWoGTCzqyZGxD6lb0zLA+KCyOkT8ep0qf9Xy17CzhkUm5EleKVs+0bVcAaF+5MDVKSM2qMa39sFUruR2bwuJD0tNNiLxGkl0iXyhbrAA/jKhjXj5w/gHf/HreghEJIYQQIq+Y/sdF9py+Z16eObQ25Yu7WjAiIbJGrVLSqYEvm6e2YNfM1rzVtCQ26kdf3aITNMz68xLV+29gwLcHOREcmaPxLDxwgztxKdjbKBnTqlyOHiu/8HFzoF+DEgDM3XedBwmpFo5ICJGeJLtEvtGpQXEGd/I3L8/68xLbjoVbMCIhhBBCWNqeU3eZ9tsF83KftmV5o0lJywUkRDarVtadnz+qx5lFnRj9dgAero9mFtXpjazZF0brUTtoNWo7a/bdRKvL3tEP9+JTmL//OgD9G5SkaEGHbN1/fjaoUSm8XOxI0uiZviPE0uEIIdKRZJfIVz7rXZVa5T3My+/PPELYfZkRRgghhHgVhUckMvC7Q6TV7K5W1p3JA6pbNighckhhdwfGvluZs4s788OIOgSUKphh/cngKAZ8e4jA/huY+edFouOzp6fRd9tDSNYa8C5gx4BGJbNln9bCyU7Nxy1Nk2CsOX2H8+FxFo5ICJFGkl0iX7G1UbFoTH3cXWwBiH2ooc+Ug1IUUgghhHjFaHUG+k07RHSCBjDVOloytgF2NqrnPFOI/M3eVsU7LUqzd3YbNnzdnHZ1fUg/D8PdqGQmLTtH5T7rGfnjMYJuvnwCJipRw66gCABGt/LD0Vad1fCtzutVi1KlmKlg/+QtwRadMVMI8Ygku0S+U8zTiXmj6pv/qJ+5Gs2EhactG5QQQgghctUXS89wPOhRnaKfRtaluLezBSMSIncpFAoaVPZi+fhGnJjfkSGd/c0TOgGkaPQs23aNBkO30HXibrYfD8dgeLFETCEnW7aPaMDI5mXpVKVwdr8Eq6BUKhjfrjzeLna8VbMYkusSIm+QZJfIl5pVL8LotwPMy4u3hLB67w3LBSSEEEKIXLPh4C1+Xh9sXh7xRgVa1y5mwYiEsKyShZ2Z1L86F5a+zjcDa1CqSMbE7z9n7tP9y33UHbKZBZuu8DBZm+l9e7rY8X6T0ijSdx8TGVQvXpBdHzWia2AxlEo5T0LkBZLsEvnWqP9Uokm1R78wfTTnOEFhMk5eCCGEsGbX7iQwbPYR83KDAC8+fa+KBSMSIu9wcbRhYEc/js3twMqJjWlc1TvD+mt3Ehg77ySV+6xn4qJT3Lz35Nq3yRo90Yma3AjZatip5au1EHmJ3JEi31KplMwbVY8ihUwzwiSm6Ogz5cAL/VIlhBBCiPwjKUVHn28O8DBZB4C3mz0LxtRHrZKPtEKkp1QqaF27GGsnNWP/D23p0aoM9raP6tnFJ2r5aV0wNQdtoufX+zl04UGGWlPz91+n5awD/Hb8liXCz9fCopP48I9z3I1LsXQoQrzS5JOByNc8XO1Z/EkD1CpTd+Ert+L5+KfjUhhSCCGEsEKfzDvBxRuxgOnL/ILRDfB2c7BsUELkcRVLFmTWsNqcW9yZ8T2qUNj90T1jMBjZfPg2HcftoumHW/ltVyg3Ih6y4MAN4lN0RD2U3l0vwmAw0veXU2w+f4/pO0IsHY4QrzRJdol8r3YFTz7vXc28vHrvTZZuvWq5gIQQQgiR7X7dcY2VO6+bl8e/V4UGlb0sGJEQ+UshVzs+eqsSpxd2ZN7H9Qgs555h/fnQWIbOOkqD97cQc+shHg429G9Y0jLB5lNKpYLhzcoAsP7sXU7firVsQEK8wiTZJazC4M7+dKjnY17+dP4pTodEWTAiIYQQQmSXC9djGDP3pHm5da2iDO9WwYIRCZF/2dqoeKNJSXZMb8XWb1vSpVFxVOmKqqek6Em5n0zosft89OMxzl6NtmC0+U/HKoWp5usKwOQtwS88A6YQIntIsktYBYVCwQ8j6lD6/zPPaHQG+kw5SExCqoUjE0IIIURWxCdq6PPNAVI0egCKezkxZ2RdmfFMiCxSKBTUKu/BwjENOL2wI8O7VUBt8+jroU5v5PfdN2g2chsdx+5k46Fb6PUGC0acPygUCia0Kw/A2dtxbDx318IRCfFqkmSXsBoFnGxZMq6hufjmrQeJfDDziPyaIoQQQuRTRqORYbOPEnrXNGOcrVrJ4rENcHOxs3BkQliXYp5OVK7qiUuFgjj6OFGisHOG9YcuRtD7mwPUHLiJOWsvEye1vJ6pqo8rr1ctAsC3O0JI0ugsHJEQrx5JdgmrElDKjWmDa5iXtx2/w4It15/xDCGEEELkVXPXB7Pp8G3z8tcDqxNYrpAFIxLCOmn1BmbuvIpCqeCdlqU5Ob8Dq79sQsuaRTNsF/Ygkf8uPkPlPusZM/cEV8PjLRRx3vdxq3I42Ci5H5/K/P03LB2OEK8cSXYJq/NuyzK806KUeXnG6hAOnL9vwYiEEEII8aKOXY7g86VnzMtvvFaC3m3KWi4gIayYjUrJ3HcDec3Pg49blkOhUNA0sAirPnuNIz+3p1/7cjjZq83bJ6boWLQ5hDqDN/P2F/+w5/RdmQ39XwoXsGdQ49LYqZXYq+VrtxC5TaHRaORdKZPi4+Px8PAgMjKSAgUKWDoc8QxJKTrajN5hnp7cq6A9e2a3yTDVssi/oqOjcXd3f/6GIt+QNrU+0qbWJzfbNDIuhSYjtnI3KhkAP98C7JjeCmcHm1w5/qtC7tOcZW3nN+6hhhU7rrFgUwi3HiQ+tt7ftwCDOvnzZpOSOKZLjFmTF23TFK2e6EQNRQvKd5C8ytruU/GIpJiFVXK0V7NkXEOcHUx/aB/EpjDw20PopKimEEIIkafp9QYGfXfYnOhyslezdFxDSXQJkUP0maxv6+psywddKnBifgeWjG1IvUqeGdYH34rnoznHqdJ3PV/+cobwyKScCDdfsbdRSaJLCAuRZJewWmWKuvDDiLrm5YMXHvD1inMWjEgIIYQQz/Pd7xfZe+aeeXnGB7Xw93W1YERCWK+j16Np8/1BdgU9yPQwRLVKSacGvmya0oLdM1vzn2YlsUk3TC8mQcPs1ZcJ7LeB/tMOcjwoMqfCz1cSUrQsPnhDJs8SIpdIsktYtU4NfOndqoR5efbqy2w7Fm7BiIQQQgjxNHtO3eXbVRfMy33bleONJiUtF5AQVkxvMPL138HciEpi0cGbL7WPqmXd+WlkPc4u7sSY7gF4Fnw0U6reYGTt/jDajN5Bq1Hb+eufG2h1r+Yoi4QULa1mH+SbrVdYd/aOpcMR4pXwUsmuBw8ecP78eU6cOEFISAgajUw9K/Ku0f/xo1Z5D/PykBmHuXnvoQUjEkIIIcS/hUckMvC7Q6R1LqlW1p1J/QMtG5QQVmzN6XAu3U1AoYDxbf1RKBQvvS9vNwc+eacyZxZ15ocRdahcumCG9SeDoxj43WEC+29gxh8XiYpLzWL0+YuLvQ2v+Zm+j0zfcZXEVJ2FIxLC+mW6QP29e/fYtGkTe/fuJTIyMkM3V7VaTUBAAO3bt6dhw4YoldbZYUwK1OdP0dHRJOvtaPrhNqLiTX9Yq5Z15+9pLbCzUVk4OvEypJCk9ZE2tT7SptYnJ9tUo9XT6dPd5uFOBZ1t2TOrNcW9nXPkeMJE7tOclZfPb0KKjpazDhCVqOGtGsWY/HqlbN2/0Wjk8MUI5m4IZsuR2/x7hKS9rYo3m5RkUCc/KpQomK3HzklZadMHCam0mnWARI2eIY1L8VHLctkcnXgZefk+FVmTqazUnDlzGDx4MPfu3aN3794sWLCAtWvXsmXLFlatWsWkSZMICAjgl19+YdCgQQQHB+d03EK8kGKeTsz7uB5pP1idvRrN+AWnLBuUEEIIIQD4YunZDHV9fhpZVxJdQuSgn/8JJSpRg5OdipEtymb7/hUKBfUDvFj2aSNOzu/I+6/74+L4aJKJFI2e5duv0XDo33SduJttx8KtvpaVl4sdgxuXAmDRoZvcipEC/kLkpEzNCWtvb8+yZcue2JvJzc0NNzc3AgMD6dGjB8ePHyciIgJ/f/9sD1aIrGhavQhjugcwdaWpFsiSv69Sp6Inb0otECGEEMJiNhwMY+6GRz+UfvhmRVrXLmbBiISwbjejk1h62FSj64MmpfFwtnvOM7KmRGFnvupXnTHdK/Pbruss2BhM6N1HJUX+OXOff87cp0xRF/p38KN781IZEmPWpE/9Evx+MpzbMclM2xbCD29XtXRIQlitTPXs6tevX6aH7dWqVYuGDRtmKSghcsrHb1WiSbXC5uWPfjxGUFicBSMSQgghXl1Xw+MZNvuoeblhZS/GvVvZghEJYf2mbr2CVm+kuLsDPeuWeP4TsomLow0DO/pxdG4HVk5szGvVvDOsv3YngXHzT1K5z3omLDxllTV27WxUfNLaD4CtF+9z9Hq0hSMSwnq9cHGt1NRUUlJSzMv3799nzZo1nDhxIlsDEyInqFRK5o2qR5FCDgAkperpM+UAD5O1Fo5MCCGEeLUkpejoO+UgD5NNhZq93eyZP7o+apV11n4VIq/oUq0ovm4OjGvjj5069+83pVJB69rFWPNVMw782Jaerctgb/uojm5Ckpaf1wdTc9Amen69n4PnH2SoF53fta7oRe2SbgCS7BIiB2W6QH2asWPH0rBhQzp06MDDhw/p168fKpWK+Ph4Bg0aRMeOHbMU0I4dOzhw4AC3b9+mSpUqjBgx4qnbJicns3TpUs6cOYOtrS0tWrSgc+fO5vWrVq1i3759uLu7M2TIEIoVM3WJf/DgAT/88AMTJ07E1tY207FJgfr86UlFB49djqDjuF3o9KbLv1vjEswbVS9Ls9CI3COFJK2PtKn1kTa1PtndpsNmH2HlzuuA6cvvuknNaFDZK9v2L55P7tOclZfPr0ZnwEalyDOffaPiUlm2/SoLN4VwLzr5sfWVSxdkYEd/ujYukSExltuyq01D7j/kYaqOwOIFsx6UyJK8fJ+KrHnhVP7Vq1cJCAgAYP/+/bi5ubFixQrGjBnDunXrshyQm5sbnTp14rXXXnvutsuXLycxMZEZM2Ywfvx49u7dy4EDBwAIDQ3l1KlTTJ8+ncaNG/PHH3+Yn7ds2TK6d+/+QokuYV1qV/Dkiz7VzMt/7bvJkr+vWi4gIYQQ4hXy645r5kQXwIQeVSTRJUQuslUr80yiC6CQqx0j36zEmUWdmD+qHtX9CmVYfz40lmGzj1Kt33r6TT1Avfc3U7Tr7zQe9jcbD92yUNQvr5y3syS6hMhhLzWM0dHREYCTJ0/SoEEDlEol5cuX58GDB1kOqGbNmtSoUQMXF5fnxnH06FG6deuGk5MThQsXpmXLluzbtw+AiIgISpYsiYODAwEBAebYDh8+jKurKxUrVsxyrCJ/G9TJn471fc3L4xec4tSVKAtGJIQQQli/86ExjJl70rzcpnYxhnWtYMGIhLB+8clahq86S/C9BEuH8kw2aiXdXivJjumt2PptS7o2Lo5K+SgpFxGbyroDt7hyK55UrYFLN2Lp/c2BfJnwSqPTGzh9K9bSYQhhdV442VW0aFEOHjzIgwcPOHHiBDVq1AAgNjbWnATLDffu3UOn01G8eHHzY8WLF+fWLdMbXbFixbhx4waJiYlcvHgRHx8fEhMT2bhxI927d8/UMbRaLcnJyRn+E9ZDoVDw/Yg6lClqSqxqdAb6Tj1ITEKqhSMTeU7cbbh79tF/cbctHZEQQuRL8Yka+kw5QIpGD0BxLyfmjKyLUpl3epgIYY1++ieUvy/ep8eSE6Ro9ZYOJ1NqlfdgwegGnF7UiQ/frIiby+OjctLq8UxcdBqd3pC7AWaDWzFJdP7pCO8uOs7NqCRLhyOEVVG/6BPeffddpkyZwrx586hWrZq5h9TJkycpU6ZMtgf4NCkpKdjZ2aFSPRqz7ejoaC6e7+PjQ8uWLZkyZQru7u707NmTVatW0b59e8LDw1m7di0KhYIuXbrg5+f3xGNs2rQpw9BMrdZUxDwmJgadTpdzL05kq/j4+GeunzWkMm98eYRUrYFbDxIZMG0/c0cEygfvPOx5bZqdlAl38PitFQq9xvyYUWVLZPftGFyK5loc1i4321TkDmlT65PVNjUajQz98QzX75pmWLNRK5j9fmUMmodES41mi5D7NGfllfMbFpPCL4fDAOhVy5ukhDjyU1rFQQkfdPClb8uiVB+8C53h8ZLTtx4kUr3/et5rXpy3XvOhgJNNjsSS3W1qozOQlKpFqzfy1cYLTOtUNlv3L54vr9yn4sVkps7aCye7GjduTEBAANHR0ZQuXdr8eGBgIA0bNnzR3b00e3t7NBoNer3enPBKTk7G3t7evE3Lli1p2bIlAEFBQURFRVGvXj0+/vhjxo0bh9FoZOrUqUyfPv2JY9Y7dOhAmzZtzMvx8fGsXr0aNzc3KVCfzzzrZqjv7s637+sZ/v+pz/eciWDFnvt8+KYMdc3LcquQpOLGhgyJLgCFXkNBWz1IMctsJcVBrY+0qfXJSpv+vC6I7Scelbz4ZmANGtcolR1hiSyQ+zRn5YXzO27LaXQGI6UKOTKgSXlsLTADY3bxL+7KpZuxPGlyxvDIFKb+foUf14fyn2alGNjRj3I+2f+dLbvbdHz7CgxZeYZ/rsUSHGukXulCz3+SyFZ54T4V2e+F3+mmT5+Ovb09ZcuWRal89PQSJUpkKAKf0woXLoxKpSIsLMz82M2bN/Hx8XlsW51Ox8qVK+nVqxcJCQno9Xq8vLzw9vZGp9ORkPDkses2NjY4ODhk+E9Yp3dblObdlo+St5NXnOPA+fsWjEhYmuLmQVTLO6H+e9SzNzQaITEyd4ISQoh86uilCD5fesa8/EaTEvRuIz0YhMhpB65GsTs4AoBxbf3zdaILYHT3AIxGSOunkPZ/O5tHrysxRcfiLSHUHbKZt7/4hz2n7mJ8UnYsj2he3pP6pU3JlslbgvPlcEwh8qIXfrfbsWMHqamP1zTSaDTs2LEjywHp9Xpzjy2DwYBGo3nikEE7Oztq167NmjVrSEpK4t69e+zcufOJszhu3LiRWrVq4e3tjYuLCzqdjrCwMMLCwtDpdDg7O2c5bpH/TR1Ug0olCwJgMBgZMO3QE6c+FtZPeeRH1Cs6oww79PyNIy6jnl0R1fKOKI/Nk3peQgjxLxGxKfSbdhCd3vRl09+3ANPfr5WnZoITwhrp9Aa+/jsIgIZlC9HEz8PCEWVdx/q+LB3XkIolC2Jno6RiyYL88mlDLi/rwlf9AvH1csqw/Y4Td3jjs700+GALS/++SlJK3itFo1Ao+LStP0oFBN9/yB8nwy0dkhBWIdPJrsTERBITEzEajSQnJ5uXExMTSUhI4NixYxQsWDDLAW3YsIEBAwawceNGzpw5w4ABA/j2228B+O6779i4caN52549e+Lg4MDIkSOZNGkSjRs3fmwo5d27dzlz5gxt27Y1vWClkl69ejF9+nSmT59Onz59MvRQE68uBzs1S8Y1xMXRNMb/QWwKA6YdlF9XXhXpfvEzlO+EUanGULIRuq6LMKrsMm6qsgNHUxdz5ZWtKIwGlGGHUe0Yj82P1VAtboHy4CyICsnNVyCEEHmOXm9g0PRD3I0y/XjkZG/6W+vskDP1dIQQj6w6cZuQB4molKZkirUkmDvW92Xf9225s+Y/7Pu+LR3q+eLqbMv7r5fnxPwOLB3XkPqVPDM8J/hWPB//dJwqfdfz5S9nCI/MW1XL/Au78HYt0wilWbuuEp+stXBEQuR/Co1Gk6k+na1bt37uG2TPnj155513siWwvCg+Ph4PDw8iIyOlZlc+Eh0d/ULjsDceukXvbw6Yl0e8UYH/9qqWA5GJl/WibfositvHUO6birFcWwy1+qc7SCi4/39oa9xtSIp6tM6xELj+f8i0Xosi7DCK4E0ogzejeJhx+KuhfEf03ZZkS6zWLDvbVOQN0qbW52XadOrK80z77YJ5ecHo+nRtXCK7QxMvSe7TnGXp87vq+G2mbrtCl2pF+G+HChaLw1LOXYtm3oYrrNl3E40u44/XKqWCTg18GdTJn1rlM9/jLSfbNDpRQ8tZB4hP0fFpW3/61Jf3ytxg6ftU5JxMJ7vOnTuH0WhkzJgxTJw4MUOyR61W4+3tTaFC1l1MT5Jd+dPLvIFNWHiKn9cHm5d/ndiYNrWLZXdo4iVlxx8lRfgJlPumogzdA4DRuTC6D06C2u45z3wGowHFnVMogjahDNqEIvYG+kZjMDQeY95EeWwuxiLVMPrUBoX0Kk0jHzSsj7Sp9XnRNt196i5vfb7X3HG2b7tyfDukZg5FJ16G3Kc5Ky+c36iHqahVSlxf4d6UD2KSWbr1Kou3hBAR+3g5nup+hRjU0Y9ODXyxtVE9c1853aZrToejVirpWKWw1fTEy+vywn0qckamk11p7t+/j6en5ys59E+SXfnTy7yBaXUGOn26i2OXTYXHXZ1s2DOrDSUKS323vCArf5QU4af+n+TaZX7M4FsXQ+MxGEs0elTpNKuMRnhwCRzcoUAR02MJd7H5vrJptZMXBr+2GMt3wFiiIahe3Q+hIB80rJG0qfV5kTYNj0ikyYitRCeYZrOtVtadLdNaYPecL5Iid8l9mrPk/OYtqVo9a/bdZN6GYM6Hxj62vrC7A/3al6NX67IUcn3yj5/SptZH2tR6vXCyC+Dhw4cEBQURGxv72MwWLVu2zLbg8hpJduVPL/sGFh6ZRNMRW4mKN/0CVLWMG1umtcTeVj6oW9pLtWlqAqq1A1Be22l+yOBTG0PjTzCWbJx9Sa5nuXcO1d+jUd45meFho70rxnKtMfh3wFiuNShfvWtMPmhYH2lT65PZNtVo9XQct4sTwabh3wWdbdkzqzXFveUHo7xG7tOcZanz+8vhm3SoXJhCzlnorW7FjEYjhy9GMG9DMFuOhmMwZPw+a2+r4s0mJRnUyY8KJQpmWJfbbRqfrKXAK9wrLzfI+6D1Ur/oEw4fPszUqVNJTk7G0dExQ/dKhUJh1cku8Wop5uHIvFH1ePMz0xCMs9diGL/wFNPfr2Xp0MTLsHWGlDgADMVqmpJcpZrkTpIrTeEq6PtsQx9/B2XwFhTBm1CEHUKREofi/B8orv+Dzq/No+3Tz60thBD5xOdLz5gTXQA/f1RXEl1C5JJ/rkQwaUsws3ZdY8uw+hRxtbd0SHmOQqGgfoAX9QO8uHnvIQs3X2H59lASkkxF4VM0epZvv8by7ddoXNWbQZ38aVWzKEpl7n0muxWTxFebg7gVnczGD+qhVr16o6qEyKoXTnbNnz+f1q1b06dPH+zt5c1TWLemgUUY0z2AqStNxXWX/n2VuhU9ebNJScsGJp7v3jmUQZsxNBlnWlYoMLT8CkNKHMbSzSybRCpQ1FQMv1Z/SIpCcWUryuBNGN1LZ6jjpVrbHzSJGPzbY/RrA06ez9ipEEJY3oaDYczbcMW8PPLNirSqJTUvhcgNWr2Br/823X81ShSURFcmlCjszFf9qjOme2VW7b7Ogo1XuHYnwbx+39n77Dt7n9JFnBnQ0Z821QuSG32AUrQG9oVEoTcYWXXiNu/VKZ4LRxXCurzwMMaOHTsyf/58ihQpklMx5VkyjDF/ymrXVL3ewH+++Ic9p+8B4GinYseM1pQv7ppdIYoX9Mw2vXce1f5vUV7ZAoDu3bUYSzbKxeiyiSYR9Ux/FLoUAIwKJUbfOhj9O2Dwb/9oNkgrIV3IrY+0qfV5XpteDY+n+chtPEzWAdCwshd/fdVUeiTkYXKf5qzcPr9LD99k8pZg1EoFm4fWp7SnU64d21oYDEZ2nrzDvA1X2Hvm3mPrnR3U9GhVhv4d/CiZw7V8v9x0meVHb1HQwYYdHzakoKMMZ8wJ8j5ovV7400fNmjW5cuXK8zcUwkqoVErmflyPoh6OACSl6un9zQEeJmstHJnI4P5FVKt7YbOoqTnRZShcFVS2Fg7sJals0HdZiKFKd4z2BVEYDSjDDqPaMR6bH6uhWtwCokMtHaUQQgCQlKKjz5QD5kSXt5s980fXl0SXELkkOlHDD7uvAfBeHV9JdL0kpVJBq1rF+Ourphz4sS09W5fJUK/3YbKOn9cHU3PgRnpM3s/B8w8eq2GdXYY1K4Org5rYZC0/7LmWI8cQwpq98DDG2rVrs2DBAm7evEmpUqVQqzPuol69etkWnBB5hYerPYs/aUCHsTvR6Y2E3I5n5I/HmD+qvkwLbGkPLqPaPw1l0EbzQ0bvyugbf2Iq9p5f20dli9GvDXq/NqDXogg7jCJ4E8rgzSge3kcReQVc0vWwjQoBbTJ4V86/r1kIkS8ZjUZG/3yCSzdMdRFVSgULxzTA283BwpEJ8er4fvc14lN0uDnaMLRpGUuHYxUqlCjIzKG1mdCjKsu2X2XR5hDuRiUDprKqW47cZsuR2wSUKsjATv50a1wiWyeycnO0ZXizsny1OYhfj92iey0fynpJ/UMhMuuFhzG2bt366TtTKNi6dWuWg8qrZBhj/pSdXVPnbQjm0wWnzMvTBtekX/ty2bJvkXnp21R59GdUOycC/09yNRqN0a+t9SZ8jAYU4SchOhRjlf+YH1Zu+QjV6WUYXYtjKN8eo38HjD61MtQAy8ukC7n1kTa1Pk9r0xXbrzHih2Pm5c96V2V4t4q5GZp4SXKf5qzcOr9X7ifQcc5hDEb4vGMF3q3tm+PHfBVpdQZ+236ZX3eHZ5iEI42Hqx192pajT7uy2Zbs1+oNdJxzmGsRibxWzoOFPatny37FI/I+aL1eONn1KpNkV/6UnW9gRqORPlMOsvHQLQBs1Uo2T21Bdb9C2bJ/kQkRwcSmGCnoW960rE1GtbonhsDeGP3bWW+S6zlUv3ZFeWNfhseMTl4Y/NuZEl8lGoAq79Z6kA8a1kfa1Po8qU3Ph8bQZvQOUjR6ANrUKcbyTxvl6qxl4uXJfZqzcuv8/no0jC83B1HW05n179eV4cM5KK1NjwdFMn9jMBsO3kKnz/h12katpEuj4gzq5E+1sllv//0hkfRdZvqxfXHP6jQq55HlfYpH5H3Qekmy6wVIsit/yu43sPgkLS1GbjPP1OLr5cSeWa1xc7HLtmOIJ4i8gurAdyguriW5UndsXv/e0hHlPdHXTMMcgzajvHMywypdu5kYA3tYKLDnkw8a1kfa1Pr8u03jEzU0G7mN63cfAlDcy4k9s9tQ0Dmf1kp8Bcl9mrNy8/wG30sgRWegqo9MoJST/t2m4ZFJLN4Swi9brxKToHls+7oVPRnUyZ92dYtlKQk54vezlPVypn+Dkjhk41BJIe+D1ixTya61a9fSvn17bG1tWbt27TO37dKlS7YFl9dIsit/yok3sIvXY2g16tEv2a1qFuXXiY3ll+ycEBWCav93KC6uQYHp7UrrUQkG7ALlC5cdfHXE30EZvAVF8CYUt46iG34OnDxN6+5fQHVghmm4Y9lWYOdi2ViRDxrWSNrU+qRvU6PRSK9vDrD58G3A1NP5729bZksvBpF75D7NWXJ+rc/T2jQpRcfqf24wd30wwbfiH1vv4+nIgA5+vNeqzEv9IGA0GqVOcA6R+9R6ZSrZ1aNHD+bMmUOBAgXo0ePpPQMUCgXLli3L1gDzEkl25U859Qa2cmcow2YfNS9P6FmFkW9WyvbjvLKirqI6MB3Fxb9QGA0AGD380TcaTZR3Q9wLSRfuTEuJB/tH71nKvd+gOjgdAKPKFmPJxhj825tqnTlZ5rzKBw3rI21qfdK36U/rgpi46LR53fT3a9G7bVlLhSZektynOSunz++523H4F3bBTi3DFnPL89rUaDTyz9n7zFsfzPYTdx5b72in4u3mpRnY0Y9yPi//fVKSX9lH3getlwxjfAGS7MqfcvINbMT3R1mxIxQwTVW85qumNKrinSPHeqUkRaP+vjIKfSoAxkLlTIXnK3QGpUr+KGWR4tpulGd/RXF1Jwptovlxo0KJ0bcuhhp9MVZ8PVdjkja1PtKm1ietTY9eiqDjuF3oDaaPkG82KcnPH9WVL175kNynOSsnz2/Uw1RazDqIm6MNc7pXo0IRy/fSfhW8SJteDY9nwaYr/LbzOokpusfWt6hRhMGd/GkSWDjT759xyVp+2HMNgAntymc+cPFU8j5ovbL0M4DRaMRolFyZeHVNGVSDgFIFATAYjAz89hD3opMtG1R+pUt59G9Hd4wBb2AsVBZd57noBh7AWKkrKKVGQXYwlmmGvusidCOD0L25HEOVtzHaF0RhNKAMO4QiOjTjE+JuWSZQIUSeExGbQr9pB82JrvLFXZn+QS1JdAmRy2buusbDVB2JGh0+bvaWDkc8QdliBZg6qCbnl3Tmq36BFPdyyrB+58m7vPHZXup/sIWlf18l6QkJsX/bevE+vxwOY/mRMK7cT8ip0IWwCi/Vs2vHjh38+eefhIeHA+Dj48Obb75JixYtsj3AvER6duVPOZ2tv3YngeYjt5GQpAWgfiVP1k5uJjPhZFbsTVQHZqC4sR/d4EOg/v8HttQEsHF8YoJLfoHJAXotirDDKII3Yag1AAqVMz0eH47ND1UxepbH4N8eg38H8A7I9lkvpU2tj7Sp9YmIjGLQ7LP8c+Y+AE72anbMaIW/rxTEzq/kPs1ZOXV+L99N4PWfD2MwwqTOFflPTZ9sP4Z4sqy0qV5v4O9j4czfcIWDFx48tr6gsy09W5ehf/tyFPN0esIeQKc38PrPRwi+/5D6ZdxZ2quG/NiQRfI+aL1euLrz6tWr+eWXX+jcuTOVKpnqE124cIHZs2cTFxdHt27dsj1IIfKyMkVd+GFEHXp/cwCAQxcjmLz8HJ/1rmbZwPK62DBUB2egOLcKhcH0S5biwmqM1d4zrc8DRdNfKSobjKUaYyzVOMPDimu7Tf+PCEIVEYTqwHSMBUtg8G+H0b8DRp9aoJDErhCvgh/XXTMnugBmDastiS4hcpnRaGTy30EYjFC+sAtvVC9m6ZBEJqlUSjrU86VDPV/OXYtm3oYrrNl3E43OVJs29qGG7/+6zJy1QXSs78vgzv7U9C+UIZmlVin5tK0/vZae5NC1aHYHRdC8gpelXpIQedoL9+zq0aMHPXv2pGXLlhke3759O8uXL2f58uXZGmBeIj278qfcytZPXHSKn9YFm5dXTGhE2zryS9tj4m6hPDgT5dmV5iSXsWBJ9A0/xlj5zUzNsCi/wOQioxHuX0AZvMk0u2PE5QyrDUUC0ffdkeXDSJtaH2lT67Lr5B3+88U/pFWv6Ne+HNMG17RsUCLL5D7NWTlxfrdfus8Hv50FYEXfmtQpJe2Xm7K7TR/EJLN061WWbLnKg9iUx9YHlnNncCd/OjXwxdbm0WiHIb+eZmdQBCXcHdk8rL5MUpAF8j5ovV74roiOjqZixYqPPV6pUiWio6OzJSgh8qP/9qpG7QqPZrL7YOYRbt57aMGI8h7l0Z9R/1Qb1ellKAw6jAVLoGs/G93gwxirds9UokvkMoUCClfG8No4dAP3ox18BH3T/2IoWh3A1LMrHeXRn1EEbwGt1K4TwlqERyQyePphc6IrsJw7X/ULtGxQQryCUnUGpmy9AkCril6S6LICXm4OjOlemTOLOzFnZF2qlHbLsP50SDSDph+mWv+NTP/9IpFxpoTY2Db+2KgU3IxOYvmRMEuELkSe98LJrqJFi7Jv377HHt+7dy/Fikk3WvHqslErWTimAYUK2AEQl6ilz5QDpGj0Fo4s7zB6+KEwaDG6+qJrNxPd4CMYq70LKhtLhyYyq1BZDPWHo++zHe2wsxjqfvBoXWoCyj1foV7dE/XM8qj+6ovi4l+m+mtCiHxJo9XTd+pBohM0gKmmzJKxDbGzkQlDhMht2y/d51ZMMjYqBZ+09rN0OCIb2dmoeLtZKXbPas2mKc3pUM8HpfLR8MX70cl8veIcVfqsZ8T3R0lM0NCrXgkA5uwNJephqqVCFyLPeuFuFD179mTy5MmcP3/e3MPr0qVLnD59mvHjx2d7gELkJ8U8HJk3qh5vfrYXoxHOXoth/MJTTH+/1vOfbG3i76I8NAtjxc4Yi9cHwFi6GbquizH6tQGVrYUDFFlW4F8/cCRHYyzZGG7sQ6FNRBG0AWXQBowqW4wlG2Mo3wFjpW5g42CZeIUQL+zzpWc4ERxlXv75o7r4ej25cLIQImd1qFwYZzs1t2KSKe7uaOlwRA5QKBTUq+RFvUpehN1/yMJNISzfcY34RNNEWKlaAyt2hLJiRyj1Azyx1xtoUc3TwlELkTe91GyMV65cYc2aNYSFmbpMFi9enDfeeIOyZctme4B5idTsyp8sMQ77298uMGXlefPyzx/V5a2mpXI1BotJuIvy0PcoTy9DoU/FULw++h4bsvUQMrY+j0uJR3Fth6nG19WdKLSJABhtndGNDHo046ZBb55tU9rU+kib5n/rD4TRd+pB8/KQjqWZNLCOBSMS2U3u05wl59f6WKJNHyZrWbX7OvM3XOHancd7y5cq4syADn6806I0Lo4yWuJFyX1qvV4q2fWqkmRX/mSJNzCDwch/vtjL7lP3AHC0U7FjemvKl7DiWasS7qE8/APK07+g0JnqCRhdimJo8CGG6r2zdcY++aOUj2iTUVzfizJ4M0a1PYa235lXqTaNgIjLGP3bE1O4Ia6la1gwUJHd5D7N30Jux9P8o20kJpsmEmlYxYsFH1bFy9PjOc8U+Yncpzkru85vfLIWF3t1hln5hGVY8p4xGIzsOnWXueuD2Xvm3mPrXRxteLdFaQZ09KNkYWcLRJg/yfug9XqpZJder+fgwYPmnl0lSpSgfv36qFTWXb9Bkl35k6XewKLiUmny4VbuRCYBUM6nADtntMLZwcp+cUmJR7l/GspTSx8luZwLY2gwEkO190Btl+2HlD9KVsCgQz2rIorkRxObGD3LY/Bvj8G/A3gHmIrji3xL7tP8KylFR6tR27l8Mw4Ab3cH9sxqjY0xWdrUysh9mrOy4/wajUbeWXQcpULBZx3K4+ftkk3RiZeRV+6ZoJtxzN8YzB97bpD8r/rACgW0qV2MwZ39aRDgJUnS58grbSqy3wsnu27cuMFnn31GdHQ0vr6+ANy+fRtXV1e+/PJLSpWy3qFakuzKnyz5BnY8KJIOY3ei05tus66NizN/VH3r+qOjSUT9U00UiREYnb0x1P8QQ2CPR0PVcoD8UbICRgOK8BMogjahDNqEIi7jTELGgiXQd1mA8f+zPor8R+7T/MloNDJ01lFW7b4OgEqpYN3kZtQP8JI2tULSpjkrO87v3xfuMfz3cwCs7FeLWiXdnvMMkZPy2j0THZ/Ksm3XmPnXJR7+v65XepVKFmRQZ3+6NS6Bva11d0x5WXmtTUX2eeFk14gRI3B1dWX06NG4uJh+WUhISOC7774jLi6OWbNm5USceYIku/InS7+BzdsQzKcLTpmXpw2uSb/25SwWT5YlRqC4fRyjfzvzQ4ozv6LQJmII7JmjSa40lm5Tkc2MRuJDDlLw7n6UwZtRRARhVKjQfXgJHAuZtom5jiI2zDTZgczemS/IfZo/rdh+jRE/HDMvf9a7KsO7mSYkkja1PtKmOSur5zdFq6fN9wcJj02hXYA3s/9TNRujEy8jr94zoREPaTFpLw/vJaNP0j223sPVjt5tytKnXTkKu8tEQenl1TYVWffCRXSuXbtG3759zYkuABcXF3r37s3Vq1ezNTghrMHAjn50auBrXh6/8BQnr0Q94xl5VGIkyt1foJ5TA9Xa/hAfbl5lrPYuhloDcyXRJayQQoHOoyKG18ahG3gA7eDD6Dv+8CjRBShPL0e9shvqWRVQbRyK4srfoE22YNBCWJ/zoTGMmXvCvNymTjGGdqlgwYiEeLUtOXST8NgU7NRKxrT2s3Q4Ig8r7enMBx38cfVzxauiG+3r+6BWPRpJEhmXyne/X6Ravw0Mnn6YM1ejn7E3IazDCye7fHx8iImJeezx2NhYihYtmi1BCWFNFAoFs4fXoUxRU4JYqzPQd8oBouNTLRxZJiVFodz9pSnJdfgHFNoksHdFEXXN0pEJa1WoHMbKb2V4SJEYiVGhRJESi/LcKtR/9kA9szyqNf1QXFwDqY/PTiSEyLy4hxr6TDlAqtYAQAlvJ+Z8WBel0oqG3QuRj9yPT2HuPtNw4n4NSlKsoPTGEc82qHEpPJ1t0dkqKVbendOLOjHyzYq4u9iat9HqDPy59wbNR26j3ZgdbDgYhk5vsGDUQuScF0529e3bl59++ol9+/YRERFBREQE+/btY+7cufTv35/ExETzf0IIkwKONiwd1xCH/4+Vvx2RxPszj2Aw5OHJUJOiUe6ZhHpOdVSHv0ehTcTo6IG++efo3j+BsVRjS0coXiH6jt+jG3EJXftZGMq0wKi0QaFNRHl5Pep1A1GeXGzpEIXIt4xGI0NnH+X63YcA2NkoWTK2IQWdbZ/zTCFETpm+8ypJGj3eLnYMbFTS0uGIfMDZTs1HLU2lUv46HU5Mqo4JPatybklnZg2tTfniGWeFP3o5kj5TDlJjwEZ+WHOZ2IcaS4QtRI554ZpdrVu3fvTk/xfZNhqNjy0rFAq2bt2aXXHmCVKzK3/KS+OwV+4MZdjso+blCT2rMPLNShaM6OmUWz5GdfoXAIyOhTDUHYqhRl+wdbJwZHmrTUX2eOE2TYlHcW0HyqDNKK7tRNdvNxQqa1oXG4Zq0zCM/h0w+LeDAsVyJmjxTHKf5h8/rQti4qLT5uUZH9SiV5uyj20nbWp9pE1z1sue34t34nn95yMATOsaQJdAGT2TV+T1e8ZgMNJt3lEu3ImnZomCrOxXK8N39H3n7jNvfTDbjt957LmOdir+06wUAzv64+f76nzXzettKl6e+kWf8O233+ZEHEK8Et5pUZqjlyJYsSMUgK9XnKemvweNqnhbODIgORZsHEBtB4Ch3jCUV/7GUHswhpp9wdbZsvEJkZ59AYyVuqGv1A10KRnqxSmDN6O8eRBuHkS1fRyGotVNia/y7cG9jAWDFiLvOXIxgs+XnDEvv9mkJD1by30ihCX5eTszvp0/+65E0rlqEUuHI/IRpVLB+Hb+dF94HID4FB2uDqaJfRQKBa9VLcxrVQtz7U4CCzZeYeWuUBKTTQXtk1L1LPn7Kkv+vkrz6kUY3NmfpoGFrWsWefFKeeGeXa8y6dmVP+W1bH1yqo42o3dw4XosAJ4F7dgzqw1FCjlaJqCUOJTH5qI8Ng9DkwmmxFYavTZPznyX19pUZF12tqnizikUZ38zzeyY+CDDOqNnBQyBvTDU6p8txxJPJ/dp3hcRm0KTEVu5F22a7KF8cVe2T2+Fk/2TfwuVNrU+0qY5K6vnN220jMg78ss9cyoslkBf1+deP/GJGn7dEcr8jVcIe/B4GSI/3wIM7OjHW01LPfVvQ36XX9pUvLgXrtklhMgaBzs1S8Y2xMXRlESKiE1lwLeHcr84ZEo8yv3fov4xENX+b1GkxqM8vRSM6fLfeTDRJcTzGItWx9D2W3QjLqDrtQV9nfcxuhYHQBFxGeJuZnxC5BUwSnFW8WrR6w0M/O6QOdHlZG/622StX2aEyA+Mxox9ECTRJV5W9eIFM3X9FHCyZcjr5TkxvwPLPm1EgwCvDOuv3Ipn1E8nqNJnPZ8vPcPtJyTEhMirJNklhAWULurCjx/WMS8fvhjBpOXncufgqQkoD0xHPScQ1b6pKFLjMdq7om88Fl2PTSAfrIS1UCgx+tTG0OJLdB+cRNtvN/qGH2Oo9MajbVLiUC94DfX3lVFuHYPi+j5Tj0YhrNy03y6w7+x98/KsYbVfqRotQuRFc/aGMnbtBSIS8smM3SJf2HslgnvxKc/cRqVS0r6eDxu+ac7e2W3o3rwUtupHqYLYhxp++Osy1QdspO+UAxy7HPFYclaIvEZ+vhPCQjrU8+X91/35aV0wAD/8dZk6FTxoW8cn5w4aG4Z6cXMUyTEAGO0KYKgzBEOtQWAvX3KEFVMooHAVDIWrZHz41hEUBi08vI/q5GI4uRijgxvGcq0x+LfHWKqJqZadEFZk18k7TP/jonm5f4dydG1cwoIRCSHuxqUwb/91UrQGfAo6MLSp1M4TWTdu7UVWnwqnS7UiTOtWOVPPqVzajR8/rMt/e1Xll63XWLwlhAexpmSZ3mBk/cFbrD94i2pl3Rnc2Z/ODXyxtVHl5MsQ4qXkuZpdOp2OlStXcvjwYRQKBfXq1eOdd95BpXr8BlqwYAGHDx9GrX6UsxszZgxly5pmENq2bRsbN27EwcGBfv36Ub58eQASExOZNGkS48aNe6HaW1KzK3/Ky+OwtToDnT7dxbHLkf9j77zDori6P/6Z2V16L9KbIFgBe8EWjb3HRGNMMzExvdc3edN+aW+qKSamaxJNV2OvUaPGXkBFQVBAQHrvuzvz+2N1cIMNBZYyn+fh0blzd/bcPTuzM9977jkAONvr+GvuaIK9GykZvCyjWTAaIT8RqfccpL73g43z5V/XzGjOPlW5Oizq0+J0xMTVCMdXmsSv85Y0yo6+GB4+BIIaCF1f1PO0eZKeU851j62loNRUYr57BzdW/e96rK/gQUX1aetD9WnjUp/P98nfDrM87gw+zjaseyQGWytVPGiOtLRzZtmhTJ7+4wgAv8/pS5R//e/7q/VGlm1L44vlCcQmF9bZ7+Vmy11jw7hzdBgezjYXOELzpqX5VOXKqffde3V1NVVVtWGQ2dnZLFmyhH379jWIQcuXL+fEiRO89dZbvPnmmyQmJrJixYqL9h8+fDhffvml8ndO6CoqKmL58uW8/vrrzJgxgx9++EF5za+//sqYMWNUwUrF4ui0It88G4OHs6kCYnG5nrve3k5VjfHaD15ThvjPx2iW3F3bJggYJ36K4cGDSEOea5FCl4pKg+Psj9T7Xoy3Lcfw6FEMYz9ECr0eWdQhB8WYCV3invkIhxZBRb4FDVZRuTpq9EbuemeHInS5OFjx3XMDr0joUlFRaTwOni5iedwZAJ4e2UEVulQajImRPkT6m55531h9/KqWHlrrNEwfFsKmD0ex6u3rmTAgAFGsTXuSXVDJWz8eJnLWnzzy8W6OnqoriKmoWIJ6i10vv/wyGzduBKCsrIxHHnmE33//nVdeeeWSotSVsm3bNiZOnIiLiwsuLi5MmDCBv//+u97Hyc/Px8vLCxcXF7p27UpOjqkiV2JiItnZ2QwePPiyx9Dr9VRWVpr9qag0NL7udnzx1AAlVVZsciEvfHXg6g9YU4648xO083qi2fwa4rE/EVJ31O537wC2Ltdks4pKq8XeE7n7bRhv/hnD4wkYr3uxdp9RbyrqsOpRtHM7oflxMuLer6Ek03L2qqjUg5e/O8T+hFqhdv6T/QloZ29Bi1RUVCRJ5vXVxwHoHuDM+G7eFrZIpTUhigIvjjWtbjp4upgVcVlXfSxBEOjXxZMFzw9k/5fjeWhKR5zsa4tZVeslFm04yeBH1jLphU2s2Z2OsakLcKmonEe9c3YlJSVx3333ASZhytXVlc8++4zt27ezcOFCJkyYcNXGlJeXU1BQQGBgoNIWGBhIfn4+FRUV2NnZ1XnNjh072LFjB87OzgwePJhRo0YhiiJeXl7k5eVRUFBAamoq/v7+GAwGfvzxRx544IErsmflypUsW7ZM2dbrTUmLCwsLMRgMVz1OlaalpKTE0iZclshAKx6ZEsZHS5IAWLA2ia6BtkyK8b3yg+grsTu6GLtDX6OpND3MSDp7KrveSrnOG7mgoDFMtwgtwacq9aP5+tQWzp47QmUBjoFDsU7ZjFhTgpC6HVK3o1n/HPp2kVS1H0ll5+nI1mrEJDRnn7ZN1uzJ4ssVicr2/RPa07O9DQX1+G1Qfdr6UH3auFzJ57s6Pp+4dFO/Rwb6UlioRsU0Z1riORNkD6M7urH2eAH/W3ucXt5abK4xotdBB49ODmL2aD+W7chk4fpUTmVVKPu3x+WwPS6HwHa23DYiiKmD/HC0bZ7pwluiT1W4oqWn9f7GVVdXK6LT/v37iYmJQRRFOnbsqERPXS3nlkeeL2qd+39VVVUdsWvEiBFMnz4dBwcHTp48ybx58xAEgdGjR+Pg4MCtt97KRx99hK2tLXfddRerVq2iZ8+eGAwG3nvvPfR6PSNGjKBXr14XtGf8+PGMHj1a2S4pKeH333/H1dVVXQLZwmgJ67BfvMOVwyll/HXANOPy0sJ4+kcG0DHo8g/OwqEf0Wx5E6HcdA7KOnukXncj9XsQKzt3rBrTcAvREnyqUj+av0/d4KZvMBprkFJ3IBxfiZi4BqE8B11OHNq8eGwG3FcbOWmoAo11m65w2vx92jY4kV7C89/WJqQfFOnFq3f3RqOpfy461aetD9WnjculPt+KGgPz/zkMwJRoHwZ2Dmgqs1SugZZ4zvxnvB1bk7eTU6bnj6MlPDysYQoguAEP39SOB6dG8deBM8xfnsDmg7XRY2k5lbyx6DgfLUli5oj23DM+nBAfxwZ574akJfpU5fLU+y7H19eXHTt2kJOTw759++jZsydgypF1ocir+mBjY0poV1FRqwqfWzp4bt/5BAcH4+TkhCiKhIWFMX78ePbs2aPs79OnD6+++irPPfccOp2O/fv3M27cOL799lvGjx/PI488wqJFiygvL7+gPTqdDltbW7M/FZXGQhQF5j8xAF8P03lUUW3kjre2UVqhv+xrhaoihPIcZJ0dxn4PYXhwP9Kwl8DOvbHNVlFpe2iskNtfhzT2fQyPHMZw+yqMfe9H7jbdbImwuPUttJ/1Rtz0MkL6XpDVUH6VpqeiysCst7dTXmmKSPdys+XLp/pfldCloqLSsBglGNXFC0cbLU+O6GBpc1RaMT7ONtwzKASANUezMDTw8kJRFLi+ly+/v3YdO+aN5c7RYWa558oqDXyxPJHec1Zy6+t/sy0u+6ryh6mo1Id6R3bNnDmTt99+my+++ILo6Gg6d+4MmKK8QkOvTSG2t7fHzc2NtLQ0vLy8AEhLS8PNze2KhDThErPnCxcu5NZbb0Wr1ZKWlkZoaCg6nQ5XV1eysrKu2XYVlYbA3dmab5+NYfxzGzEYZZIySnli3h6+fGpA7ffbUIV48AekiHHgZFrmKPW8C6pKkHrfA/aeFhyBikobQ9QgB/RFDuhr3i7LiMdXIRSloNk1D3bNQ3bwQooYhxwxHjmwP2h0Fz6mikoDIcsyT32+l2OpxQBoRIGvnx5AO1d18k5FpTngaKPlv+M68tjwUBxt1N8ElcZldkwwDtZabuntj7YRJzw6Bjrz/oO9efH2SL5fl8zXq06QmWcKZpFlWLM7gzW7M+gS7MKcieFMHRKMjVqUQaURqPe3fPDgwfz44498+umnvPnmm0p79+7dlVxe18KgQYNYsWIFRUVFFBUVsWLFCoYMGXLBvrt376ayshJZljl16hSrVq264JLE7du3065dO8LDwwHw9PTk6NGjFBYWkp2djYeHxzXbraLSUPTu6MFrd3VXtpf8ncY3q0+YRK69X6Od1wvN+ucR//mo9kU6O6Sh/1GFLhWV5oIgYJj+E8ahLyL5RJuayrLR7P8W7eIb0H7UGSFpo2VtVGn1/Lj+JL/8laJs//eOKAZ0bWc5g1RUVC6IKnSpNAW2VhpmDQhqsgq8ro7WPHpjZw58NYFvnomhd0fzZ+6jKUU88vEeou76kzd/jCOrQC0Gp9KwCDU1Nc0qftBgMLB48WJ27twJwIABA7jlllvQaDQsWLAAgDvvvBOAN954g/T0dIxGI66urgwePJgxY8YgirUaXmlpKW+99RYvvPAC9vamikPHjh3jm2++oaqqikmTJjFixIgrsq2kpAQPDw/y8vLUnF0tiIKCgha3DluWZe7+3w7+3HEaAJ0os67XAnqJpiqNssYaqc+9SMNetqSZFqMl+lTl0rR6nxanIyasQkhYiXB6N4IsoX9gH7gGm/aXZCCk70EOvR6sm18ui6uh1fu0mROXXMDopzdQrTctVRnT148fXhh0ySj4y6H6tPWh+rRxudjnuz+1kF2nCrk7JuiaE4WrNC2t6Zw5lVdOkJsdoth0uUX3J+bz5fIElm1Pw2A0lyF0WpHJAwOZMzGc7h2aLhVLa/KpijlXJHa9+uqrPPXUU9jb2/Pqq69esu/LL7feh29V7GqZtNQLWElpOSMeXkZSvkm8DbAq4O/oubj0vQmp/8Pg6GNhCy1HS/WpysVpUz4tz0VI+we50ySlSdz5MZq/XkPWWCOHDEHqOB65w6gWnXevTfm0mVFcVsPwJ9Zx6kwZAEFe9myeOxpnh2srV6L6tPWh+rRxudDnK0kyU7/YzZHMEsZ182butEgLWadyNbSGc0aWZf5v1XEW7TnN21O6MqV7Paq/NxCZ+RV8tzqJBWtOUFBaU2d/304ezJkYwbj+jbvkElqHT1UuzBXl7LK3t1dmAs9FR6moqDQuToZcvvd7l+GFj1MpWXG6xo17az5l8fUjm3QGRkVFpYGx9zQTugCoLkfW2SHoKxCS1iMmrUcWNMhBA5AjxiOFjwWntitwq1w5sizz0Ee7FaHLWify3XMDr1noUlFRaRiWHMrkSGYJggD3DAy2tDkqbRBBECirMSLJ8P6GE4zs3A5763qn8r4mfN3teOG2SJ6Y1pk/tqYyf3mCkl8SYPexPHYfy8PPw47Z4ztw+6gwXNTfMZV60uyWMTZn1MiulkmLUeuNeihMAY/aajzimqf4KcGDBzYFKW2mH4YuFjCw+dBifKpyxag+BfQVCCe3ICasREhci1Bdouwy9n+4xS1bVn1qGeYtPcZL3x5Stj94sDd3jA5rkGOrPm19qD5tXP79+ZZVGxg5dzu5ZTXc2MOPt6a07fu5lkhrOWeyS6oY+dEOKmqMPDCkPY9f3zC/E1eLLMtsi8vmi+WJrNubwb8LNdpZa5g2LIR7J4QTEeDcoO/dWnyqUpd6xwRWV1dTVVWlbGdnZ7NkyRL27dvXoIapqLQZjHqEQ4vQzu+HdvFUMFQru6TR7zL9see4bWRttdC3Fh3m79gsS1iqoqLSmOjskCPGYpz4GYbHj2OY8RvG7ncg23siR4yr7VdZiPbr6xD//h9kH6XOHaFKm2XX0VxeXRCrbE+7LpjbR6nVplVUmgvzt54it6wGe2sNT1hYXFBp23g52XDf4BAAvtmRQkaRZZPDC4LA4ChvFv13MHvmj+feCeHY29ZGm1VUG1mwJokBD6xm2stb2LQ/E0lS739ULk29I7uee+45Bg4cyPjx4ykrK+Ouu+5Cq9VSUlLCnDlzmDBhQmPZanHUyK6WSbNV6yUDwuHf0Gx/H6EoBQBZ1GK85XfkoIFmXSurDYx5ZgOHTxYB4Olizea5o/Fxt2tio5sHzdanKleN6tNLIBlBEOFsOgEh7me0Kx5SdsuuIUgR45A7jkf27WHq2wxQfdq05BZVMfTRtUo1q46Bzqx/fyT2Ng23NEX1aetD9Wnjcv7nm1ZQweiPd6A3yjw1ogNzzgoNKi2L1nTOVOuNjPp4BxlFVYzt6sVH06MsbZIZJeU1LN54ki9XJJKaXV5nfwd/J+ZMDGfadSHX9FvXmnyqYk6974iTkpLo2rUrANu2bcPNzY0ff/yRZ555hmXLljW0fSoqrQ/JYHpYnd8f7cqHEYpSkEUtUvRtGO7fU0foArC11vLdcwNxsjeVps4tquaed//BYJSa2noVFZWmRtQoQheA7NcLY8yTyB7hAAiFp9Ds+hTtgtFoP45E3PKmpSxVsRBGo8S97/2jCF32tloWPD+wQYUuFRWVa+N/6xLRG2UCXG25c0DQ5V+gotLIWOs0PDvKdC+x+kg2e1MKLWyROU72Vtw3qSN7vxjPDy8MYmC3dmb7T6SX8NRn++h25zJeWXCI9Jy6gpgKnMk6yLHEP5W/M1kHLW1Sk1Hvu6Dq6mrs7EzRJPv37ycmJgZRFOnYsSM5OTkNbqCKSmtD8+d9iPHLAEwJqCNvxjjwCXC59I1PiI8jnzzalzve3A7AzqO5vP5DHK/cGd3IFquoqDQr3MOQhj6PNPR5yEtETFiFcHwlYlYsQlkWQukZ8/5nYsEzArQ2lrFXpdF556cj/B2brWx/9HAfOvirEegqKs2FaoNEjcE0QfnsqHCstc0jAldFZXQXL3oFubAvtYiP/krix7t6W9qkOmg0ImP7+TO2nz+HTxby5YoEft+SqpxTxeV6PvnjGJ8tPc74/v7MmRhBn04eSoG9Zo8sgyyBbDT9K+pME50AkgGqSmr3XejPJbh2UrQiH0rPKPuy8o4zJe5/1Jz3WVjJMkuHf4uPd/emH2sTU2+xy9fXlx07dhATE8O+ffu44YYbACgqKlJEMBUVlfOQJbNlRVLkDIRjK5C7TTeJXK7BV3yo8f0DeHByR+YtOw7AJ38co09HD8b2829oq1VUVFoCHuFIHuEQ8zjG4tOICauRfaJr9xuq0S6aDLKEHHo9UsfxyKHXg7WjpSxWaWA27svkvV+OKtuzx3dgyiA1akRFpTlhrRX56rYeHEwrIrqBk2u3SYrTTQ/157BzB2f1Xhij/jwRxFgrokhnhRIbJ9CcrWhoqIaybATZyGsx1qx0NDKrnw3kHKs9hlfXWhGlNMuUduXcPkkyfy9rR+SAfoopQsY+KD5d+96ybC7YeEQgB/St7X9oEUJV0YX7SkakjhPAqwvd2rvyyX1deCVkHQsOu/BNnCvZFaaVL0ZJ5s8dp/lzx2mi2ztx35QuTIoJwDo3FnH3Z+eJSud9JrKEk2AD0xcotoj7v0M4+kedfsLZf6WggUgj31D6a5bORsg4cOHPXpaQBj2F1Ptepb/23SAw1Jw9prGOGw23rUAO7G/6XLIOo/1uxCXdrn8+CwSTrCMeXYJm/fPKvhIrHTV+5tW8awSBopI0Vey6EDNnzuTtt9/miy++IDo6ms6dOwOmKK/QUDUJqoqKgmREOLYMzbb3MI59HzlwAABy+2EYHtx/1T/K/70jin0JpnK8AA/O3cXmuaMJ9nZoMNNVVFRaIM4BSH3mmDUJ2UdBX4Ug6RGO/Yl47E9kjTVyyBCT8NVhNNipeSpaKuk55dz3wU5lu0e4O6/d1fpvXlVUWirdA10sbULLpzgd7ed9EYy1BZ1kUYs04DGwcUJu1xU5ZLCyT9z7FVQWXFREkbtMRfY9e92sLkXc9LJJ1JAkoK4wIvZ5Fs7mdxIy9iFue+9soZi6fbF2wjh98Xm2fIl4aLGpr1Q3UkcOHoRx3Fylv+a32xBO775oRI805Hmk/g8r/bXvBCJI+ot+dIbblivPI+QeQ/ft9QB0PvtHknl//XOZijgmHl+JZv1zFz227B2J4e6/zD538egfF+1v7Hm3mdil2fEBQlHqxY/v1h7Z62z1UkMV3oc/4Tng8a5aluR35/OsoRwqD1T6HzpZwn3v7+Tl7w5xdx+J2bkb8dCVXfDY1jZumElOxacRT++6qC2Cc4B5Q1k2QnHaRftTU2G+bdRf0k/ItWlq5CvJw3pe/3MBFplaDavt7fnD0f7yr2/F1FvsGjx4MF27dqWgoID27dsr7d27dycmJqZBjVNRaZHIEsKxP9Fsew8hLwEAcdu7GGcuNe0XhGuafdJpRb5+NobrHl1LXnE1JeV6Zr21nTXvjsDGStMQI1BRUWklyH49MDx+HCFpPeLxVQjJmxAMlabtpPXIOjsMjyeAztbSpqrUkxq9kbve2UFhaQ0Aro5WfPtsDNY69XdARaW5UFZtYOfhLMZ29Wo5S6qaOULsT2ZCF4AgGdBsfw8AY49Z/xK7vkQoPHXR4xm8I+Gc2GWoRnPw+0u/f/faAjGU5yImb7xoX9nO3byhLBsh58jF+5dFmDdUlyBUFlzcGMnwL+MuI4xI50k69RVRRNEkvAias0VzxNriOaIG2cpcVJHt3JGd/Ov0U7YdzPNvyd6RyPbtavOUmr2HCA5etZ21NqZIL0FEJwhMF0SmcYpdOUXMP+LLihR3JNl0vmUXVPLmWnhf8wY3RRQzJ7qQru30ZseuMAicn+hBDh2O0da1rg2CyTbZJdDMdinmSaTueaY+58aIqIxD9uhg1t8447e6Y0REFs/+3/W84hWeEejv22V+7HPHP9dfNEW2FVcXs8HRgTW9J3AwLxaVq6jG2JZRqzG2TJqswoYsIRxfgWbbuwi5pmWGMgJy16kYBz4J7h0uc4D6sTU2i6n/3WyaTALuHB3G+w82v3X2jYFaNaX1ofq0idBXICRvRkxYiXBiHbJ/H4w3/6zsFvd+DYZKpIhx4Nb+Ege6PKpPG5fnv9zPlysSle1fXh7C9b18G/U9VZ+2PlSfNi6vLItl0f5shoR78NWt3VXBqwEQVzyMJu6nOu2SR0ewdkCOGIvU/xGlXbPiISjLVoSKfwsYUq+7a4tD1ZShWfe8Is7IZiKH6f/5ne/E1f/sPX3eCcQj54SLf4tAIuhskXrfo9giZOxHyIo777jieSKGAI4+ZoWqhNO7obLwX/3OE1FcguD8KKMzsRcWis79OXqD7mzaIaPeFPF23n69JPDL/ky++SeN7+/qQ4Cnq1mBnJbC6ZxyvlmVyPfrkikurxtBNTCyHXMmRDCqty8ajdjir4NJRUl8FvcZ2zO3YzhPAPWy82KQRw/+TF2Nvo3m7LoqsSsxMZGtW7eSk5ODwWCuKL/88ssNZlxzQxW7WiZNcgHLP4F2yd0IOfHAWZGryxSMA58Gj4YVuc7nvZ+P8Naiw8r2Z4/3Y/qw1l/Kuql/lM6Un6GoukjZdrF2wcfe5+IvUKk3Lf1Go0VirDHlPHE8+12WZbSfdkcoSTdttuuCFDEOqeN48OxU7xte1aeNx7Jtadz9zg5l+4lpXXjhtshGf1/Vp60P1aeNR0p+OWM+/geDJPPc6HDujgm2tEktD30l4qFFSF1vBFsXU9upregWT63b9a5N4BPV6Ca15nOm2iAx5uMdnC6sZGTndsybEW1pk66Jsko9v/yVwpcrEkjKKK2zP9jbgXsnhDO6pytBfu0ucITmiVEyYpANWGusAUgtSWXKyikAOOocuT7wesYGj6V7u+6IgsiZrIMUldQus3RxCmwTQhdcxTLGzZs38+6779KzZ08OHDhAjx49yMjIoLCwUF3GqNJ2cfSF8lyTyNV5kknk8oy4/OuukSemdWH3sVz+OpAFwFOf7SUy1JVOQS6N/t5thTPlZ5i8YjL689bWW4lWLBm/BF+Hxo2iUFFpVDRWtUIXmCK6Oo43VXcsPo2QcxRNzlE0295Bdg0xCV+954CTKvRakhPpJTzyyW5le1CkF8/d0tWCFqmoqFyI/61NxCDJBLnZcVvfwMu/QKUWfQXigYWIOz9BKM+BygKkwc+Y9rmFImuszXN2aaxNSepVrglrrchzo8N58KdY1sfnsPtUAX1DWq6w52Cr4+5xHZg1Joy/DpzhixUJyjMTQEpWGf/56gBv/KDh1pGh3DM+nBCf5lnAR5ZljhceZ03KGtamruWOTncws+NMAIKcgriz8510c+9GjG8MVueKEJzFx7t7mxG3/k29I7vmzJnDuHHjmDhxIpMmTWL+/Pl4e3szd+5c3N3duf322xvLVoujRna1TBp8BkaWERLXIBSlIPV9QGkWTqwzhRN7dmy497oC8ourue6xtWTkmZIfhvk5svGDUTja6ZrUjqakKWbVKvQVrE9bz+Lji0kqTqqz/95u9/Jb4m/4Ofjh7+CPr4Ov6V9707/t7NqhFes9n9Bmac0zpS0OWYasOMSEVabljnm1S+X0Dx2qzTlYUw5aa7jI91z1acNTXmVg5JPrOZ5WDICXmy1b5o6inWvT5FxTfdr6UH3aOPyTnM8dC/YDMP+WaIZ3ajlRIxalphzxwHeIu+YhlOcCIGttkPo/jDT42dp+FqzG2NrPGVmWuf27few6VUhHb0eW3d8PjdjyljJejITTxXy5IpFfNp2issa8EqIgwKg+fsyZEM6gyOaRZy+jLIM1KWtYnbKalJIUpb2LWxd+GP2D5QxrIdT7SSwzM5M+ffqYXqzVUlVVhSAITJ06lWeeeaZVi10qbRxZRjixFs3f7yBkH0bWWCF1mgxOpugeucMoi5jl7mzNN8/GMOH5TegNEkkZpTz+6R6+enpAs7hItyRkWeZo/lGWJi9lXeo6KgwVF+2bW5FLYXUhhdWFHMmvm2y0m3s3Fo5aqGxvy9hGpaESfwd//Bz8cLJyUv2j0jwRBPCJQvKJQhr6H8hLNEV7FSSbPUyIe79C3P0ZcofRpsqOIYNBa6M8hGhLSqDaSS0J30DIsszTn+9VhC6NKPDNMwOaTOhSUVG5MgxGiTdWmwoU9Ql0ZFhHTwtb1AKoKUPc9y3i7nkIZ0UsWWeH1ONOpH4PmicnB9Nvivq70igIgsALYzsy6bOdHM8q5bf9Gdzcu/V81hEBzrz/QG9evC2S79cl8/WqE2SeDRiQZVi7O4O1uzPoHOzMfRMjmDok2CIFwPbn7OfTQ58S+69E8xGuEYwJHsOoIMs8d7Y06i12OTo6UllZCYCHhwcpKSmEhIRQVlZGVVVVgxuoomJxZNlUuezvdxCzai84cthIuFTZ2Cakd0cPXrurO89/aZpFXLotjX5dPJk9LtzClrUcJFnizvV3mglXtlpb+nr3ZUv6ljr9B/kOop9PPzLKMsz+zpSfwSAb6ixx/C7+Ow7lHlK2HXQO+Dn4KX/D/IcR5dn4uSZUVOqNRziSR91riZCwCqGyACFuMWLcYmQrB+SgGITkvxAkPecWlMgaawz371YfTK6RH9ef5Je/UpTtl+6Ion8XNVpERaW58cv+DBJzyhAFeGxIoDqxdSXkJ6PZ/BoAss4eqdddptUT9qpQaAk6ejsyrZc/P+9N58NNJxjXzQtHm9a1YsTV0ZpHb+zMA5M78suG4yzanMGeY3nK/viUYh75eA+vLojlzjFhzBoTho+7XaPZU2moRBREJQ+XVtAqQpePvQ+jg0YzNngsoS6hjWZDa6TeYle3bt3Yv38/ISEhDBo0iM8++4xDhw5x4MABundvm2tBVVovQvImxK1vI545qLRJ4WMxDnoavLtZ0LK63DO+A7uO5vDnjtMAvPj1Qbp3cKdnuJrD4EJIskRmeSb+DqYHcFEQCXIM4kj+Ebq6d2Vy6GRGBY2ipKaEfzL/oUaqUV5rJVoR4RZxwST1RslITmUO0vnlmjEltXezcaOgylRCukxfRkJhAgmFptnfAIcARezSG/VMWjEJH3sfJRLs/D8PGw/15lnF4hinLUJKXGOK+kr5G6GmDOHEujr9BGO1abmJKnZdNXHJBTz7xT5le0xfPx6c0rRL5lVUVC6PLMusPmzKCTSjdwChHmrk5QWpKoHSzNrUHz5RSF2mIjv7m0QuNf+WxXlseBgr47IoKNczb8tJnhvd+LmILYFOKzK2rze3junMgcR8vliRwLJtaRiMpkxP+SXVvP/LUT76PZ7JgwKZMyGCHg30bGWQDOzJ2sOalDVsTt/M872fZ1zIOAAiPSKZ1XkWMb4xRHtGIwpig7xnW6PeObtKSkrQ6/W4u7sjSRK//vor8fHx+Pn5ccstt+Do2DyTujUEas6ulsm1rK3XrHgY8Wx5Y6nDaIyDnwHvxq94dbWUVOi5/ol1JJ+tOOLvacfmuaNxc7K2sGUNy7X4NLcyl5UnV7I0eSll+jLWTl6rJHJMKUmhxlhDuKt5FEtDVmOs0FeQWZ5pFg2WXpbOXV3uUsSutJI0Jq+cfNFj2Ghs+G2cKV8YQGZ5JomFiSYxzN4PO13jzTw1Fq09B0arp6oY4cQ6xNifEFO31dmtn7UevLqBpnXNDDcFxWU1DHt8HSlZZQAEedmzee5onB2sLvPKhkc9T1sfqk8bHoNR4pf9GYzp4gXVZernez5VxYh75iPu+QKcfDHc8ze0sIf4tnTOfLMjhS0JufxnTEc6NdPE7Q3Bv316Jr+C79YksWBNEvkl1XX69+nkwX0TIxjX3x+tpn7fX1mWiS+IZ3XKatanrie/qjb33CDfQXw09KOrH4hKHeotdrVlVLGrZXLFP0qyjJDyN3JAP1PSZYDCU2g2/BfjoKfAJ7pR7Wwo4lOKGPnkeiXp4vU9ffjppSGIrSi5ZH1vNAySgZ1ndrI0eSnbMrZhlGsTUn4+7HP6evdtDDOvmtKaUrZlbCOzPJP0snRFFMuuyEZGRkBg5/Sdiki3JGkJr+95XXm9m42bkijfz8GPDi4dGBk00lLDuSLa0s1jq+ZMLLpvh9dpNgx6Fs2hH5D6zEHqfjtYt96b5oZElmVue2Mba3ZnAGCtE1nzzgiiwixzrqjnaetD9Wnjon6+Z6ksNIlce79EqDZNyMrWThjuXAsXWCbfnGlLPjVKMqJAq19NcDGfVlYb+GNrKl+sSCA+pbjOfj8PO2aP78BtI0Nxdbx8YMGm05v49NCnpJamKm2iINLbqzdjg8dyXcB1OOgcrm0wKmZcVakwo9HIjh07SEtLAyAoKIgBAwag0TR98jYVlWtGlhFObUX8+3+IGXsxjn4Xqecs0z7XEIzTfrSsffWkc7AL7z3Ymwc/3AXAxv1n+PC3eJ6c3sXCljU9sizzxeEvWJa8jJzKHKXd3cadie0nMqn9JAKdml85cEcrR8aGjK3TXmOsIas8i6yKLLOywlWGKhx0DpTpTZEfBVUFFFQVKPnHoj2jzcSuDw58wImiE0olyXMRYWrifJVrxs79giXhxePLEUoz0Wx6GXH7e6akw73uBaeri5BsK8xbelwRugDentPTYkKXiorKpUnKKSOsnfqgakZFAeLuzxH3fYVQY7pHkW1ckPrch9T7HrBxtrCBKpeiNVVhvBpsrbXcOjKUmSPasy0umy9WJLJuTwby2VChjLwKXl0QyzuLjzB9eAj3TggnIqD2O11YVYijlaNSnV0raBWhq6NrR8aGjGVU0Cg8bdXcdI1FvSO7UlJSePnllykoKCAgIACA9PR0nJ2dee211wgJCWkUQ5sDamRXy+SiMzCyjJCyzSRype9WmqXoWzGOm9t0BjYSj3+6h+/XJQMgigJ/vDaUwVHeFraqYbjUrJosy2ZizcObH2bHmR2IgsgAnwFMCZ3CQL+B6MTWt5yqpKbELBLs3F+4aziPdX9M6Xf7utsvWEESTInzH4h8gJsjblbadmftxtvOGx97HzORrSFpSzOlrZ6z1RhLSkpMv5V27iBoEPd+gXhwYe2svqhD7joVY98HoV0nCxvd/Nh1NJeJ/9mEUTLdpk0fFsy8x/pZVIxWz9PWh+rThmHbiTzu+v4Ak6J8eGNyF6y1pqVNbf3z1XwzXCnuJNu6IvW53yRyteDo3rboU1mW2XQ8lz8OZPDJzVH1XrrX3KmPT09mlvL1qkR+3HCS8kpDnf1DurejT98y0m03sjt7F3OHzCXGNwYw5eT9Nv5bRgaOJMS59WomzYl6i12PPvoozs7OPP3000p+rtLSUt577z2Ki4uZO3duY9jZLFDFrpbJhS5gish1epfSJrUfhjT4GWS/Xk1tYqNQVWNk9NPrOXyyCABPF2s2zx3dqJVEmooL+fRk8UmWJS8jLi+O70Z8pzwQ7jyzkyN5R5gYOhEvO68LHa7NsS51HclFyUq+sIzyDCVxPsBLfV9icuhkwFQdJuZX04+0gEA7u3a1yfLPRoMN9huMo9W13bi2xZvH1s4FfVpdinhwIeKeLxBKzyjNhjtWI/v3aWILmy85hZUMfWwd2QWm6tedgpxZ995I7G2uKiC/wVDP09aH6tNrR2+UmDBvJ8m55Qzu4M43t/dU9rW5z7e61EzIEg7/imbDi0j9HkTqeVeLFrnO0eZ8CqQVVDBi7nYkGV6Z0ImZfQIsbVKDcjU+LSmvYfHGk3y18oSSU/N8tI75OEUcYMaw9rw26MWGMlWlntT7rik5OZlPP/3ULBG9o6Mjd955Jw8//HCDGqei0hgIp3ejXTRF2ZZChiINfhbZv7fljGoEbKw0fPfcQIY9vo6Scj25RdXMfucflr0xDJ22dczIVBoq2ZC2gWXJyziUe0hp35ezj95eJn/29+lPf5/+FrKweTIqaBQEmbednzj//AT9uZW52GhsqDJWISOTXZFNdkU2B3IOKH1WTFyhiF0Hcw7yXfx3yvJIfwd/fB18W2zifJUGxtoRqd9DSL3vRTi6FM3ueWCsMZ9kKM8FW1cQLSvsWAqjUeLe93YqQpe9rZbvnhtocaFLRUXlwvy0N53k3HI0osDzrbRi3WUpy0bc+QniwR8w3LkG2nUGQO5yA4aIsWClLu9syQS62TGjdwCL9pzmo01JjO/mjbNt61shUR+c7K24b1JHXCJieW/NejKPRFCdU3tzbSh1p2DfCH48pkNMOsjsceH4t7O3oMVtk3rfOfn7+1NYWEhwcLBZe1FREb6+vg1ll4pKw1JTDlamC4zs3wfJtwdYOZhEroDmlZy8IQnxceTTR/tx+5um6mi74nN544dYXpnV3cKWXRuJxYlsStrE2pS1lBvKlfZgp2CmhE4hzDnMgta1TOx0doS5hBHmYv7ZBToGsmPaDvKr8s0rSJank1mWyZnyM2YRc4lFiWzP3H7B93CzcSPKI4r3B7+vtKWXpSMKIjq5bd80tTk0VsiR0zF0mwZlWWbVuDQrH0HITUDqex9S1Ezl2t1W+N9PR9gWl61sf/RwHzr4q9HkKirNkcKKGj7+KwmAmX0C2l7OrtIzZ0Wu7xEMVQBo9n5Zmw5E1KpCVyvhkWGhrIg7Q2GFnk83J/PC2I6WNskinC49jb+Dv7KCxEZnjeQVi6/3YcI1w6hI7M2eAxLVegmA4nI9nyw5zmfLEhjX3585EyLo29lDzY/bRFyR2FVeXvswedddd/HZZ59x22230amTKcfGsWPHWLRoEbNnz24cK1VUrhLh9C5cNr2ORqfDOHPp2UYB4y1/tIpQ6ithXH9/HprSkU+XHgfgkyXH6dPJk7H9/C1s2dUz9+hcEksSAbDR2DAiaARTQqcQ5RGl/ng0AoIg4GHrgYetB1GeUZfs29G1I7d1uo3MskxlmeT5ifNLa0rN+n988GM2nt6IVtDibe9dmzD/7F8nt074O7Tc76rKZRAEcDwvSX1pFsLJzQiSAc36/yD+/Q5Sz7uQes0Gh3aWs7OJ2Lgvk/d/Oaps3zM+nCmDgi7xChUVFUvyyeaTFFcacLHV8fB1oZY2p+koyUT85yPEQz8qBUlkBy+k/o+YKu6qtDrc7K14eFgob6xO4Mfdp7m5dwChnm1jMiq/Mp91qetYk7KGowVH+XHUj3R2N0UvDg8YToW+gpFBI/Gw9QAgt6iKhWuT+Hb1CbILTSKwUZJZvuM0y3ecJirMjfsmhjN5YCBWOrXAX2NyRTm7Ro0aZfYAKZ8tQXCu7fzttWvXNoadzQI1Z1fLQUjfY8rJdWqr0qa/axP4XPpBvbWiN0hMfuEvdsXnAuBkr2Pz3NEEezfv2TZZljmYe5C92XuZ022O0r7w0ELWZ61nSugURgWNuuZ8USqNhyzLlNSUkFFuigiz1dgy0G+gsv+2tbdxtODoRV8/p9scM9+/tPMlXG1cTaKYvWmZpI+9DzqNGhnW3LjqvCZFaYh7vjA9ROlNk22yxhq52zSM/R4A9w4NbGnzID2nnKGPraWwtAaAnhHurHxreLO6EW6LuWpaO6pPr54TOWVMmLcToyTz8viO3Nq3bnXn1vj5Cgmr0SydjWA0XatkRx+k/o8idb8VtDYWtq7xaY0+vVL0Ronxn+7kZF45Q8I9+Pq2HpY2qUG4kE/L9eVsTt/MmpQ17M7ajSRLyr57u97LfZH3Xfa4NXojy7anMX95IrFJBXX2e7nacNfYDtwxOgxPl9Z/7liCKxK74uLirviAkZGR12RQc0YVu5o/QsY+k8h1crPSVuPTG3H4C8hBAy/xytZPZn4F1z26lrxi0wxcZHtX1rw7Ahur5vMgdY78ynxWnlrJsuRlSoneX8f+qiyxy8vPw8Pdw5ImqjQQBslATmUOxzOPUyKWmFeSLM/gse6PMT5kPABl+jIG/za4zjEEBLzsvPBz8OPFPi8S5GSKhKk0VFKuL8fdxl2N+LMA1/xAUFmIeGAB4t6vEMpzAJBt3TA8egQaqSqopajRGxn33CYOJOYD4OpoxZa5o5tdfo+2/JDXWlF9evU89mscqw5n0aGdPcsf6H/BCnWt8vOtyEf7aQ+wdUEa8KhpubnW2tJWNRmt0qf1YGtiLrN/OAjAN7f3YHCHln8//m+ffhb7GT8e/5EqY5XS5mrtyqigUYwJHkNX9671uq+UZZk9x/L4YnkCK3amI0nm8ou1TmTqkCDmTIyga4jrtQ9IReGKljG2ZgFLpfWgWf4A4uFflW0poB/S4GcodOyCm7u7BS1rHvi62/Hl0wOY+t/NyDLEnSzkP1/u54OHmkcFNKNkZFfWLpYlL2Nr+lYMcm053+6e3aky1P7giELrSLCvAlpRi6+9LzbuNhe8eTwXOQwmYezm8JsVISyjLINqYzUyMlkVWWRVZGFz3qzynqw9PP7349hobPBz8MPX3ldJmO/v4I+/gz+hLm1o2UlLw9YVKeZxpL73Ixz5Hc2ueUgdJ5gLXdlHwbMjiM1PtK8PL317SBG6AOY/0b/ZCV0qKirmvDahE16O1gzq4HFBoatVUJiC5p+5yM6BSAOfMLXZuWOcuQTZq2ubErlUTAwJ92RwB3dS8ivQii1/IlGWZZJLks3uQe10dlQZq7DR2HBdwHWMDR5LH+8+6MSrW0UgCAJ9O3vSt7Mn6TnlfL36BN+vTaK4XA9AtV5i8cZTLN54ioHd2jFnYgSjevuiaa3XlSbkiiK7cnJyaNfuynNl5OXl4eHR8lXef6NGdjVvxJ2foPnrVST/PqbE88GDQRDa/AzMv3nvlyO89eNhZXve4/24eViIBS0yMXvjbLMKf67WrkxoP4HJoZMJdgo266v6tPVxNT6VZZm8qjwyyzJJL0snoyyD2V1nK2LoouOLeP/A+xd9vb+DP8snLle2V51aRUpJilJF0s/Bj3a27dC0cCHFUjT4eSpLYKgGna1puyIf7SfR4OSD1Od+pMiba/e1IJZuS2X2O/8o209O78J/bm2ek4zqtbf1ofq0cWmxn2/BSTQ7PkQ4/CuCbES2dsLw0CGwUZ9/WqxPG5D8smocbHRYt+Dq7ieLT7ImZQ1rU9aSUZ7B8onLlRyxuRW57M3ey1D/oY1WSby8ysAvf53ii+UJJGWU1tkf7O3APeM7cMuIUJzs1FQdV8sViV3Tpk1jwIABjBkzhoiIC5fULS8vZ+vWrSxdupSxY8cyZcqUBjfW0qhiVzPizCE02z/AOOp/4HQ2uXFNOUL6XuSQIaakx2dRf5TMkSSZGa9tZeP+MwDYWmnY8MFIOgW5NJkNeqOeowVHifaMVtq+OvwV8w/Pp79Pf6aETmGw3+CL5mFSfdr6aAyf1hhrOFN+RhHCzkWDnfvr6t6Vz4Z9pvR/4u8n2JK+xewYWlGLj50Pfg5+jAsZx7iQccq+cn059jo1+uZiNPZ5KsQvQ7PsXoSzeTRkOw+kXncj9bwL7FpGNO+J9BKGP7GO8kpTJOugSC/+eG1os53NVa+9rQ/Vp/Wn2iBd8UN+i/t885PQ7PgA4cjvtddW1xCMMY8jd70J1PyYLc+nKgq5FbmsS13H6pTVHC88brbvv33+y5SwptcvJElm88EzzF+ewF8Hsurst7fVMvP69twzPpz2vmqO4vpyRWJXSUkJixcvZt26dVhZWdGhQwfc3d2xsrKitLSUtLQ0UlNTCQsLY+bMmfTp0zyWRTU0qtjVDDgTi2bbO4gn1gFg7DUbadTbl3yJ+qNUl4KSaoY+upaMvAoAwvwc2fjBKBwbeeYgpSSFZcnLWHlqJUXVRayetJp2dqao0cKqQiqNlfja+17eftWnrY6m9qksy9RINVhrapdgfH3kaw7kHCC9LJ2s8iyzpbQAD0Y9yN1d7lZeP+T3IQBKFNi55ZFK4nwHn6sOeW8NNIlPC08h7v4cMXaxUvZe1toiRd2C1Pd+cA1u3Pe/BsqrDIx8cj3H04oB8HazZctHo5t1klr12tv6UH1aPwrKa5g4byfTevlxz8AQbC+T97TFfL6SAc2KhxCOLqkVudzaY4x5ErnrVBCvKPNNm6DF+LSJOHamlANphcy8QIGG5sTLO19mVcoqs0TzQY5BjAkeQ3+X/nQL6GZB60wknC7myxWJ/PrXKSqqjWb7BAFG9fZjzsRwBkV6qblor5ArErvOUV1dze7duzl69CjZ2dnU1NTg5OREWFgYPXv2JCTE8kuhGhNV7LIgWYdNIlfiGqVJ8umONOR55NBhl3yp+qN0YfYl5DH+uU3oDaaL/uSBgXz9zIAGv3hWGirZdHoTS5OWcjD3oNKuFbS8EfMGIwJH1PuYqk9bH83NpwbJQE5FjhINll6WziDfQUR5miq6FlUXMeyPS197vhvxndI/syyTVadW4efgpyyTdLNxa9U3K03q0/I8xP3fIu7/BqHClPtK6noTxkmfN8371xNZlnngw138ujkFAI0osPzN4fTr4mlZwy5DcztPVa4d1af14+UV8Szek46rnY6Njw3EyfbSExot6fPV/HILYtJ6ZPcOGAc+gdz5hhafD7ExaEk+bWx2nSzg9gX7EAWBFQ/0p4NX86jyrjfqOV54nG4etQLW3INz+f7Y97jbuDMyaCRjg8fS2a0zQjNMeVNYWs0P65P5euUJJTDhfDoHOzNnQgRThwRha60K0ZeiXmJXW0cVuyxA4Sk0m15BTFilNEneUaacXGEjzJYrXozmdgFrTny5IpHnv9yvbL89pyf3jA9vsOPPi53HL4m/UKYvU9qCHIOYHDqZ8SHjcbe9uqVGqk9bHy3NpwbJwKmSU2b5wv6dOH/DlA3Kd3xT2iae3v602THOJc73c/AjxCmER7s/quyTZKnFF2KwiE/1FYhxPyPu/hzD1AXg1cXUbqxBOLUVOXQ4NIPP9ft1STz+6V5l+9VZ0Tx0QycLWnRltLTztKVwpvwMRdVFyraLtQs+9j5N8t6qT6+chKxSJn62E0mG1yZ2YkbvgMu+ptl+vjnxiLGLka5/rfaamH0EIS8RudMkVeS6BM3WpxbAYJSY+NlOTuSUMzDMnW9v72GxSTxJlojNjWV1ymo2pm2k3FDO+inrcbF2ASC9LJ3Tpafp7dUb7b8iFZurTw1GiVU70/lieQK7j+XV2e/maMWdYzpw19gwfNwbJ7dYS0eVAlWaN7KMkLjW9F/vSIyDnkHuMOqKRC6Vy3PP+A7sjs9l2fY0AP77zUG6d3CjV8TVFZjQS3qzZVvl+nLK9GVYa6y5PuB6JodNpoen5X4IVVQaCq2opYNLBzq4dKiz71zifDeb2hsna401Pdr1IKMsg5yKHGRkqoxVJBcnk1yczOnS02Zi17v732VD2gZlSaTZMkk1cf7F0dkh9bwLqcedZqKWcHQJ2hUPIXuEY+z7IHLXGy1WRSw2qYDnvqidZBjbz58Hp3S0iC0qDY8kS+glPTXGGmqMNeg0OpysaidI4wviKa0pRW/UUyPVkFuRy/sH3jdbNm0lWrF0wtImE7xULo8sy7y5JgFJho5eDkzr6W9pk66OrMNotr+PmLASADmgL3LHCaZ9Xl1NFRZVVK4QrUbkP2M6MmvhfrYn5bM5MY9hEU0boZxclMzqlNWsTV3LmfIztbYJWg7nHWaQ3yAApQp3S0KrEZk0MJBJAwM5kJjPFysSWLYtDYPRFKtUUFrDB78e5eM/4pk8MJA5EyPoEd4ycpY2FarYpdK8yDmGUJ5jSjIP4NbeFMXl2RE5fIwqcjUwgiAw9+E+HDlVSFJGKXqDxF1v72DLR6Nxc7qyB0FZlonNi2Vp8lJ2ZO5g2YRlOOhMYcw3driRICfTevjzb/ZVVFozgiDgaWt+szfQbyAD/QYCUG2s5kz5GbNk+Y5W5klHM8oyKKgqoKCqgMP5h/k3N3W4ied7P69sL01aiqOVoxIp1ubPt39Fbwk5R03/5iWiXfUo8tY3kXrfaxLFbJybzKzishpmvb2dar1p+XiwtwOfPtpXnQC4RoySkRqpRhGZHK0clXx8lYZKjhUcMxOgaiTzf2/qcBNWGisA4vLiWH1qtbJPL+mpNlYr//e09eTNmDeV9/7wwIesOLUCvVFPtVSNQTLP9Tep/SRe7veysv3Szpc4WXzykuOpkWooqi7Cx96H+IJ4Ojh3uGjBFpWmYdPxXP45WQDAf8ZGoBFb2Dl7JhbN9vfM0oHI7bqCddNd/1RaJwPD3BkW4clfCbm8tSaBgaHuWDVBlUZZlrl3073sz9lv1h7lEcXYkLFcH3A9rjaujW5HU9Ej3J0vnhzAK3dG892aJBasSSK/pBoAg1Hm962p/L41ld4dPbhvYgTjB/ijbabFbpoSVexSaR7kJqDZ9g7CseXg5IfhgT1w9sZTGviEhY1r3Tja6Vjw/EBGPLGeyhojGXkV3P/BTn56aQjiJW7mCqsKWXVqFUuTl3Kq5JTSvj51PTeE3QBAe+f2tHdu3+hjUFFpSVhrrAl2CibYKfiifR6JfoQJ7Sco+cLOLZc8lzj//EIOsizz7v53qTJWKW2Oulrhy8/Bj6lhUwlwvPySm9aKdP3/IUXfjmb3PITDvyKUZaPZ/H+IOz5Air7NlMzeya9RbZBlmQfn7iI1uxwAa53Id8/F4Oxg1ajv21gYJIMiIOklPR62tRHB2RXZpJelmwQgY3Wt0HRWQHK3cWd44HCl/x9Jf5BYmGguRJ0nNI0NGcvUsKlK/xtX3UhBVYHp2EZ9nWISHw35SJnNz67IZvbG2Zccy9jgsYrYlVaSxq8nfr1o339HBlQZq8yWIP6bGmON2ba1xhprjTU6UacIcvlV+Rd8bU5FDreuvRVrjTWd3ToT7RlNpEckkR6RreohrrlTbZB4a20CACM6taN/+xYUOZF1GM3WtxCT1itNsnckxoFPIYePbhbLulVaPs+PCWdbUh4p+RUs2nOaWQOCGvw9SmtKOVF0gh7tegCmiUV/B3/25+wn2CmYscFjGRM8Bj+Hxv0ttzQ+7nb859ZInpjWhT+2pvDF8kSOphQp+/cez2Pv8Tx8PeyYPa4Dt48KxdXRMpHszYFmJ3YZDAYWL17Mzp07EQSB/v37c8stt6DR1F2ucbm+69atY8WKFdja2nL33XfTsaNpmUB5eTmvv/46zz//vJp7y9LkJaLZ9i5C/DIETCGZsrUjlGQ26yparY1OQS6892BvHvxwFwAb95/hw9/ieXJ6lzp992Tt4Y+kP9icvtlsBjvaM5rJoZOvKuF8cyezqJLCCr2y7Wqnw9fF1oIWqbR2wlzCCHMJq9N+LnG+rbb2+1duKCfMJYyMsgwKqwsBKNWXcrzwuFJae2TQSKX/0fyjPL3t6VoxzN6vbSTO9+iAcdxcGPI84t6vEQ98i1BVjGbPfOTgQciNLHbNW3qcNbszlO3/zelFZOiV5wiRZRmDbFAEJI2oMYvgSyxMpLSmtFZckmoUMahGqqG7Z3fCXU05GfWSnrkH5yr7zv17vjD1377/VQTZ2NxYnt7+NNUG0369pMco11aK0ok6dt+8W9ledWoVn8Z+etGxdPfsbiZ27cjcwZb0LRftf36SYYDi6uIrFpisRCsEBEVgstJYmf5EK3QaHdai+UOAv6M/o4NGm/WxEq2U153L/3KOG8JuYIDPgDrHPvf/c5HO5/hx1I9m59exgmPMXDvzguNILEwETNGgB3MPmhV5CXIMIsozihd6v6BGfTUyP+xKI62gEp1G4NnRDZfXtCkQcuIVoUvyiUYa9DRy2Eh1pYRKgxLsbs/t/QL5Zkcqn2xOZmKUD+721z6RU2OsYUfmDlanrGZbxjasNFZsuGGDMlFwR+c7mBY+jY6uHVvnfcslsLHSMHNEKLdc357th3OYvzyBdXsykM9mY8/Mq+C1hbG8+9MR+nX2JC23nPSccsL8nHh6RlcmDGgbE6DNTuxavnw5J06c4K233gLg/fffZ8WKFUyePLlefYuKili+fDlvvPEGJ0+e5IcffuCNN94A4Ndff2XMmDGq0GVJ8k+g2faeqcTxOZHLsyPGQU+bcgeoM01Nzs3DQtgdn8v365IBeHvxYXp1dGdIlLdZv6XJS9mQtgEwJdEdHzKeyaGTW20EV2ZRJSM/2kG1obZUsbVWZP2jMargpdLkaEUtvg6+Zm0OOge+H/U9YMqTl1meWSci7PxolPTSdLIqssiqyKoT/g/gbuPO+inrlRvH+Px4citz8Xfwx9fB10xoa5E4eCFd9wJSzKOIhxYhJP9lKnhyjjOxCJUFyCFDkTEJjNWSSTSy1dpio7UBTDfhxwqO1Vnudk44qjHWMLH9ROx0duw8msNrCw8pbxHRrZij9l/z5N+1S++crJx4Z9A7Sp/P4z5nSdISs2PL1NYUGhk4krcHvq1s/9/u/+NowdGLDvvJHk8qYpeIyE8JP13yYyqpKTHbzqusmxz3HHpJb1ZUwUq0QiNolOglM8FItKoTZdijXQ9stbZKH+V1ZwWkf4tdL/d9GaNsvOCxrTRWZvnyfOx92Ddj3xU/CEV7RhPtGX1FfQEiXCOIcI244v7/tsPF2gUr0YoayVygc7F2oZNbJzbdsInD+YeJzY0lLi+Oo/lHqTJWkVqaSo1UYyZ0rUtdx+nS00R6RNLVvSt2OjVhcUMwolM79qcWEuJhT5Bb8/5MhfQ9yLZu4G6aLJG7TkVK3oDU7eazBTraliCg0nQ8OLQ9Sw9lojfKJGSVMiD06iIgJVniYM5BJdF8qb5U2acTdSQVJdHF3TQZf6kI+baCIAgMivRiUKQXp86U8tXKRBZtOElZpSkgobLGyOZDWUr/+JQi7nxrOwueH9gmBK+rErs2btzIypUrycrK4qOPPsLLy4slS5bg7e3NgAEDrsmgbdu2ccstt+Di4gLAhAkT+Pnnny8odl2qb35+Pl5eXri4uNC1a1c+/9xUfjwxMZHs7GxmzZp1WVv0ej0GQ23kSmVlJQDV1dVUV5vWyIqiiE6nQ6/XI0m1D8MajQatVktNTQ2yXHtzqtVq0Wg0ddp1Oh2iKCrHPb9dEARqaszD4K2srJBlGb1eb9ZubW2NJElm7YIgYGVlhdFoNBvPuXaDwYDRWDtD2xRj0i1/CDHT9JBldA9H3/9xxG43ICOgr2nYMen1evR6veqnKxjTK3d05UBiHkdOFSNJMrP+t5Xxtx7hreueUyqXTAmdQlFVEROCJzDIZ5Cy9ANosjHp9Xqqq6ubxE/5ZdVmQheYljTklVbhbnteAmwLnk+t4bt3zqetaUzn01Rj0kpaAm0DCbQNRPQyH9M5m7q4duH/+v8facVpZJRnkFmeSWZ5JrmVucjIuFq7mtn5R9IfLE1eqmy7Wbvha++Lv6MpWX6kayR9vPoo+/P1+RRVFVFYWIhjjSPO1s5423lf8ZhkWUYv60EDlTWVOOtqc8oU1hSSVZVFZU0l1YZqJRLJgAGDbMBeY88wv2FK//Xp6zlWeIxKfaUiGtVINaYIKUnPIO9B3HTD93D2d+eeLfeQmX/MFD21U6TmX8+F/9f3/7je/3rAtMRs1oZL30v08+yHVu/K3f/bgfHs10rnnEtFx0WsTzP3ubuNu9n3plJfedHlbYBp/Of114k6tIJWEYt0okkEstZaYyVaYSfamfUfGTgSjaAxveY8kcnWyhadqMNV46r097X15d2B71JVXoWTvZOZIGVvY48oi9RU1yhCzrTQadza6dZLnk/n2zKjw4zLnk/n9x/gM+Cy51O1sbZ/c75GuGnd+GXUL5Qby0EAg96As7Uzblo3qqurcbF2YaDPQPp69AVM4uuJ4hMcKz6GLMtmn8uK5BX8k/WPyS5BQ5hzGN3cuxHpHklPn554WHmY2S6Kpt+wln7da2w/eTtomHtjZ4ySjCRJ9RoTNM29kXB6F1b/fIA2bTvGjhORpn5bO6ax80xjkmVEQWi1fmqqMZ37f2saU0P4yUqAj6d1I8zLCUcd5r9PVzgmg2RgxvoZpJen135Ogpb+3v0ZGTiS4UHDsRKtzI7TEGM698zf0v3k72HDy7d35YkbI/hlcyrfrE5WUiecQwYE4N2fjzCql1ezH9OlvntWVpePHqy32LVixQq+//57pkyZwk8//aS8uYODA0uXLr0msau8vJyCggICAwOVtsDAQPLz86moqMDOzu6K+3p5eZGXl0dBQQGpqan4+/tjMBj48ccfeeCBB67InpUrV7Js2TJl+5zDvv32W2xsTDO7YWFh9O/fn507d5KUlKT0jYyMJCoqio0bN3LmTG1liH79+tGhQweWL19OcXGx0j58+HB8fX356aefzL4AEyZMwN7enp9//tnMtptvvpny8nJWrFihtGm1WmbMmEFmZiabNm1S2p2dnZk4cSInTpxg165dSruPjw/XX389sbGxxMXFKe2NMSabmgLKRCdlTO5d70Y4k8I/VoNJrOqMvCWTm73zGmVMNjY2+Pv7q366wjH18qvg+Gk/DAYdxaUSv//uhObUywQbApQxPR3yNCtWrOAEJywyJhsbG6qqqhrNT1WSyPEad6K7RxNsU8WFiD92jM1L9lrMT/UdEzTv755Wq6WqqqpVjak5+6l/VH/K95ZTc6YGTzyJIoqefXri6OfIus3rmD9/vtK/OKIYO40dFcYKAAqqCyioLuBIwREAOlV04kD5AQDKxDKWuC8xi0BCBl+9L36+fpRVlhF2Ogw3oynyRu+iZ4PjBkW8MgpGJMFcXL4t9zZETA/luQG5rK5azcXwwovE3ERlOz4knr1ley/avyy1jPyNtYJSrn8OBRjhIkld121YR1K16bPU6/TgYroRF4wCIiIa2RTN5OLoglQj8cOPi1i2z5/sQtMNmVZnZOj1ycj6YDR6DaIs4uHqQfuA9uSm55p97l6dvHg5+mXi4+IpLihGlEU0aIjuFk1YcBg7/trB/Nja/o8Newz/Xv6X/O7Np7b/4zc/fsHv3k0zbiIzM5Ml3y9R2s999+LT4tm/vjYa8Pzv3tK4WkG0rZ1PDTqmX+o3pvO/M3o3PeGu4SSVJGGUjSQUJZBQlMDvyb8DMM5xHB4na3OrhYSGENk1UvVTI45p3LhxjTqmXh5V1Gx4De/KE0p7WdYpqnKzWL5qreqnRhiTn58fw4YNa1Vjakg/CS4dWPzblY2p/8j+FAvFxK+LV9oEJwGsoZNjJ5wznQmqDsImx4aU+BQqZlSQlJnU4GOysbEhPDy81fnpnRndmPlJhVK98RwycCK9hMWLF7e4MZ3/3Zs2bRqXQ6ipqZEv2+s8Zs+ezaxZs4iJiWHSpEnMnz8fHx8fTp06xdNPP83vv/9en8OZkZ+fzxNPPMGnn36Ko6OpMlVJSQkPP/wwH374IW5ubvXqu2fPHlatWoWtrS0zZ87kwAHTzXjPnj35+eef0ev1jBgxgl69el3Qnn9HdpWUlBAQEEBGRoayBLKtq/iXHVNxKmx9F238H1RO/x3Jv49pTEBNdRWItbnYGmtMRUVFuLm5qX66xJj0sp51J9exImUF+3P3U5EeRu62KUqfaSNdmXvvdc1mTEVFRbi4uDS4n46fKWLRngxWHM6mUi/haKNl/i1RzPy27lKvNyZ2YlJkuyb1U2v87p0b0zmftqYxnU9LH5MgCOSW5pJZkalEg2VVZpFZlsnIgJGMDRoLQHxBPHdvvptL8fGgj+ndrjcAScVJ3Lbxtkv2/2vSX8rSyWWnlvG/A/8z5VI6lyPpvPxIwY7BvN73deW1S04tITYvFq2gVSKddKIOW50t1lprOjh2oJ93P6X/ntw96GU9msJ0bBPXYpu2HWujAStZRqezx7nzdHT9HwNrJ2RZxsraCmQu6qc3f4hl7h8Jyr5vnolhfH/fFvvdy8vLU+65zm9Xz6fmNaay6jIO5x4mLj+Ow/mHOZJ/hBJ9CZ8M/oRenqZ73oKqAm5YewMRThFEe0XT1bUrke6ROFs7N8sxNbWftFotTy85Sv9gF8Z3a4d4NmqxvmMqLy/HxcWlYccky2jStmO1ay6a07UPjUb//tQMeBwhZDCaC4y1NfrJEmMqLi6mXbt2rWpMjeGn3NJqskur6eLjaDamkpoSNmdsZl3aOg7mHcTLzoslo5coy+BPlZzCydYJbzvvJhtTUVER7u7urdJPwx7fQHxqEeeZjiBAl2AXNrw3vEWOqVEju7KysggLq5s018rKSpmVv1rORUtVVFQoN1Pnlg6e21efvn369KFPnz6K3fv37+ell17izTffZNq0aQQEBPDiiy/SqVMn7O3t69ij0+nQ6WpzIZxzmLW1NdbW1nX6XoiLOeFi7f8+7qXaBUG4YLsoihds12g0F0z0r9Vq0WrrfhWuaUyFKWh2fIgQ9zPC2SS2NvG/YgwdpHSxtq2b96AxxnS+H1U/Xbj9+b+fN0sOHNGxmi6CgS1/m97v1/WFjO+Vy7j+/s1iTDqdzux9rsVPkiSzLSmfBf+ksi2pNsLD2VbL9F7+ONtaYa0V6yxlfGXVcRxtdYzpap7TrDH9dKVjOoel/XSl7dbW1nV8eq7937SkMV1pe0sZUzundrRzakc00Rfcf86GCzE+eDw+Dj7oRB3BrsHKuAJdAnk75m1lWZyZeHU2ObiTnZNyE3xTxE1Mi5h2xfmXZnScwQxmXFFfgEH+Z3+jAoDI26EkE3HvF4gHFiJUFCEf/RXDdf8Bq/P8IlzYr5sPZpsJXfdOCGfyIFM0ekv97l2sXT2fmteYHKwd6O/fn/7+/QFTDpyUkhR87H2w1pr6H885TrWxmrjCOOIKa2fTg52CifSIJMojiontJ6IR6342bcFPa49msyIuixVxWYT7OBPl72y2/0rHVF5e3vBjqixCu+xuBL1peZIUPAhp4FPIQTFmD3ZtwU/naMoxnRtHaxrTORpqTH8nF/HUH4dxt7di9cMx6GU929O2szplNdszt6OXakUQWZYpMBQoVRQ7enZU9jXVmHQ6ndKntfnp6RldufOt7QgCyDLKv0/P6NpixwQX90ed415Rr/Pw9vYmOTkZLy8vs/a9e/eaLSm8Guzt7XFzcyMtLU05flpaGm5ubmZLGOvbF2DhwoXceuutaLVa0tLSCA0NRafT4erqSlZWFqGhoddku8pZitLQbH8f4fAvCGcr9cmuIaYSx12nXubFKk1Bub6cvzP+ZnTQaOWBcVTgKP7J/IfhgcOZEjqFnu16YhgrMznvL3bF5wLw0Ee76Bw8ihAfx0sdvkVRrTcy+fNdJOXWrmcP9bTnjv6BTI7yxdbKdDFe/2iMUo0xp7SaN1YfJ7Wgkkd/jSOzuIq7BgRd8cO3ikprRqu58G3FjI4z6OTWqU67o5WjWaXIyyE2dfESJ1+k4a8ixTyJeOh70FiBVe3kmHhgAbJ7GHJgjFni59M55dz3wU5lu2eEO6/Oim5Ky1VUFERBrFNEppdXL+YOmcuutF0klidyNP8o1cZqUkpSSClJYeeZnUwOnaz0j82NRS/p6eLepeUXqbgM1Xoj/1trEqpHd/GqI3Q1ObIMRam1VcptXZB6zkLIPoI06CnkgH6XfLmKSlMT7uVAjUEiraCSr/45ym/ZD5klmnfQOTA8YDhjQ8bSw7PHBUV1lYZhwoAAFjw/kHd/PkJSeglh/k48M6Mr4/u3/uT0cBVi19SpU/n000+VUL7jx4+zefNmfv75Zx5//PFrNmjQoEGsWLGCDh06AKYcYUOGDLmmvtu3b6ddu3aEh5uqEHl6enL06FGCgoLIzs7Gw8OjzmtU6o+QvAnNrzP/JXI9idz1RhCbXeHPNoUsyxzOP8yy5GWsS11HpaESH3sfpeLUdQHX0c+nH87WtTd0Oq3A188M4LrH1pJbVE1JuZ5Zb29nzTsjsLVuuf6sNkhYa00PzNY6DcHudiTlljMk3IM7+gcyMNS9jnDl62KrVF7sAvQMdOHBnw6x61Qhb69NJLukmv+MufJqXCoqrZVLVZZr0dg4IfV7yLytPA9xw4sIhiokn2ikfg8hdxxPtVHgrv/toLDU9Bm4Olrx7TMxWOnUm3mV5oOjlSOD/QbT1bYrbm5u6I16EgoTiM0zVX10tXY1+y1ceGwhW9K3oBE0hLuGE+URRZRnFFEeUXjbe1/inVoeC3amkV5UhZVW5NlR4ZYzRJYRktYjbnsPoSAZw0OHwMaURkW67r9mqUBUVJoDsiyTWJRIqb6UWQOC+HJbCl//nUPXnsGcKD7OQN+BjA0ey0C/gVhrLhw9pNLwTBgQ0CYqL16Iej+xjhkzBisrKxYsWEB1dTVvv/027u7u3H///Vx33XXXbNDEiRMpKyvj+eefB2DAgAFMmDABgAULFgBw5513XrbvOUpLS1m9ejUvvPCC0nb77bfzzTffUFVVxeTJk3F2tvCMTStBDugLNs7IVg5nRa6bQHPh0EOVpqGouohVp1axLHkZycXJSrud1o600jRF7DqX7+bf+Ljb8cVTA7jxpS1Ikszhk0X856sDfPhQnzp9mzOyLHMgrYgFO9M4nlXK2kdi0Iimm/gnR3Tg6ZHhtPesu5T5YjjZ6vjm9p68sOwoy2LP0MXXqbFMV1FpUfjY+7B0wlKKqosoKS7BydkJF2sXfOx9LG1aw1NVhOzfGyFlG+KZQ4hLZyO7BPFc/iMcSDRdTwUBvniyP/7trvz6oqJiCXQaHV09utLVoyszmVlnf42xBo2gwSgbOVZwjGMFx/g50ZSs2MvOi8eiH2NU8KimNrvBySmt5vOtJwG4e0AQ/q4WiGKTZYTENWi2v4eQVbvEVEhab5pABlXoUmlWZJZlsiZ1DWtS1nCy+CThLuF8NexHlhzMJK+sBrfKmWy4oR9OVur9skrTUu8E9edTVVVFZWUlrq6uDWlTs6WkpAQPDw/y8vKUBPVtlpJMxB0fgpU90vBXattzjoF7WLMSuQoKCsyKG7QV5sXO4/tj35uti+/m3o0pYVMYGTgSO13d5b4X4/1fjvLmj7U3XPMe78fNw0Ia1N76cKU+rTFIrDmSxcJdaRzOKFHa58+MZnjHdpd45ZUhyzJ7UwvpE9z2vl8NTVs9T1szbcanZw6h2TUP4difLMmNZlbSLGXXU9O78PytkRY0rmFpMz5tQ9THpxX6Co4WHCU21xT9FZsbqyxN+njIxwz0GwhAcXUxT217ypT7yzOKSI/IFhPd+dzSI/xxIBNPByvWPzYQh2uMZK/XOSNLCAmr0Wx7DyHniNIsdRiNcdBT4BN9TbaoNAzqddBEUXURG9M2sjplNYdyD5nt6+DSga+v/5q1h4v5z7J4BAGW3tev2U4Oqz5tvVzTFdzGxqZO4niVVk7JGcR/5iIe+gHBWIOssUbqPQeczs7at6ubk0WlaSisKsTVplZ4drdxRy/pcbZyZmzIWKaETiHMpW5xiSvh8Zs6s+dYLhv3m0rxPjVvL5HtXekc7NIQpjc4+WXV/LQ3ncV7TpNbVrucqleQC3f2D2JIh4ZZuiwIgpnQJUkyb6xJYEZvf8LaOTTIe6ioqDRzfKIxTvmKxIgnefjFfUrzEN8inpnR1YKGqag0LHY6O3p79aa3l6ma6rnE97G5sUR5Rin94vLi2J+zn/05tZWMg52ClaWPkR6RhDiFNLtcl0cySlhyMBMwRX1fq9BVX8T1L6DZ95WyLUWMwzjwSfBuPYK5SuvgdOlppq6aikGqrarnbefN6ODRjAkeQwcXU4qhG7o78OPu08SfKeWNNQksuqtXszvvVVo39b6K33///Rf9klpZWeHr68vIkSOJjo6+VttUmhOlZxD/+Qjx4PcIRpN4IDv5IcU8DvbuFjau7WKQDOzI3MHS5KXsyNzBb+N+I9gpGICxIWNxtXZlaMDQa14XL4oCnz/Rn+seW0t6bgWVNUbufHs7mz4YhaNd84niO8dDP8eyL7UIAJ1GYHw3b+7oH9ToM0pzNyXx/a40lh3K5LNboukbos4Sqai0BcqrDMyad5IyvWlpkY+DkS+ejkGjOZtQ31CNZs1TSNG3mpb8q6i0As4lvv938ns/Bz9mdZ7FodxDxBfEmyW+//Pkn7jZuLFhygalf05FDo5WjhZPfC/JMhHtHNBpRaZE+zbBGxpNa53PFt6Qu92EvO9r5I4TTCKXV5fGt0FF5TIYJSP7cvahE3X0aNcDAH8Hf/zs/SioKuD6wOsZGzKW7p7d6xSR0YgCL47tyC3f7OVAWhHHskrp7NM8o7tUWif1Xsb4zTffsHLlSkJCQoiIMCVkTkhI4NSpU4wcOZLU1FQOHTrESy+9xIABAxrFaEvRJpcxyjLippcQ932LYKw2NTn6IsU8hhQ1E7TNP7lgawxNTS9LZ1nyMpafXE5eZZ7Sfk/Xe7g/8v5Ge999CXmMf24TeoMEwOSBgXz9zIAmn6U536dGSWZvSiH92tf6+M/YM7y1JoFb+vgzo3cAno5N8z1NyCpl9g8HyCqpRqcReHtKVyZGtcJcRY1AazxP2zptxaeyLHP/B7v4bUsKYLq5X/7mcPp18VT6CLGL0a58BADJr7cpmX3EGOUht6XQVnzalmhsn56f+D42N5ZDeYfo5t6N9we/r/R5ZtszbEnfoiS+j/SMtFjie6Mkk19eQ7sGum+44OcrGRHil6DZ/gHGof9B7nhevuGiNHC5tur2Ko1LW7gOyrLM8cLjrD61mnVp68irzKOPVx/mD5+v9EktScXH3ueCOX//zRd/n2JYhCcdvJrnqoe24NO2Sr0ju0pKSpg6dSq33nqrWfuiRYvIycnh7bff5vvvv2fRokWtTuxqkwgCQkUBgrEa2cEbKeZxpOhbW4TI1RrZlrGNRccXsSd7j9ImCiIDfQcyJXQKMb4xjfr+vSI8+L+7u/PcF6alCcu2p9G3syf3Tmj6akWlVQZ+P5DB97vSSC+s5Pc5fZXy4GO7ejG6i5dSdbGpiPB25Lc5fbnnh4Mczyrlyd8Pk1lUyZzBzW+5hoqKSsPw/bpkRegCePnOKDOhCwDnACT/vojpuxEz9iL+cQeyW3ukvg8gdZsOOstGtKioNBZmie87zkSWZaqMVWZ9juQfwSAbiC+IJ74gnp8SfwJMy6IiPSKZHj6d7u26N4m9GlFoMKGrDpIB4cgfaHZ8gFBgKhqk2f4BhojxpggvUIUuFYuSXpbO2pS1rE5ZTUpJitIuICAIAnpJj040regIcgq64uPOGWy5PL8qbZt6i11bt25l3rx5ddqHDh3Kgw8+yOOPP87QoUP5448/GsRAlSamLAfxwAKkmMfgrFJvHPgksk80UvfbQKvmaLMkR/KPKEKXn70fk8MmMyFkAu3srj3Z+pUye1wHdsfnsnRbGgAvfXuQHuFu9IpomDxYlyM1v4Ivt6SxKj6f8hojYLpHPJBWpIhdOo3loiW8nWz4aXZvHvk5lm1J+by/MYn0okpeGd8JrQXtUlFRaXhikwp4/svavERj+/nzwOSOdfrJwYMwBg9CSt+LuGseQsIqhIKTaNY8hbj1bYwj/s9UwVhFpZUjCEKd5Yq/j/udw/mHicuNIzbPlPy+TF9GVkUWWWlZjAqqrfJYpi9jQfwCUwSYRyTO1tdeUX3JwQwC3ezoFdRIBbeMeoQjv5tErsJTAMiCiNz1Rowxj9cKXSoqFiQ2N5ZZG2aZtUW4RjAmeAyjgkbhZefVIO9TrTdSbZBwsm1+aVBUWh/1FrusrKyIj4/Hz8/PrD0+Ph4rK5M4Isuy8n+VFkJ5LuKuT03LFQ2VyA5eyD3uMO1za4/k1v7Sr1dpUCr0FaxPW8/yk8t5d+C7uNua8qJNCp1EakkqU8Km0Nurd5218U2BIAh8+FAfDp8sJCmjFL1B4q63d7Dlo9G4OTVexN/+1EK+3JbC5sRc5LOLrx2stUzr6cet/QIIcL3y6pKNjYO1li9u7c4rK47x6/4MftmXgY1Ow4tj6z4Eq6iotEyKymqY9fZ2qvWmZd0hPg58+mjfS0Zxyv69Md64AAqSEXd/jhj3M0JFHti4NI3RKirNEDudHX29+9LX25TPTpIlThWf4lDeIeJy48yS3x/OO8y3R79VtkOcQpSk91EeUQQ7BdcrkjqrpIpXVhyjUi/x0bRIxnZr2KWTmuI0tD/NRihKAUAWNMjdpplELvXeWsVCVBoq2Zq+FT8HP7p5dAOgi3sX3GzcsNZYMyZ4DGODx9bJx3etbEnM5dUVx+gT4sb/blALuKg0PvUWuyZNmsTHH3/MiRMnCA83LV1KTExkzZo1zJgxA4B9+/bRvr16AW+2FKdDRb7p/1XFiMeXIx7+FUFfAYBs305dpmgBZFkmviCeJUlLWJe6jgqDyR8rT63kjs4m4dHX3pe3B75tSTMBcLTTseD5gYx4Yj2VNUYy8iq47/2d/PzyEESxcWYoD5wu4q+EXAD8XayZFRPCDd19m7xa0pWi04i8Pqkz/q62LNpzmrtjgi1tkoqKSgMhyzIPzt1FanY5ANY6ke+eG4izwxVO9LmFIo15D2nws4hH/0AOHa7sElK2mSKs+z2I7NujMcxXUWnWiIJIqEsooS6hTA2bWmdfd8/uSuL7UyWnOFVyimXJywAIcAhg2YRliuAly/Ilxa/31p+gUi/h5WTN0EaIUDc6+oIgIIta5G7TMcY8Bq7qki6VpscgGdibvZfVp1azOX0zFYYKRgaOVJ4rtKKW70d+j7e9d6NNpheU15BeVEX6wUxu7RtAN79rj8xUUbkU9X5KnDlzJt7e3ixfvpxNmzYB4O/vz+OPP86wYcMAGD9+PBMmTLjUYVQsRXE62s/7Ksnmz0e290Tq/zBSjztB13yiZFo7JTUlrD61mqXJSzlRdEJpt9HYMCpoFH28+1jQuovTKciF9x/szQMf7gJg04EzfPDbUZ6afu0zNdklVSzac5rpvfzxczEtd5jW0589pwqZ0SeASA8RD/fmXwVUEATuH9KeW/sG4mhTe7k1GCV1SaOKSgvm06XHWbs7Q9n+35xedGt/FUug7D2R+txn1iTu/BTx5CbEY38iBQ4wJbMPu77FJbNXUWkMzkWA6Y16jhceNy17zI3jUN4h8irzCHIKMhO3Xt71MieLTxLlGUWURxRRnlHKcqxDp4v4M/YMAE+PDMfO6honzwzViLGLoCAZacQbpjZRi3HCp8iO3uBy5TmOVFQagnMT6atTVrM+dT35VfnKPmuNNdZaazNB2NehcauQTo7yZdHu08RllPDG6gR+mt1bzWmr0qhc1VV9+PDhDB8+/KL7ra3VqKBmS0X+BYUuY+97kYa+AFb2FjCqbTMvdh6/nfhN2e7i1oXJYZMZFTQKB13zrFpyjunDQtgdn8vCdaZEq28vOkzvjh4Mibq6ZQBx6cUs2JnKmiPZGCQZvVHm2VGmCFJnWx1f3WaKcigoKGiYATQR5wtd6YWV3LVwPy9P6ERMaPMX7FRUVMz550gO/7cwVtm+eVgIt45suGh2qfutUFWImHkAMe0fxLR/kD0iMPZ7ELnLVDXyWkUFU+L7bh7dTEuwOpoe6s+Un6HSUGnWb3/Ofs6UnzElvk+oTXwf5RHF/hPOCLpgIr1CmHAtyxcNVYgHf0Tc+RFC6RlkBKTo28HTVLVeDuh79cdWUbkG/jr9F09vf1rZFgWR3l69GRM8hmEBw5r8OUMUBV4Y25HpX+1hf1oRqw5nMT5SrVqu0nhc9RSGXq+nqKgI+VzynLO0a9d0ibJVGg6p23RV6GoC8irzWHlqJdPDpysJWieFTmJtylrGhYxjcuhkwl2bvrLhtfDmvT05eKKAuJOFyDLMee8f/po7Gl/3K4sONBgl1sfnsGBnKgdPFyvtXo7W+Di3voIIr6w8xqn8CmZ/f4DXJ3Vmag+/y79IRUWlWZBTWMnsd//BKJnufToHO/Pu/b0adGZa7jgBY8R4pNM7TVFeSesR8hLQrnwEecubGKYvBu/IBns/FZXWgCAIdaJSZFnmpb4vXTTxPdagsbmFF8eORhQFKg2V7M/eTzePbleW+F5fiXjwe8SdHyOUZZveU2NtKuhkoy7PUmlaCqoKWJ+6nh7teijPEv19+mOjsSHYKZixwWMZFTQKTzvPyxypcekR6ML4bt6sPJzFO+tPcH2ndtjoNBa1SaX1Um+xKyMjg/fff5/4+Hiz9nMhkGvXrm0w41RUWgNGycjOMztZmryUvzP+xigbcbV2ZVLoJAA6uXZi/Q3rsda0zNl6GysN3z0/kOseW0tJuZ7comrueecflr0xDJ320stu/knO57mlRzlTXFuGPMrfmTv7BzKqi5dFqyo2Fm9N7sKcRQc5nFHCc0uPklFUycPXhaph3CoqzRyjUeKe9/4hu8AUOeJgq+W75wZiZ9MIeQMFATlwAMbAARhzE9Ds/gzhyG8g6cE9rLafLKuV3FRULoIgCHUS358sPsnerIN8sG0jet1Jrg/pTXSACwBH84/yyNZHAGjv3F6p+BjlGUWQo/nySHH/t4jb3kMozwFA1togdb8Dqf9D4KhGqqg0DRX6Crakb2F1ymp2Z+3GKBu5qcNNPN/7ecBU/GH5xOV42DZNxfQr5emRHdh4PIczxVV8vT2Fh64LtbRJKq2Uet+hvfvuu2g0Gl577TXcW0DOHJV/YeeOrLE2W8ooa6zBTvVlQ5NZlsmfJ/9k+cnlZFdkK+3uNu4YZIOyLQhCixW6zhHs7cC8x/px2xvbANgVn8vr38fy6l3d6/Q9PzeAn6stWSVVaEWBUV28uKN/IN3P3nS2Vjwdrfnxrl48/uth/krI5ZPNJ0kvquL1iZ2xuow4qKKiYjneXnyE7XE5yvbHj/QlzM+p8d/YMwLj+I9gyPMIeYlmOTXF9c8jVBVj7PcgeKmVrVRULoUoiIS5hFFV3g5NvifVNQZeeKx2iWFmeSZWohU1Ug0ni09ysvgkS5OXAuBi7UJ/7/68EXM2F1dRGkJ5DrLWFqnHHUj9HgLHhq3kqKJyIfSSnt1ndrMmZQ2b0zdTZaydMHaxdsHZyjyqsLkJXQC+LrbMjgnm0y0n+XLbKWb0CcDd/goLvKio1IN6i13JycnMmzePwMDAxrBHpbFx9sdw/+7aaoxgErqc/S1nUyvk6yNf83nc58iYlrqIgsgAnwFMDp3MIL9B6ESdhS1seMb28+fhGzryyZLjgCmBc+9OHozvH4AkyWxLymPhzjSGhHtwR39TktYgNzv+N6Ur/dq7tcolixfDzkrLZ7dE8/rq4/y4+zRLD2aSXVzFpzOicLRpfd8NFZWWzvq9GXzw61Fl+94J4Uwa2MT3QY7epiTX5yjPRTz0I4KhCvHIb0ghQ03J7EOGqNFeKiqXoKufExseG8jhjGK8nWrvPSa2n8jooNGmxPe5scTmxRKbG0t+VT5F1UUUVuYqfaV+D/JVeSKF7ToR5duPKI2ImshFpSn448QfvLP/HWXbRmPDUP+hjAkeQz+ffi3mGeOeQcEcSCtiVkyQKnSpNBr1FruCgoIoKSlpDFtUmgpnf1XcamBOFZ8iwDEArWg6pTq5dUJGxtvOm8mhk5nYfiLe9q1/xu+F26LYl5DPzqOmG8KH5u4iqbiSNQk5nMyrACAlv4Jb+waiEU0PY1O6N27ll+aKRhR4aVxH/F1teXttIv+cLOD3A5nMGqBWa1JRaU6czinn/g92Kds9I9x5dVa05Qw6h40LxnFz0eyah5B9GPHUFsRTW5C9umHs9wByp8mgaRkPPSoqTY2jjZYBFygSY6WxItIjkkiPSG6rnoyw50vOHPiCQ3IVDp7nJfO292Q55Zw+uZTFJ03RX9523kR7RitLHzu4dGiq4ai0UlJLUlmdsppxIeMIdDRNsAwPHM6HBz+kl1cvxgSP4Tr/67DXtby8y3ZWWhbO6mVpM1RaOUJNTY18+W61HDx4kIULFzJr1ixCQkLQaMwTytnbt7yT7UopKSnBw8ODvLw8nJyaYOmCSoNQUFCAm5tbgx+30lDJxrSNLE1eyqHcQ3w05CMG+Q0CTHm69uXso1e7XmjEtpV08Ux+BYMfWUtBiWmprMZWg1MHZwRRYOjZqK6YULdrylHVWD61FGuOZLH1RB5vTuqCKLbNiIzW5lOV1uHTar2Rcc9u5OAJUwVYN0crNs8djX+7ZnSvI8sIKX8j7voU8eTm2mbXEAxzdoCm4WbMW4NPVcxpSz49kV2GrZUGf1fbS3esKkbc+yXinvkIVabCObKVA9LAJ5D6m3J6SbLEN0e+4VDeIQ7nHaZMX1bnMG8MeIO+Tn1xc3NDL+mpNFTiZKU+P7R0GvucyavMY33qetakrOFogSmi+J6u93B/5P1Kn9KaUhytHBvNBktwruidJXLYtqXrYFuj3pFdzz33HADPPvusWbuaoF6lrXCs4BhLk5ayNnWt2c3N9sztitilETVKQtS2RqUkY2hnDWfFLmOlEb8agQXPxhDi0YweEJsRY7p6M6areeRfakEFQW5XVtFSRUWlcXjp24OK0CUIMP/J/s1L6AJTMvuQIRhDhmDMPmKK9IpfihzQz1zoMtY0qPClotKSkCSZZ5ceISG7jFfGd+KmnheohFxZhLhnPuLeLxGqTatYZGtHpN73IvW5D2xdla6iIHJPt3sA0wTnyZKTxOaaKj7G5sZyuuw0UZ5RcDZF7pG8I9y98W4l8X2UZxRRHlEEOgaqBWpUKNeXs/n0ZlanrGZP9h4kWVL2BTgE1Mm71dqEriMZJbyx5jg39/JnUnTbXPGh0jhcVYJ6FZW2yNb0rXxx+AuOFx5X2mw0NowIHMHk0MlEe0ZbzjgLUm2QyCurxs/FNFMa4mFHdLg7CUbIPmW6WTxyPJ9dsdmEDG9vSVNbDL8fyODFP+N5dUInpvdSlxyrqFiCJX+n8vXKE8r2k9O6MLxnM78J9+qKcdLncN2LpkqN5zBUo53fDzlokCmZvWeE5WxUUbEAy2IzOZxhuifp6O1wwT5C4Uk0298DQLZxRuo9B6nPHLBxvmD/c2hEDR1cOtDBpQM3drgRgIKqAlytXSmsLgQgNi8W4IKJ7yM9Iunj1YdbOt5y7QNVaZEsjF/I10e/VrZdrV0ZFTSKscFj6eLepdULot/vSmVfahGnCysZ0bkddlaNUOVYpU1S729SZGRkY9ihotLs+Hc4bam+VBG6Orp2ZErYFEYHjW51sytXSn5ZNYv3pvPTntMEutnx8z19ANPn9dkt0bjb6bjtjW1s3H8GgKc/20dUqBudg10saHXzR5JklhzMxCjJvPhnPOmFlTw+PKzNLm9UUbEECaeLeeyTPcr24CgvnpnRgqodOplHrQjxSxGKTyPELUaMW4wUNsKUzD5wgJrMXqXVU15t4P0NSQDc0N2Xbn5nxauKfCjLhnadAZB9eyB1vRHZLQyp971gc/VLDt1szJdE3RJxC909u5siv/6V+P7vjL+p0FeYiV3rUtehETREeUThaed51XaoNC9kWSYuL47VKauZ022O8j0ZHTyaRQmLGOo/lLHBY+nr3VfJA9wWeGJEB9YezSa7pJovt6Xw2PAwS5uk0kqod86uc1RVVZGTk4PBYDBrb9++9UZuqDm7Wib1XYddUFXAylMrWZa8jNf6vUZXD9MDTpWhik8OfcL49uPp5Napscxt9sSfKWHhzjRWxJ1BbzRdPnQagQ2PDVSiu85RUFLNdY+tJT3XlJw+1M+RjR+Mwsnu2pImt/a19VV6I0/9fph18TkAjO/mzds3dMVaK1rYssajtfu0LdJSfVpeZWDEE+tIOG2KAvF2s2XLR6PxdGnBFWONNQhH/kCzex5Cbm10suTTHan/Q8gR4+EK8ku2VJ+qXJy24NMPNp7g862nsLfSsP6xgbQTSxF3z0Pc9y24BmOYvRmExvl9vdjnK8syGeUZytLHUOdQpoVPU/ZPXTmVUyWnAPCx9yHaI5pIz0iiPKIIcwlrU0JIc+NqzplTxadYk7KGNSlryCjPAOCZns9wc8TNSp8qQxU22hb8O3ONfLo5mY/+SsZaK7L+0Rh8XS6TW68BaQvXwbZKvcWuoqIi3n//ffbu3XvB/a05Z5cqdrVMruQCZpSM7M7azbLkZWzJ2IJBMom4U0Kn8N++/20KM5s1Rknmr+O5LNiZyp6UQqXd3d6KW/r4M6N3AJ6O1hd87f7EfMY9uxG9wZR/YPLAQL5+ZoCaoP4ySJLM/9Yl8u0/qQD0DnblsxnRuFyjUNhcaQs+bWu0RJ/Kssz9H+zity0pgKlq6oq3htO3cyuJrJBlhOSNpmT2qTuUZqnrTablj5ehJfpU5dK0dp+mF1Yy6uMd1BgkXhzsyixhBeKBBQh60yScbOeO4Y414NY4k/VX8/nqJT2PbX2MuNw4yg3ldfbbae3438D/EeMb01BmqtSDK/VpbmUu61LWsTpltVkKFICu7l25o9MdDA8c3lhmtjiq9EZGfbSDzOIqxnXzZu60pltN1tqvg22Zek8LfP7555SVlfHxxx/z1FNP8fLLL1NYWMjixYuZM2dOY9iootJoZFdk82fynyxLXkZWRZbS7mrtyvj245ncfrLljGtGyLLM/60+zpniKgA6eTty54BAxnXzuWy0Uc9wd16f3Z1n5+8HYNn2NPp29uTeCeGNbndLRhQFnh8TgZ+rLa+vPs7elEKmf7WHr2/vToCrmrheRaUx+H5dsiJ0AbwyK7r1CF1gSmYfNgJj2AikzIOIu+chHFuO1GVqbR9ZhspCsFNv/FVaPu+sS8TZkM9T9muZdmAjgqESANneE6nfg0g97gSrC+fwshQ6Uce86+ZhlIwkFycrSe9j82JJL0unwlBBgEOA0v9o/lFe3fWqEvkV5RFFgGNAq8/z1Nz58vCX/JH0h7Id5BjEmOAxjA4eTaBjoAUta57Y6DQ8Myqcx36NY9XhLG7rG0DPINfLv1BF5RLUW+yKjY3llVdeITw8HFEU8fLyomfPntjb2/Pzzz/Tt2/brECn0jLZkLqB+YfnAyAg0M+nHzeE3sBgv8HoNK0zguZKSM2vYG9qITf2MOV90WpEbu8XyIG0Iu4cEEjvINd63UTdPbYDu+NzWfJ3GmCqcNa9gxu9O3pc5pUqt/cLxNfZhsd/iyOzuJL8shpV7FJRaQQOJRXw3Bf7le1x/f25f1LrTeQu+3bHOOVrGHYanGoLYQgpf6P5dSZS5M1Ife8Ht1ALWqmicvWcKa5iU0IOf1q9TSfjaQBk+3ZI/R9G6nEH6Jr3b6lG1BDuGk64a7iS+D6/Mp8j+UcIcKwVuw7mHiSpOImk4iSWJC0BTInvz1V97O7Z3VQZUqVR0Bv17Dizgw1pG3ixz4vYak3L78YEj2FL+hYl0Xwnt06qAHkZxnb14oddLuxPK+L11Qn8MaevmrNW5Zqot9hVVVWFi4sLAA4ODhQXF+Pv709wcDBJSUkNbZ+KSoORWpLKsuRl3BB2g3KTMC5kHL+d+I0xwWOYGDoRX/tmXmmrEZFlmZ0nC1i4M43NibmIgkBMqDs+zqb8AXfHBDF7YPBVHVsQBD54sA9xyYUkZZSiN0jc/b8dbJ47GnfnCy9/VKnl+k7t+PGu3uSVVRMd4GJpc1RUWh1FZTXc9fZ2as4utw7xceCTR/q2jQcT5wCzTfHg9wiGKjQHFiAeWIgcMQ6p34PI/r0tZKCKSj2pLAJbF3ycbVj7SAyJ6+6kY/ZXSAMeQYq+DXRNlwuooXG3dWeI/xCztgE+A5C6S0r0V0FVAUXVRWzN2MrWjK1Ee0bz7Yhvlf7HC47jbuuOp20rilptYiRZ4lDuIdakrGFj2kaKa4oBGOQ7iNHBowHo7tmdNZPXqPnV6oEgCLwwNoK7vz/ADd19uarE4ioq51Hvs8/f35/09HS8vb1p3749q1atwsvLi1WrVqlrXVWaHVWGKjZmbmTDgQ3szzHN2IuCyMPRDwPgauPKsgnL2sYDzUWo0htZHnuGhTvTSMwpU9oD3Gw5U1yliF3X+hk52ulY+PwgRjy5jopqIxl5Fdz3wT/88vJQddbmCojyNy99Xl5tYNPxXCZG+VjIIhWV1oEkyTz44S5Ss025cax1It89NxBnBysLW2YZjBPnIQUPRrP7M4SCZISElYgJK5H8+2Ld9Q5wvbHRknmrqFwTxemI/8xFjPsZw12bwDOCAFc7AqY9iEGaA600+Xd75/a0d24PnWoT3x/KPURcrqnyY7RntFn/l3e9zImiE/ja+xLlEUWkZyTRntGEOoeqwsxlOFl8kj8S/2BL9hbOlJ9R2rWClv4+/c0qZwqCgFZQP8/60s3Pmb+fGoyN7vJFU1RULke9z8DJkydTUFAAwK233soLL7zAX3/9hVar5amnnmpwA1VUrobEwkSWJC1hdcpqyvS1Ak6QYxB+Dv8qyd6Gha4v/j7FNztSKKzQK20xoe7c0T+QIR08GlyE6hjkzHsP9OaBD3cB8NeBLD747ShPTe/aoO/T2jEYJR79NY6tiXkczSzh2VHhqmCoonKVfLr0GGv3ZCjb79zXi27t23CeEK0Nco87MHS/DSFxLeKueYjpuxHTd+OSvhuDTkaOnG5pK1VUailKQ7PjQ4S4nxEk0/2Mcc9XaMa9Z9ovak1/bQBBEPB38MffwZ/xIeMBkwB2jmpjtSLSZJZnklmeyZrUNYAp8X1X96480v0ROrt1bnrjWwCfx33OptOblO0ojyjGBI9hROAIXG3a8O9GA6MKXSoNRb2v/Ndff73y//DwcH744QdOnz5Nu3btcHZ2vsQrVVSahsXHF/PegfeUbSvRihGBI5gcNpkenj3atLj1b3JKqyms0GOtFZkc7csd/QLp4NW4iVqnDwthz7E8Fqw1LXt+e9FhekV4MDTau1HftzVhlMHB2nT5/vafVDKKKnnvxm7qzYGKSj3550gOr38fp2zPGB7CzBGNU5WtxSGIyBFjMUaMRUrfi7jrU+S03cidJtb2KcsGjRXYqg95KhagMMUkch3+BeFsFW3ZOYA1LjN47XB3nvY/o0Y/Yz6pa62xZvPUzSQXJxObF0tsbixxeXFK4vs92Xuw0dRGwJ0sPsnihMVEe0QT6RlJgEPbSHxfWlPKptOb2JG5g7dj3kYjmu6vxgSP4UTBCcaHjmd08Gj8HfwvcySVayG7pIqP/krm/iEhar5alatCqKmpUZfDXiElJSV4eHiQl5eHk5OTpc1RwTRbFZcXR7BTMM7WJrE1rTSNySsmE+4SzpSwKfRz7keQV5CFLbUseqPE+vgcftufwSc3R+FoYxJKUgsqWHskm2m9/HC1a7olO1U1RsY+s4HY5EIAPJyt2fzRaHzdr+yHTC0RbFp69cHGJL7YdgqA7gHOfD6zO+72LXPplerT1kdz92l2YSXXPbqW7EJTldnOwc6se3ckdjZtIwLkaijMSsPVu7aKmGbV4whH/0CKmonU9z5wadu/tS2R5n6eXgwhfhmaZXMQZCMAskswxpjHSfEbz5h5e9AbZZ4Z1YF7BoZY1M6W8vnmVeYRmxdLfH48D0Y9iHh2qfLPCT/zzv53lH5uNm6mpY8ekUR5RtHJrRPWmtaRe7XGWMP2zO2sSVnDtoxt1Eg1AHx23Wf08+kHmHJ1FRYU4u7ubklT2wSyLDNh3k4SsssY3cWLT25uvCILLeU8Vak/9Ra7jEYj69ev59ChQxQVFSFJktn+d999t0ENbE6oYlfzobCqkFUpq1iWvIyTxSd5sseTzOw4U9l/qvgUwU7BCILQpi9ghRU1/Lovgx93p5FVUg3Ai2MjuKO/5R9IUrLKuO6xtZSUm5Yc9O3kwZ9vDkenvXwumLbs03/z07BphggAAIjhSURBVN7TvLLiGJIMgW62fHN7D4Ld7S1tVr1Rfdr6aM4+NRglpr60me1xOQA42GrZ9OEowvzU3/ZLYeZTYw3aeT0RSk1LomRBRO40EWO/h8An2nJGqtSL5nye1kGW4VxUUXku2nk9wcEL48AnkbtMBY2OBxYfYsOxHALdbFn9cAzWV3BP0Zi0qM/3Avyd8Td/Jv/JodxDFFYX1tkf6RHJgpELlO1KQ6VSjbClsD9nP6tPrWZj2kZK9aVKu73WnmGBw7i14610cOmgtLd0n7Yk1h7N5uGfYwH48a5e9A1pnM9d9Wnrpd7Tl59//jnr16+nb9++BAcHN4JJKioXRpIl9mTtYWnyUjanb8ZwNmQdILEo0axviLNlZ/IszYmcMhbuTOPP2Eyq9CZBWisKjOriRa+g5rHcJNjbgc8e78etr28DYPexPP5vYSyv3d3dwpa1LGb0DsDH2YZHf4kjraCSaV/u4YdZvYjwdrS0aSoqzZa3Fx1WhC6Ajx/pqwpd9UVjheG+XYhxPyPu/hyhKAUhfhli/DKkoIFI/R5CDh1eK06oqFwteYlotr+H7NkJKeZxU5u9J4bbVoBXFyUf166TBWw4ZjqvnxsVYXGhqzUw2G8wg/0GI8sy6WXpSsXH2LxYkouS6epunnN19sbZlNaUKpFfUR5RhDqHKssAmyPz4+YrRay0opaBvgMZEzyGQb6DsGmlRQ1aCqM6t6N3sCt7Uwp5Y3UCS+/vh0bNUatSD+otdm3ZsoUXX3yRPn36NIY9KioXZFvGNv63739klmcqbS7WLowPGc/k0MmmKjQqAMzbcpK5m5KUbRdbHTf39ueWPgFKZcXmwpi+/jw8tROf/HEMgHnLjtOnswfj+wdY2LKWxdBwTxbf3Zt7fzyIt5M1gW5qXgMVlYuxfm8GH/4Wr2zPmRjOpIGBl3iFykWxskfqdTdSjztNVRt3fop45iBi6naE07sxPHwIHLwsbaVKSyXnGJod7yPE/4mAjGzjgtRrNlifnczxqV3WZJRk3lh9HID+7d24vpPnhY6ocpUIgkCAYwABjgGMb29KfF9aU0qNsUbpU2moJLEwEaNsJL0sndUpqwFT4vtuHt2I9IhkcuhkfOwtk0ctqzyLtalrOZJ/hHcHvqvkHhsbPBZJlpRE8+fSoqhYHkEQeGFMBFPm7+JYVilLDmZwU081T5rKlVNvsUur1eLr69sYtqioKOjPVtPRiTrAJGydE7r6efdjcuhkhvoPxUrTMvMTNSTl1QastSJajWkGs0+wKXIrzNOeOwcEMTHSB1ur5juj9uJtkew/nsc/R3MBeGjubroEuxDio0Ym1Ycuvk78dm8frLRis/a3ioolOZ1Tzv0f7FK2e0W488qd0ZYzqLUgapA7TcLYcSJS2j+Iuz4F+3ZmQpeQsQ/ZPRxs1Ag6lcuQfRTN9vcRjy9XmmTPjhgHPgm6C0/m/Lo/nePZZYgCvDAmok0kUbc0jlbm92nWGmsWjV5EbF4sh3IPEZcbR0Z5BhWGCnZn7WZ31m6GBwxXxK4z5WfYn72fKM8o/B38G8VnJTUlbEzbyJqUNUr0FkBCYQId3ToCMDl0MlPCpjT4e6s0DF18nbiphx+/7s/g/Q1JjO7ireQeVlG5HPX+ptx4440sXbqUhx56SP0hUWlw0krT+DP5T1acXMFj3R9jbMhYALq6d+WJHk9wnf91+Dn4WdjK5kFGUSU/7Erj1/0ZvDm5C6O7mB4qegW58PPs3vQIdGkR56hWI/LVMzEMfXQNuUXVlFbomfX2dta8MwJba/XHrD74upjnydiXWsi6o9k8NzpCDftWafNU643Mens7RWWmSAQ3Ryu+fTYGK7WKacMhCMhBMRiDYkAy1rYbqtD8djvoK5C6347UZw44qb/lKv/CWINm6T2ICauUJrldZ4wDn0LuOB6ECy9LrNIblYj26b381WX8FkIURMJdwwl3DeemDjcBkFuZS1xeHLG5sRwvPE6oc6jSf0fmDt7c+yZgnvg+2jOajm4drzrxvSzLbDq9iTUpa9ieuV2ZQAdoZ9uOUcGjcLKuFd1bwr1yW+fx68NYdSSL/PIaPt96kmdGhVvaJJUWwhU9Sb766qtm24cOHWLv3r0EBQWh1Zof4uWXX24461TaBNXGav46/RdLk5eyL3uf0r7s5DJF7BIEgVs73mopE5sNsiyzL7WIhTtT2XAsB+lseYlf9qUrYpcgCPRsJnm5rhRvN1u+ejqGG/67GUmSOXyyiOe/PMDch9Xl0ldLVkkV9y86RFGlnrSCSj6c1g07K1U8VGm7/Pebgxw8UQCY0kjNf7I/fp4tr5hDi+G8HD1CzjEwVCHUlKHZ/Rni3i+Ru9yAse+DppxLKioAGiswmIrpyF7dTCJXxJiLilznsNFpmDcjmo//SubR4WFNYanKFeJp68nwgOEMDxheZ1+NsQZXa1cKqwspqCpgc/pmNqdvBkwrO8YEj+GVfq9c8Lhnys9QVF2kbLtYu5gtj/zi8BckFycD4KBz4PrA6xkTPIYenj2adf4wlQvj4WDNg0Pb88GGJDUNpEq9uKInH3t785vBmJiYRjFGpW2RVprGLwm/sCplFSU1JUq7v4M/U0KnKDkBVKDaILH6cBYLd6VyNLO2Uoyfiw239wvkxh4tf4Z8UKQXz8/sxhs/xAHww/pk+nb2YMZwNR/b1eDlaM1t/QL4ZPNJ/krI5dZv9/HFzO54OraOEuEqKvXhj60pfLPqhLL91PSuDO+ppmRoKmTf7hgejkU89CPinvkIJRkIh39FPPwrUvthpmT2IYMtbaZKEyNkHEA48ivSyDcVQcs49AWk7rcjh4+pV3GDXkGufD+rV2OZqtII3NLxFmZEzOB02Wkl8X1cbhzJxcnoJT02GvM8s89ufxadqCPYKZivjnxlFrElIrJi0gp87H0QBIFJ7SdxMPcgY4PHMtBv4FVHiak0H27vF8TwiHa0VyepVOqBUFNTI1vaiHMkJiby/fffk52djbe3N3fccQdhYReeocnNzeWpp57C2rr24tWpUycef9xUpaWgoIB58+aRmZlJ9+7dmT17NqJo+iFduXIl1dXVTJ06tV72lZSU4OHhQV5eHk5Oas6Ja+XvjL95bOtjAFiJVgwPGM7ksMn0bNcT8TKzePWhNZSTXXMki0d+iVO2ewe7cke/QIZ39FRydbUGJEnmlv/7mw37TPnZbK00rH9/JJ2DXcz6tQafNhW/H8jgv3/GY5Bk/Fxs+Pq2HoS1c7C0WXVQfdr6aC4+TThdzIgn1lNeZargOyTai99eGYqmFV07m4oG8alRj3BsGZqd8xByjgAgBQ/COHNpA1ioUl8scZ4K6XsRt72HeHITAIapC5E7jruqY0mSjNiMl+k3l+tgS6K0ppS4vDg8bT0JdzUtV6s2VjPot0Fmldj/zdwhcxns1/iiuerT1ofq09ZLve/0zpw5Q0ZGRp32jIwMsrKyrtqQsrIyPvzwQ66//no+++wzhg8fzgcffEB5efklX/fhhx/y5Zdf8uWXXypCF5gErYiICD766COys7PZv9+UlDAnJ4fdu3czceLEq7ZVpX7IsszhvMO8tvs19mTtUdoH+Aygn3c/nu75NOumrOONmDfo7dW7QYWulkr8mRJO5tZ+96/v1I5AN1umdPdl2f39WHx3b0Z18WpVQheAKAp8/kQ/AtqZZm0qa4zc+dZ2Sir0l3mlysW4sYcfX9/eAwdrLRlFVUz/ag+7TxVY2iwVlSahvMrArLe2K0KXj7stXz41QBW6LIlGh9z1JgyzN2OY8TtSyFCkfg/X7pdlhNifoKbMYiaqNA7C6V1oFt+IduEYReiS/HqBvftVHW97Uj6TPt+l/qa1MhytHInxjVGELgBJlniyx5OMCRqDh43HBV/370gwldZLZY2RH3enYZSaTcyOSjOl3nd77733HkePHq3TfuzYMd57772rNmT//v24uroydOhQdDodQ4cOxdnZWRGp6ktOTg6dOnXCysqKiIgIcnJyAFi4cCG33HILOp3uqm1VuTKKqotYfHwx01dP5471d7AseRm/n/hd2a8VtXw27DNmRMxQy/xiKpu9IT6Hmd/sZdJnu/h4c7KyT6cRWfNwDO/c0JUuvq07qtDV0Zpvno1BpzVdnpIzS3ns493IsvqDdrXEhLrz8+zeeDtZU1JlYNbC/WQWVVraLBWVRkWWZZ6Yt4eE06Zl8hpR4JtnYvBwVh+ImgWCgNx+KMZbfkcOHVbbfGoL2pUPo/0kCnHz61CWbUEjVRoCIWM/mkVT0H4/HvHUFgAk/74YZvyO8Y41yAH96n1Mg1HizTXHOZ5Vyvy/TzWwxSrNDVutLdPDp/NGzBt8NPSjC/b5d3VIldZJld7I2E//4dWVx/l1f7qlzVFp5tRb7EpOTqZLl7rJRDt16kRycvIFXnFlnD59msDAQLO2oKAgTp8+fcnXvfDCCzzyyCN8+OGHZGZmKu0BAQEcPXqUmpoaEhIS8Pf3559//sHFxYVOnTpdkU16vZ7KykqzP5VLI8kSe7L28PyO5xm1dBTvHXiPpGJThZyu7l0Z5DfIwhY2P0qr9Hz3TyrXz93OAz8dYk9KIQCnCyvQGyWln5W27UQi9Ax35/XZ3ZXtP3ec5ssViRa0qOUT4e3Ib3P60tHbkYevC61TuVFFpbWxcG0yv29JVbZfmRVN386eFrRI5f/bu+/wKKq2j+Pf3U1vhCRAQoAYCB3pIFVAiiAKooIIIjZERB8bWPBRH33FDvaCBREQRBBUFAFB6UiTHnpNSEKAJKSXze77R2Qh0jHJJJPf57q8ZHdnd++dk/vs7D1nzrkkJ2NxevhiyT6JbdW7uH3YDNvPj8JxfQeUWcd3Yz24HABHjbbYB88m/66fcdbsfFnzcp3p2/Wx7EnMwGa1MKZX3SIMVkq7QM9APKwehe7zsHoQ6BloTEBSorzcbVxbu2A06DuL9pKapas/5PyuaGmucxV9MjIycDgc59j60uTk5ODj41PoPh8fH7Kzs8+5vb+/Py+88AIRERHk5OTw008/8dZbb/Hqq6/i7e3NjTfeyJQpU3jppZdo3rw5tWrV4tVXX2XMmDHMnj2bHTt2EBISwl133YW397l/9P3888/88MMPrtt5eQXJlJycjN1+/mvGy7PFcYt5bctrrtv+7v50r9qdntV6UtO/YKLxpKSSHW6empp68Y0MkJyZx8Q18fy8/TiZeQW5Y7XAtbUCGdisCk3D/Ug7mWJskAbq1yaYZRtD+WVNweXRL0zcSFSoO82iAkttm5Z2HsCE/rXxtFlceehwOnE4nIZfEqs2NR8j23TbgZM889np1X27t6jM7R0rlfj3j9mUSJtG9MZyZ0e8o7/FZ8tkbJmJWDZ/g3XzN+REXEd6ixHYqzQp/jjKiSJvU6cTjyOryPerSn5gZMF9Va8joHYfsur3Jy/8moL7kpOv+C1OZtt5Z1HBghO3NK5EsFtuqc1tfbcVPU88+arjV4UWtwrwCMAzx5OknOL/O1CbGm9o82B+2hxPcmYe4+ZH82in6v/q9dSmZdOlzLN22RPUP//883h6evLss89isxUs3Zqfn8/YsWPJzs7m1VdfvaTXWbVqFZMmTQIgODiYhg0bkpGRwfDhw13bfPrpp/j7+zN48OCLvp7T6eTRRx/l/vvvp3Hjxmc9/uWXX1KvXj38/f359ddfGT16ND/++CN5eXkMGDDgnK+Zl5dXqKiVmppK9erVNUH93+wOO6viV1HDvwZXBVwFQGZeJj1/6En9oPr0q9WPLtW7GL4CSmmddDAtO4+Oby0jIzcffy83BrQI585ralCtokbcnJKWmUf3JxeyJ7bgS6hqiA9L3u2JJT+jVLZpWfTar7vYk5jO+wOb4Od5Rec/ikRpzVO5cka1aUp6Ll0enc/hxIJ5DyPD/Pj9nesJ8PW4yDPlYkq8Te05WLZ/j+3Pj7Ac31VwV69xOJsPLbkYTK7I2tTpxLL/D6zL38J6ZB2OhreQf/Nn//51z+GVeTv5evVhKni78dtjHajoU3pzW99t5qM2LR2+WnWIV3/dhZvVwrxH2hEZcuWrNKpNzeuyf9ncf//9PPnkk9x33300atQIgG3btpGRkcGbb755ya/Trl072rVr57q9dOlSFi5cWGibw4cP07Nnz0t6PcsFhkHv3LmTpKQk2rdvzy+//EJkZCRWq5WoqKiz3vNM7u7uheb2OjWyq7yLTY/lx30/8tP+nziWdYz+tfvzbKtnAfBx92Fun7mag+sfsvPy+XFzPFl5+dzdNgIAfy93RnSqia+njX5Nq+JrYKGhtPL3cWfSMx3o/uQCMnPyiTueyYPjV/HJI2cXtOXybTuSysRVBZd53fHFOj4f0ozQAM1nJGWXw+Fk5Dt/ugpdXh42vnqmgwpdZZWbJ84mg7A3Hohl32Ksm6bibHz76cdP7MF6YDmOJgPB3ef8ryPFx+nEsm9RweqKcWfMs5udCg47WIv22GbfsQy+WVMwxckjXWqV6kKXiBSfwa2rM31tDAdOZPL6/N1MuLPZxZ8k5c5lX7cSERHBhAkTuPbaa0lJSSEzM5Nu3brx5ZdfEhkZecWBtGjRgqSkJJYuXYrdbmfp0qWkpKTQokWLc26/b98+4uLicDgcZGdnM2PGDCwWC1FRUYW2y8vLY9q0aQwdWnAWsFKlSuzatYu8vDy2b99O5cqVrzjm8iQ3P5cFhxYw4vcR9PmpD19u/5JjWcewWqyk5xVeMUmFrtMSUrMZ99sern17Gf/9MZr3f99Hes7p0YLDr43kzmtqqNB1AfUiKjBuZCvX7d//SuDjufsNjMg8GoUHMO62q3G3WdiZkEb/CWvYmZBmdFgiV+zDOTuYv/b0itFvPtiCq2tWNDAiKRIWK86o7uTf9jW4nS7I2/78CNuCp3D7sBnWZW9AxnEDgyxnnE4su+dj+6o7bjPucBW6HLW6Yb97PvkDvy3yQhcUjEa2O5zUquTLoNb/7tIlESm7PNysPPv3fH2/7zrGir0nDI5ISqMr+hYKDg7m3nvvLdJA/Pz8ePzxx/n666+ZMmUKoaGhPP744/j6FgxJPHHiBM8++yyvvfYawcHBJCYm8v3333Py5Ek8PT2pWbMmo0ePPmver59//pnWrVu7ilotW7bkr7/+4pFHHqFatWqMHDmySD+HGa1NWMszK58hJSfFdV+4bzh9a/WlT80+VPZRwfCfNsWk8PXqw8zffhT738vierpZuaFRKDl5+YZeLlYWDegSyZro40yaX7DYwftz9nJts+p0bhpqcGRlX58mYVQJ8OShaZtISM1h4Bdr+XBgUzpEXdlS8CJGWbk1kVcmb3HdvqNrJIO71zIwIiluTs8AnG7eWDJPYFv+FtbVH+BoPBDHNQ9BUE2jwzO3rCRsPzyAJS8TAEdUDxwdR+Gs2rzY3tLpdNL76lB2JKQxpldd3A2ea1JEjNW5Tggdo4JZvvcEy/Yc17GrnOWy5+wqz1JTUwkJCTH9nF1Z9izsDrtrCd/jWcfp9UMvrBYrXap1oV9UP1pVaYXVUjYOMkryOuy8fAdDJq5nw+EU131VAjwZck0NBrQM13D7fyE7N58bnvqNzfsKJrUNDvDkj/d6Eh6iS1eKwt7EdIZN+YvYlGzcrBb+r28DbmseXmLvr/kSzKck2/RochZdHp3P0eSCRW0aXFWBBW/1wMdLJxaKUqnM08wTWDd8hXX9F1gyC0Z2ObHgrNub/G4vQWCEwQGWbpfcpk4HJO2D4Nquu6yLX8SStJ/8DqMgrOQWDcjOy8fL3VZi7/dvlMqckX9FbVq67DuWQdzJLDpGhVzxa6hNzUvFrstg5mKX0+lkR9IOZu+bzYKDCxhcbzAPNn7Q9fiS2CU0CWlCRa+ydzlIcXdg+Q4nNuvpOeMemraJ33Yk0rR6Be5uG0GPBpV19rGIHEpIp8tj8zmZUTB/Xuv6Ifz0alfc3bR/i8Lx9BwemLqRrUdSqRLgyfz/tC+xUYg60DCfkmpTe76DW5//gxVbEwHw83Zj8TvXExVuru/p0qBU52leFtatM7Cu+RhL0n6cNg/sD28EvypGR1aqXbRNnQ4sO37CtmIcpMUX7FNPf9djlJETn0Yp1TkjV0Rtaj5qU/PSKc9yKD4j3nVJYkZeBn8l/sXvMb+zO2W3a5uFhxcy/Orhron/O1frbECkpdueo+l8/edhNhxK5ueH27kKXo91rcUDHa+iafVAYwM0oYhQPz56vA13vrIcgLU7jvN/X2/m5fs0KWVRCPHzZOq9LXn+xx3c3+EqXW4rZcLr32x1FboAPnj0GhW6yiN3bxzN78bRdAiW3fOxpBwoVOiybJwCOHFePaDQvF9yHo58LDt+wLZivGslTCcWLAeW4Kx3U8E2JVToSs7MZey8XTxyXS0igjSaW0TOzeFwsj0+lavDNX+0FNAvmXImPiOefnP7kevIPefjDYIa0C+qH9dHXH/BFS7LK4fDybK9x5m06jAr952eCHHxzkR6NCg4qK5Txd+o8MqFXtdUY9gNV/H5vIMAfPTDTlrVD+Gmdpqotij4eLgxrv/Vhe6LP5mNr4eNAG/38zxLxBgL1x3hnZnRrtsP9qlLn/Y1DIxIDGe14azXm0KXLdizsS0ZiyXzOM6lr+NoeT+OFveAd9kbrV7sHHYs22djWzkey4mCeTKdWHA2uJn8Dk9CpXolHtL7v+/jx83x/Lk/iT+e7KjR8iJylqOp2Yycvpltcan8PLItUZX9jA5JSgEVu8qZlJyUcxa6utfozr0N76VuxboGRFX6ZeTYmbMpjsmrD3PgRKbr/jqV/RjatgbX1r7y68Tl8j1xW222H8pg1fZjADzy3hoaXhVIzaoqNBa1k1l53PP1BqwW+HxIc8IDvY0OSQSAw0fTGTH+T9ftlnWDefHukps3SMqQ3EwcdW/AumUGloxEbEtfxbrqPRxNB+No/SAEqkB6iu3X0Vg3TQHAabHibHgL+e2fhJDaF3lm8dhzNJ3p62IBGNEpUoUuETmnIF8P0nPs5DucvDZ/F1/e1cLokKQUuKRi14gRIy55lM/HH3/8rwISY9zd4G4Vui5g5PTNrpFcFgt0qVOJoW1r0LZmkEbAGcDNZuXzp9rT5dH5JKZkk5aZxz2vr2D+W93x1qV3RSo6PpWY5Cxy7Q76T1jD50Oa07CqLhETY+Xk5XPP6ytJSS84eRPk78HEp9vjUUYmrZYS5hOE44bxOK59Buv6L7H+NRFLVjK2dZ9hXf8lzgZ9yb/pQ7CVw0Vk8vMKzb3laDoYy+ZpOBvdRn77xyE4yrDQnE4nY3/dRb7DSZ3KftzespphsYhI6eZus/Jcr7rcO/kvlu05wZLdx+hcp5LRYYnBLun0SLt27Wjbti1t27alRYsWxMXF4e7uTuPGjWncuDEeHh7ExcXRooUqqFL2OZ1Oth45Wei+W5tXxdfDxpA2NVj4aHsm3NmMdrWCVegyUGiQN5+Nbof177nSth1I4ZkJGwyOynza1gxmyj0tqejjzrH0XAZ9uY4lu48ZHZaUc//9YiOb9iYBBScgJoxqR3glX4OjklLPrzKOzs9if3gT+T1ew1mhBhZnPmSeKH+FrvxcLBsnEzL9eiy7fnXd7QxvWbB/+nxkaKEL4Pddx1wnGsfcUBc3jeoSkQvoWDuEznUKrrZ57ddd5OU7DI5IjHZJQyCGDBni+vf48eO5+eabufvuuwttM3nyZI4d0w+g0i7QMxAPq0ehSxk9rB4EegYaF1QpkWN38MvWeCatOsyOhDRmPtDaNcl8z4ZV6FwnBH8vzVlUmnRsXIUxd17NK5O3ADD1t/1c06ASg7rVNDgyc2leI5DvHmjN/ZM3cigpk+FTN/LijfUZ1FrzpEnJ+37pQSbO2+O6PXpgI65rHmZgRFLmePjiaDUMR4t7sOz8GQLCTz/mdGL7YRiOWt1wNrzFfEUwew7WzdOwrnoPS2rB5YGOVe+SX6/36W0CjM+nXLuD1+cXLJzUtV4l2tcKNjgiESkLnu1ZlxV7T7D/eCZT18RwT7sIo0MSA132KZJly5bRvXv3s+7v2rUry5cvL5KgpPiE+YYx56Y5fNPzG9d/c26aQ5iv8Qc2RjmWlsP7v++l09vLeHr2dnYkpAGwfO/pCejdbVYVukqpR29tQI+WVV23n/pkPdsPJBsYkTldFezLdw+0pln1Cjic8OLcHby5YDcOh/PiTxYpIrtiTvL4h+tctzs3DWXU7Q0NjEjKNKsbzgY346zWynWXZf/vWKN/wG3uw7h91ALr6g8gO9XAIIuIPRvr+i9x+7gVtvmjsaTG4rS6k9lgIPm3TjQ6urNMXXOYgycycbdZeKZnHaPDEZEyomYlX4a0KZiH8cM/9pGUce5F2aR8uOxil6enJ9u3bz/r/u3bt+PhYbKzXyYV5htG/aD6rv/Ka6FrZ0IaT32/lU7jlvHBH/s5kZGLu83CLc2q8uNDbXikSy2jQ5RLYLVa+PiJNlSvXHAJU1ZuwVw+qZl5BkdmPkG+Hky+pyXXN6gMFKzSKFJS0rPyuPu1FWRk2wEIC/Zmwqi22HRpkxQhZ8WrcDS+A6fVHUtaPLbfX8LtwyZYF78IqXFGh3dlju/B7aOW2BY8jSUtDqfNg/wW92IfuZ60Ti9DhdI3Sjff6cTTzcpdbWpwVbAuURaRSzeyc00q+riTmm1n2toYo8MRA132TM79+vXj/fffZ8+ePdStWzCh+a5du5g/fz6DBw8u8gBFisvKfSeYsykegBA/Dwa1rs4draoR4udpcGRyuSr6ezLx6fbc8PQi8uwO9sWl8ej7a5j4dHvNq1bEvNxtvH97E6avi+G2FtVcc6aJFCen08mTH69jd0zBCBs3m4Uvn2pPSAUvgyMT0wmqRf5NH0CnMVjXf4b1r0lYctKw/fkR1rUTcLR7DEenZ4yO8vJUvArcPHHaPHE0uwtH20cg4O8R0UlJhoZ2PsM6RNK7USj+Xlp0RkQuTwVvd8b0qktmbj4DWoRf/AliWpf9DTJw4EDCwsKYM2cOv//+OwDVq1dn1KhRdOrUqcgDFCkKqVl5zPrrCNc3rEJ4oDcA/ZuHs2hHIre3rEavRqF4uml0QFnWvE4wY+9vzlOfrgfgp5UxfDZ3N8P7aJXRoma1Whh8TY1C9/26LYEGVQOICPIxKCoxs0nz9zJrySHX7f/d3ZRrGmiVJSlGAWE4rnsRR/snsG6cjHXthIJRUQGl/IdTbgbWjV/DySM4eowtuM/mTn7fT3FWqAb+ZWc0f9W/j9dERC7XzU2rXnwjMb0rOl3SqVMnFbakTDh4IoPPlhzml+gTZObmk5iWwzM9C4ofAd7uTL+/tcERSlG694Yo1kQf4/tlBT+KX5i4keZ1gmlVL8TgyMxtxd4TPDFzKwHebkwY3My1sINIUdi0N4kxn/3lun1j22o82FdFbCkhnv442ozE0eoBLDt+xFnvxtOPHduF7aeHcLR5CGf9vmA1cBRSbjrWDROx/vkxlszjOLHgaH43hNQGKDQvWWnldDqZtjaGmxqHEeCteVJFpOhk5Njx9dRI0fJGQ1nEdJxOJyv2nmDYlL/o/u5KZm5KJDM3H38vN3VyJmexWBj/cCvqVA8AwJ7v5N43VnLiZI7BkZlbBW83Kni7k5SRx5Cv1rMw+qjRIYlJpKTncs9rK8i1FywfXjPMjw8evUaXJ0vJs7njbHQbuJ2+dNa25mOsCZtx+2E4bh+3wrp2AuSml2xcOWlYV76L24fNsf3+ckGhy90HR5uR4F2xZGP5l37bkcj/ft5J9/dWcCJd39si8u8dTc3miZlbuPmTP8n5+1hCyo/L/uWfn5/P7NmzWbZsGYmJidjt9kKPf//990UWnMjl2ng4hed+3M6exAzXfTUqenJP+0j6Na2qYlc54OftzlfPdKD7EwvIzMkn7ngmw8etYsaLnTSRdTG5OrwCM4e35v7JG9l/PIOHv93MmF51ubutlnuWK+dwOHlo/GoO/92fe3nY+OrZDgT4ajEcKR3yW94H9mws0T9gORmD7bfnsC5/C0fze3C0uh/8qhTr+1vXfIx15TtYsgpWIHZ6+OJoeT+O1iPAt2yNaM6xO3h9/m4AWkZUJFjzp4pIEcjKy2f+9qPk5TuZuuYw97W/yuiQpARd9i+/qVOn8v3339OpUycyMjK45ZZbaN++YBLoO++8szhiFLlkwX4e7D1W8MOoQ1Qwnw9pxoyhjbjzmhoqdJUj9WpUYPzDpy9R/WNjAuO/izYwIvOrXtGH7x5oTeurKuJ0wth5u3hl3k7yHU6jQ5My6oPZO1iw7vTqd28+2IJGkWVrpIqYXGhj8m+egH3kevJbD8fp7oslOwXbqndw+7AZlp2/FO/7pyVgyUrG6eFHfvsnsI/ciKPL82Wu0AXw9epDxCRn4W6z8PT1dYwOR0RM4qpgX+5qUzDP7Id/7Neo0XLmsotdv//+O48//ji33XYbNpuNLl268MQTTzB48GB27txZHDGKnNOmmBQe+24LU/487LqvRpAPL95Yn3mPtOOroS3oXKcSVl3uUi7173wVd/eKct1+Y/pW/tgYb2BE5lfB252JQ1twU+NQAL5efZinvt9mcFRSFq3cmsgrU7a4bg/qFsng7rUMjEjkAipUx9F9LPZHNpPf5XmcvpUBS+F5shx2cP6L4n9WMtblb0NO2umXbDOS/I6jsT+8CUfnMeATdOWvb6Dj6Tl8vHQ/APe0i6CGFjoRkSI0snNNgnzdSc+x887ifUaHIyXosotdSUlJREZGAuDt7U1GRsEomjZt2rBmzZqijU7kH/LyHfy8JZ7bJqyh/2dr+WVrAhNXHSo0emRw6+rUruxnYJRSWoy9vzlNogoO/p1OGP72ao4czzQ4KnPzdLMy7rarGdEpEqsFejUq3st4xHyOJmcx7K2VOP7u1xteFcgbw1saHJXIJfAOxNHuUewPbyR/8PfgV9n1kHXlO9i+7oVl58/gyL/018xMwrrkVdw+bIZt2etY1395+jG/KjiufRq8A4vuMxhg/KK9ZOTkE+LnwYhONY0OR0RMxt/LnSe6FSzWMXNDLDvi0y7yDDGLyy52VapUiRMnTgAQFhbGhg0bANi1axfu7lo5RYpHcmYuny7dT5fxy3l85lY2x54EoFn1CozuXtvg6KS08vKw8dXT7angW9A3nUjN4f43V5KnCSqLlcVi4Ylutfn54XZ0q1/54k8Q+Zs938EDb63iaHI2AH7ebnz1bAd8vHQZupQhbp44q7c5fdueg3X9l1iPrMft+7tx+7QN1g1fQV7W+V8j4zjWP/4Pt4+aYVs5HktuOk7viuDhW/zxl6DtcanM+usIAE90q42fppwQkWJwW/Nw6oX643DC2F934vw3I22lzLjsb5T27duzadMm6tevT9++fXnjjTeYP38+x44d45ZbbimOGKWci03Oouf7K10raLhZLfRqVIWhbSNoUq2CwdFJaRcR6sfHT7Rl8P8tA2DtjuO8/PUm/u++5gZHZn5njrC05zt4evZ27mhdjZYRmndJzu21b7ayYmui6/YHj7ahVlV/AyMSKQJunuQPmIb1zw+x7PoZS/IBbPNHY132Oo4W9+Go0wucf5+EyU7GuuMnrNtmYckrGIns9A7C0WYkjhb3gqe58mHF3hM4ndCwqj+3NqtqdDgiYlI2q4X/3lCXOyeuZ82BZBbtOEb3Bjoha3aXXey67777XP/u3LkzlStXJjo6mvDwcNq2bVukwUn55HA4OZGRSyX/gpV4wgO9qFvFj5jkLAa2qsag1tUJDfC6yKuInNazdTj/ubU+73+/A4CPf9hF6/qVuKlddYMjKz/eWbyXn7bEMz/6KG/e0ojeV4caHZKUMgvWHuHdmacXkniwT136tFeOijk4w5uTf+tESD6Adc2nWDdPw5J5AtvyN7Euf5NzzS7q9An5u8h1D3iYc3qG4ddG0jqyIm5WC1ar5lgVkeJzTWQQN14dSpUAT9rU1InX8sCSm5urMXyXKDU1lZCQEI4fP05AQIDR4ZhOeo6d2RvjmPLnYYJ8PZgx7PRqeoeSMqni74mXu+2yXzcpKYmgoLI5aauc25W0qT3fQb/nfmfV9mMA+Pu48/s711NTo0ZKRPzJbO6f/Be7E9MBGN2jNsM6XIXl7wUklKfmczltevhoOl0eW0BKei4AreqF8NOr1+FxBX2+FB/laRHKPIF1w0SsaydgyU456+H81g/i6PRssV+2qDYtXtq/5qM2LducTqfr2PMUtal5XfacXQCLFi3iscceY+DAgRw9ehSA2bNns2rVqiINTsqHmORMXv11Fx3fWsb//bKTgycy2RiTwoHjGa5tIoJ8rqjQJXKKm83K50+1p3JgwajAtMw87nl9BVk5doMjKx/CKnjx7bBWtKtVcDDx1sI9vDh3B/Z8zZ9W3uXk5XPP6ytdha7gAE++fKqdCl1ibj7BODqOxj5g2jkfdjTqb7r5uc60PS5V/b+IlLh/FrrE3C672DV37lwmTJhA69atycjIwOEo+KLy8/Njzpw5RR6gmJPT6WTNgSQemraJbu+s4KtVh0jPsePrYeOuNjX47dEORIaY9yBPjBEa5M3nT7VzXSqx7UAKz0zYYHBU5Ye/lztfDGnOLX/PyzJ9XSwjpm0iQwXHcu2/X2xk094kACwWmPBkW8Irqf+XcsLN0+gIStzR1GwGfbmOvh//WejEpohIScnMtfPu4r289PMOo0ORYnTZxa4ff/yRxx57jEGDBmG1nn567dq1OXDgQJEGJ+aV73Dy1Pfb+G1HIg4nVKvozZhedVk++lqe712PiGAfo0MUk+pwdRXG3Hm16/bU3/bzzaL9BkZUvrjbrLzeryGPXlcLgCW7jzP2110GRyVGmbXkIBPn7XHdHj2wEV2ahxkYkUgJ8wnGaStc8HLaPMEn2KCAit+4RXvJzM3nZFYelf3LX7FPRIy3MDqRj5bs55u1Mew8qqK7WV32BPUJCQlERUWddb+HhwfZ2dlFEpSYz7G0HNYeTHZNSu1ms3Jnm+os3X2cu9tG0KVuJWyamFRKyKO3NmBt9HEWro8D4KlP1tOkVkUaRWqyypJgsVh4uEstwgO9+XTZfp7sFgW5OtAob3YePskTH61z3e7cNJRRtzc0MCIRA1Sohn3EGsg8cfo+n2CoUM24mIrRltiTzNlY8N07qkdtfD0v+6eIiMi/1qdxGF+vPsy2uFTGL4mhbb1qusTRhC57ZFdoaCj79u076/5169ZRo0aNIglKzGN7XCpPfb+VTuOW8cTMLRxJyXI9dl+7q5h6byu61a+sQpeUKKvVwsdPtKF65YJLpbJz87n39ZWkZuYZHFn50q9ZVeaObEew3+kz+1m5+QZGJCUlPatgzryM7IJLWMOCvZkwqi022xVNJSpStlWoBmFNTv9n0kKX0+l0jeRtXC2APo01ilNEjGG1WnjuhroAbI5LZ/72owZHJMXhso8qb731Vj788EOWLFmC0+lk586dTJs2jYkTJ9K/f//iiFHKmHyHkwXbj3LHF2u5+ZM/mbMpnrx8J8G+Hhw8kenaTktMi5Eq+nvy1TPt8XAr6Ab3xaXx6PtrcDq1QG1JOrX/AfYfy6DrO8v5cVOcgRFJcXM6nTz58Tp2x6QC4GazMPHp9oRU8DI4MhEpTj9vTeCvwykA/LdXPR0HioihWkZU5IZGVQB4Y8FusvN0wtVsLnvscK9evfDw8GDSpEnk5OTw+uuvExwczIgRI+jSpUtxxChlyLS1MXy+/ACxKacvaW1UNYC729agV6PQQj9sRYzWrHYwY4c1Z/Qn6wH4aWUMn83dzfA+dQ2OrHwav2gPx9JzGfX9NmJTsnmoU6SGlJvQpPl7mbXkkOv2S/c0pXX9SgZGJCLFLSs3nzcX7AbgpsahNKsRaGxAIiLAU9fXYfGORI6kZDNx5SEe6lzT6JCkCF1WsSs/P5/ff/+dli1b0rVrV7Kzs8nKyqJiRc1zIwUOHM8gNiUbqwV6NKjC3W1r0LxGoH6wSql1T68o/tx+jO+XFfz4fmHiRprXCaZVvRCDIyt/3rilEdn2LSzdfZx3F+/lSEoWL91UH3dd2mYaG/ecYMxnf7lu39SuuorLIuXAj5vjSUjNwcvdyugedYwOR0QEgPBAbwa1DOWrNfFMWH6AW5tXpUqARpqbxWX9grDZbLz//vvk5uYC4OXlpUJXOeV0Olmx9wQPT99E2t9zrgAMaVOD+ztcxe9PdOSDgU1oEVFRhS4p1SwWC+MfbkWd6gEA2POd3PvGSo6f1IIbJc3X041PBzVlYKuC+WpmbjjCA1M3FupjpOxKTsvhntdXkmt3AFAzzI/3/9Na3xEi5cDtLcMZ3/9qnupRhzBdsiwipchdLUOp4u9J+1rBODSbialc9unyunXrsnfv3uKIRcqArNx8vl0Xyw0frOKerzewIDqR7zcecT1eI8iHp6+vQ3igt4FRilweP293vnqmAz6eNgDijmfy4LjV5Oc7DI6s/HGzWXn5pvqM7lEbgBV7TzDoi7XEq/hYpjkcTka+8ycxiQWrbnp52Pjq2Q4E+HoYHJmIlASLxcJNjcMY0kaLWYlI6eLjYeOnkW35eFBTFeNN5rLn7Lrpppv47LPPOH78OLVr18bLq/AfRM2aus7VjOJPZvPNmhhmrI8lJev0inUdo4KpH+pvYGQiRaNejQq883Brho9bDcAfGxMY/100o+9oZHBk5Y/FYuGBjpGEB3oz+vut7DyazoLoo9zdNsLo0OQKfTB7BwvWnV544M0RLWkUqZHhImaXnmPHz/Oyf26IiJSoIJ18M6XL/vZ59dVXAfj4449d91ksFpxOJxaLhfnz5xdddFIqfL36EK/N303+3+M6vdyt9Gtalbva1CCqsp/B0YkUnds6X8Wf0cf46teC0atvTN9Ky3rBdGmm5dGN0PvqUCr7e7J4ZyJDNRqgzFqx9SivTNniuj24e00Gd9OJMRGzczic3D1pA0G+7jzXqx4RwT5GhyQickEOh5MfNscR4OVOt/qVjQ5H/qXLLnZNnjy5OOKQUiQv34HVYsH295LQjaoGkO9wEhrgyZA2NRjQohqBPu4GRylSPMYOa85fe5LYvDcJpxOGv72aP97rSXiIDtKN0OqqirS6qvAIoM2xJ2lSrYJBEcnlSEjKYtibq3D8fbKk4VWBvDG8hcFRiUhJmLslns2xJwF4uHPeRbYWETHe+3/s46Ml+wkNKJjDy9vDZnRI8i9c9pxdVapUueB/UnYlZeTyydL9dBm3nIXRR133N68RyBdDmvH7Ex15oGOkCl1iap7uNr56uj0VfAv+zk+k5nDfGyvJs2v+rtJg8p+HuW3CGt5ZtBenU7OIlmb2fAcPvLWKxJSC+db8vN346tkOeOuSJhHTy8y189ZvewDo1zSMxjpBISJlwO0tq+HlbiUhNYcvVhw0Ohz5l67oiDMmJoYff/yRw4cPA1CjRg369u1L9erVrziQlJQUvvrqKw4ePEhKSgovv/wyEREXnp9lw4YNzJgxg+TkZCIiIrj33nupWrUqALGxsXzyySckJyfTuXNnBgwY4HrepEmTiIyMpFOnTlccb1kWl5JFcubpM2zJmbn8uu0oP26OJ+fvH/RT18TQq1EoUHCZaqc6lQyJVcQIEaF+fPxEWwb/3zIA1u08TsOhc0jPshMVHsDoOxpxU7sr7+/kyjidTtYcSALg46X7OZKSxas3N8TD7bLP20gxmrsqhremb2Pn4ZOuy98BPni0DbWqao5HkfLgs+UHOZqag7e7lSe61zY6HBGRSxJWwYsHOkTy/h/7+GzFAW5rEa5J68uwyy52LV++nFdffZU6depQv359AHbu3MkDDzzAmDFj6Nix4xUFYrFYaNy4MX369OHll1++6Pbx8fF8+umnjBw5kgYNGvDzzz/z3nvv8eqrr2Kz2fjuu++47rrraNu2LS+88AKtWrUiMjKS3bt3k5CQwNChQ68ozrIuLiWLHu+tdBW1/qmijzsDW1VjcGv9kJfyrWfrcB69rT7vzdoBwInUXACiD6Zw92sruP/G2jSNCjIyRFPJyMjA1zflotu1qxRAUsUMVu47wXd/HGDD1kTualMDLw0zLxU27U3ii5/3nHX/9a2q0qe9vldEyoMjKVmuERHDr61JaIB+KIpI2XF/h6uY+dcR4k9m89bC3Yzv39jokOQKXXax64svvmDgwIFnFYsmT57MF198ccXFrgoVKtC1a9dL3n7VqlXUr1+fpk2bAtCnTx9+++03du/eTf369UlMTKRBgwb4+PhQs2ZNEhMTqV69OlOnTmXEiBFYLJaLvkdeXh52u911Oysr67I/V2mTnJl3zkJXRJA3w6+N5KbGYXi560ejCMCYOxvz+dzdZObku+47NU7lXD/opeRtOZzBqL8SjQ5DLiLmWIbRIYhICXlrwR5y7A7CA724r71W0RWRssXbw8boHrV5YuZW5m5JYMg1NWhWI9DosOQKXHaxKykpiW7dup11f9euXZk5c2aRBHUpYmJiqFHj9Opcbm5uVK1alZiYGOrXr0+1atXYtm0bAQEBHDx4kL59+zJv3jyaNWtGWNilraz2888/88MPP7hu5+UVXPqXnJxcqAhWlpw8ee4fHC/1vIr6VXzITDtJZgnHVNxSU1ONDkGKWEm2qT1f80KJ/Ft7j6SRlJRkdBjyL+n71HyKuk23xKXzy7YEAB5qV9WUx5WXQzljPmpT8zlXm7YL96BxmC9b4jN4ae52vhhYD+slDJaRkhMUdPErbC672NW4cWO2bdtGeHh4ofu3bdtGo0aNLvflrlhOTg4+PoVXR/P19XWNvrrjjjuYPHkyy5Yto3v37ri5ubF+/XrGjBnD5MmTXcWyO+64Aze3c++GG2+8kZ49e7pup6amMmvWLCpWrEhAQEDxfbhiVCH73J81sEIFgoLK5me6FJeSDFK2lFSb1q4WQPShFP45F7q/jzsdG2tRjqKSl5uLu4fHZT/vZFYem2JScDidtIqoiJ+XFtAw0vItCaRlFj4ZZLFAnWoB6odNQu1oPkXZpm38K/DodXn8dTiF/m1qXdKVFGannDEftan5nKtNX+zTiFsnrMFpsYKnH0F+ngZEJv/GZRe72rZtyxdffMGePXuoV68eUDBn17Jly7jrrrtYvXp1oW3PZ9WqVUyaNAmA4OBgXnvttcuKw9PT86zLCjMzM/H29na95uOPP+567M0332Tw4MGsWrWK3NxcnnvuOT7//HOWL19Oly5dzvke7u7uuLuf/uF0amRXWVbRxx1PN2uhSxk93axU1AqLIuc0+o5G3P3aCiwWcDpx/f/Dx67hxraag6ioJCUlXfHB46ETmcSfzKZNTR18Gm3uqphz5svoO0ruZJiIGMfL3cbDXWrhdDpV6BKRMq1xtQpMvbclLSMqYrOqPyuLLrvY9cEHHwAwd+5c5s6de87HoGDC+fnz55/3ddq1a0e7du0u9+1dqlev7loNEsButxMXF0e1atXO2nbFihWEhIRQt25dVq9eTa1atQCIiooq9BrlQdVAbxY+2r7QaowVfdypGuhtYFQipddN7aoz6dkOvPXtNvbGphJVLYCn7mikQlcpEhHsQ0Tw6ZG+J7PymLnhCPe2i8Cqg5MSdWa+7IlNpbbyRaRcUqFLRMzgmkidSC3LLrvYtWDBguKIA4Dc3FzXv+12O7m5ubi5uWG1nr2sfLt27Zg/fz6bN292rcbo5+dH3bp1C22Xnp7OvHnzeO655wCoXLky0dHRdOzYkejoaGrWrFlsn6e0qhroreKWyGW4qV11bmqnH+tlQa7dwUPTNrH2YDJbj5zkzVsa4alFN0rUqXz5N6P1RKRseXfxXtKy7TzSpRaBulpARExo3cFkwgO99Du6DDm7imSgYcOGMWzYMABefvllhg0bxq5duwDYtWsXDzzwgGvbsLAwhg8fzjfffMOIESPYtm0bjz32GDZb4R8106dPp0+fPvj6+gLQpUsXsrOzefjhh8nJyTnvJYwiIlL2uFktNK5WAYB5245y99cbSM7MvcizRETkSsUkZ/LZ8gNM/vMws/46YnQ4IiJF7tVfdzHoy3W8uUCrsZclltzc3Etaaiw6OprU1FTatGnjuu+3335j8uTJZGdn065dO0aOHInHFUwwXFakpqYSEhLC8ePHy+wE9eWRRheYj9rUfIq6Tb9Zc5iXf9mJwwmRwT58fldzIoJ8Lv5EKTLKU/NRm5pPUbTpw9M3sSA6kWoVvZn/SDuNpj2DcsZ81Kbmcylt+uu2BP4zYwsA0+9vRcuIiiURmvxLlzyya+rUqRw6dMh1+8CBA4wfP57mzZtz++23s2bNGr799ttiCVJERORyDL6mBh8Paoq3u5UDJzIZ8NkaNsakGB2WiIiprDmQxILoRACeub6OCl0iYko9G1ahZUQgAGPn7cLhuKTxQmKwSy527d+/n2bNmrluL1myhHr16vH4449z22238dBDD7Fs2bJiCVJERORyda1XmW/ua0WInwdJGXkMmbieTSp4iYgUiXyHk1fmFUw3ck1kRXo0qGxwRCIixcNisfDfG+phscC2uFRmb4ozOiS5BJdc7EpLSyMwMNB1e8uWLbRq1cp1u06dOhw7dqxIgxMREfk3rg6vwMwHrqFWJV+aVKtA/TBdgi4iUhS+/+sIOxPSsFrguV71tAKjiJhaw6oB3NosHIDxv+0hPcducERyMZdc7KpYsSIJCQkA5OXlsXfvXurXr+96PCsrCze3y17cUUREpFhVq+jNjGGt+XhQUzzdStW6LCIiZVJatp3xi/YC0L9FNeqH+RsckYhI8XuiWxS+njaOpefy6dIDRocjF3HJR/2tW7dm4sSJbN26lYkTJ+Lp6UmjRo1cj+/fv5+wsLBiCVJEROTfqODtTgVvd9ft5XuP859vN5OVm29gVCIiZZPd4aBTnRACvNx4vGsto8MRESkRlfw9GXFtTQB+3hpPjt1hcERyIZc8FGvo0KG89NJLjBo1Cm9vb0aNGoW7++kfDgsWLKBFixbFEqSIiEhROZGewyPfbiYjJ58jJ7P4bHAzgv08jQ5LRKTMqOjjwRu3NCI5M5eKPuZdiV1E5J/ubheB0+nkzjY1dMVAKWfJzc29rKUEMjIy8PLywmYrvNpKamoq3t7ehQpgZpOamkpISAjHjx8nIEDzvpQVWiLYfNSm5lPSbTpzQyzP/7SDfIeT6hW9+WJIc2pW8i2x9y8PlKfmozY1H7Vp8dL+NR+1qfmoTc3rskuRvr6+ZxW6AAICAkxd6BIREfPo36Ian9/ZDF8PGzHJWdz++VrWHUw2OiwRkVJtzYEkvl59iLx8XbojIgJwJCWLfMdljR+SEqJxdyIiUi51rB3C9PtbUyXAk5SsPIZOWs8vWxOMDktEpFSy5zt4+eedvDJvFy/9vMPocEREDOV0Ohm/aA/d313BzA1HjA5HzkHFLhERKbfqh/kz84FrqFvFj7x8J0/O2sqhpEyjwxIRKXW+23CE3YnpWC1wV5saRocjImIoi8VCUkYeeflO3lm8h7TsPKNDkn9QsUtERMq1sApeTL+/Fe1rBTOmV10ignyMDklEpFRJzcrj3cV7AbijVXXqVPE3OCIREeM93rUWfp5uJGXk8dGS/UaHI/+gYpeIiJR7/l7ufHlX80KjFfLyHWTk2A2MSkSkdPhwyX6SM/MI8HLjP9fVMjocEZFSIdjPk4e71ARg8p+HOXgiw+CI5EwqdomIiAA2q8X1b6fTyf/m7mDwxHUkpuUYGJWIiLH2H8tgyp+HAXjkuloE+XoYHJGISOkx5JoaRAT5kJfv5PX5u40OR86gYpeIiMg/7D6azpxNcWyPS6P/hDXsOZpudEgiIoZ4bf4u7A4nNUN8GNy6utHhiIiUKh5uVp7tWQeAxTuPsXLfCYMjklNU7BIREfmHuqH+fHlXc/y93Ig7mc3tX6xl9X4dvIhI+ZKRYyf978u5n+1VF3ebfjqIiPzTdfUq0a5WEADjftuD0+k0OCIBFbtERETOqW3NYGbc35qqFbxIy7Zz3+S/mLMxzuiwRERKjK+nG9Pua8XUe1vSuU4lo8MRESmVLBYLz/WqS++rQ/lgYBMsFsvFnyTFTsUuERGR86hdxY+Zw6+hYVV/8vKdPDV7Gx/8sU9n7ESk3LBYLFwTGWR0GCIipVqdKv68O6Ax4YHeRocif1OxS0RE5AIq+3vyzb2t6FQnBIDZG+NIy9YqjSJiXimZeSzZfUyFfRGRK5SX7zA6hHJPxS4REZGL8PV049NBTbmvfQRfDmlOgLe70SGJiBSb9//Yx7ApG3ly1lajQxERKVPyHU6+Wx/LdeOXs+9YhtHhlGsqdomIiFwCN5uVZ3rWpWYlX9d9B45nEH8y28CoRESK1p7EdKatjQGgWfVAY4MRESljHE4nX648SEJqDq/P32V0OOWail0iIiJX4Hh6DvdO/ov+E9YQHZ9qdDgiIv+a0+nktV93ke9wElXJlztaVTM6JBGRMsXdZuXZXnUBWLL7OMv3HDc4ovJLxS4REZErEJeSTXq2naNpOQz6Yp0OZkSkzFuy+zjL954AYMwNdXGz6aeCiMjl6lynEtfWDgZg7K+7NH+XQfQNJiIicgUaV6vAjGGtqV7Rm4zcfIZN3ch362ONDktE5Irk2h289mvBJTdd6obQMSrE4IhERMquZ3vWxWa1sO9YBtPX6fjQCCp2iYiIXKGalXyZ+UBrmlSrQL7DyXM/RjP+tz1awUxEypxv1sZw4EQmblYLz/asa3Q4IiJlWlRlPwa3rg7A+7/vJTkz1+CIyh8Vu0RERP6FYD9PptzTku71KwPwybIDPDlrK3YNWReRMsLpdPLDpjgAhrSpQWSI70WeISIiF/NIl1oEertzMsvOB7/vMzqcckfFLhERkX/J28PGBwObMLRtDQB8PGzYrBaDoxIRuTQWi4XvhrXm6evr8HDnmkaHIyJiCoE+7jzatRZXhwdwY+Mwo8Mpd9yMDkBERMQMbFYL/72hHq0iKtK1XiUsFhW7RKTs8HS3cX+Hq4wOQ0TEVO5oVZ1Brapj1UnQEqeRXSIiIkXo+oZVCq1g9t36WLYeOWlgRCIi5+Z0OjmUlGl0GCIipmWzWlToMoiKXSIiIsVk8Y5E/vtTNIO/XMfinYlGhyMiUsjy/Sfp8e4KXpwbrXkGRUSK2Z/7k3h4+iZy7epvS4KKXSIiIsUkqoofVwX5kJXn4KFpm5i65rDRIYmIAJBjd/DeshgcTjiWlltoRKqIiBSto6nZ3Dt5AwuiE/lmbYzR4ZQL+lYTEREpJhFBPsx4oDUtIwJxOOGln3fy+vxdOBxOo0MTkXJuyp+HiU3Jwd1m4emedYwOR0TE1KoEeDHkmoKFjD74Yx8nMnINjsj8VOwSEREpRhV9PJg0tAU3NKoCwJcrD/Hod1vIzss3ODIRKa9OpOfw0ZL9AAxtG0FEkI/BEYmImN/IzjWp6ONOWrad93/fa3Q4pqdil4iISDHzdLfxTv/GDPt7pbP5248ycvomnE6N8BKRkvfO4r2k59ip6OPGQ51qGh2OiEi5EODtzuPdogD4dl0suxLSDI7I3FTsEhERKQFWq4Wnrq/DSzfVx9PNyj3trsJi0eo8IlKyouNT+W7DEQBGtAvH38vN4IhERMqPAS2qUa+KHw4nvPrrLp34LEal5tstJSWFr776ioMHD5KSksLLL79MRETEebffsWMHr7/+Op6enq77OnTowF133QVAbGwsn3zyCcnJyXTu3JkBAwa4tps0aRKRkZF06tSp+D6QiIjIOQxqXZ2u9SpRJcDLdZ/T6VThS0RKxLuL9+J0Qv1Qf25sGGJ0OCIi5YrNauG5G+ox5Kv1rNqfxOKdx+hWv7LRYZlSqSl2WSwWGjduTJ8+fXj55Zcv6Tk+Pj588skn53zsu+++47rrrqNt27a88MILtGrVisjISHbv3k1CQgJDhw4tyvBFREQu2ZmFrhy7gwe/2cjtLavRs2EVA6MSkfLg1Zsb8u7ivdzUOAwtwCgiUvLa1Ayie/3KbDlyEp3qLD6lpthVoUIFunbtWmSvl5iYSIMGDfDx8aFmzZokJiZSvXp1pk6dyogRI3QGXURESoUPft/Hir0nWLnvBM9cX4d72kXoO0pEik2Inyev9G0IQFJSksHRiIiUTy/3qY+Phw0fj1JTkjGdMr1ns7OzefTRR7FYLNSrV48BAwYQFBQEQLVq1di2bRsBAQEcPHiQvn37Mm/ePJo1a0ZYWNglvX5eXh52u911Oysrq1g+h4iIlF8PdLyKzbEp/Hkgmdfm7yY2OYvnbqiHzaqCl4gUnbx8B+4ayiUiUiqE+HlefCP5V8pssSssLIz/+7//o2rVqqSmpjJ9+nTeffdd/ve//2G1WrnjjjuYPHkyy5Yto3v37ri5ubF+/XrGjBnD5MmTiYmJoUaNGtxxxx24uZ17N/z888/88MMPrtt5eXkAJCcnFyqCSemWmppqdAhSxNSm5lPe2/TtmyIZ+5uVX3ecYMqaGA4eS+P/bojE291mdGhXrLy3qRmpTcuu4+m53DN9B3c0r0L/ppVdRS+1afHS/jUftan5lIY2PZyczbrDqdzaRHN3XapTg5wuxJKbm2vI9P+rVq1i0qRJAAQHB/Paa6+5Hhs6dOhFJ6j/p+zsbB588EFeffVVqlatetbjb775Jn379uXIkSPs37+f+++/n88//5yoqCi6dOlyztf858iu1NRUqlevzvHjxwkICLjk2MRYSUlJl5QMUnaoTc1HbVowSf17v+/joyX7AWgcHsCEO5uV2TN/alPzUZuWXc/M2cb3f8VRyc+DhY91wM+z4ESv2rR4af+aj9rUfIxu082xJ7nji7XYHU5+GNGGBmGqMxQVw0Z2tWvXjnbt2hXZ611ofpMVK1YQEhJC3bp1Wb16NbVq1QIgKiqKw4cPn/d57u7uuLu7u26fGtklIiJS1CwWC491jSI80JsXfopmy5FU3pi/m7duu9ro0ESkDNt2JJXZG+MAeLJ7bVehS0REjNeoagC1K/sRHZ/G2Hm7mHpvS83dWkRK1YX7ubm55ObmAmC328nNzcXhcJxz2x07dnDs2DGcTifp6elMmjSJ8PBwQkNDC22Xnp7OvHnzuP322wGoXLky0dHR2O12oqOjqVxZQwVFRKT06N8inM+HNKdJtQr8t3c9o8MRkTLM6XTyyrydOJ1wdXgA/ZqeffWDiIgYx2a18N8bCo731h5MZkF0osERmUepOrUzbNgw179ffvllAJ555hnq16/Prl27GDduHJ999hkAhw4d4rPPPiM9PR1vb2/q16/PE088gdVauH43ffp0+vTpg6+vLwBdunRhx44dPPzww9SpU+e8lzCKiIgYpUNUMO1rBRU6s3ciI5dgXw8DoxKRsmbetqNsOJwCwHO96mLVwhciIqVOq6sq0qthFX7dfpQ35u+iS50QPMvwvK2lhWFzdpVFqamphISEaM6uMsbo67Cl6KlNzUdtemE74tMY9OU6Hrw2kgc6XlUmhrerTc1HbVq2ZOflc/17K4k7mU3vq0N5d0Djs7ZRmxYv7V/zUZuaT2lp09jkLK5/fyW5dgdPdoviwU41jQ6pzCtVlzGKiIjI2SatPkR6jp23f9vD8z/twJ5/7kv8RURO+XLlIeJOZuPlbuWpHrWNDkdERC6gWkVv7mtXsEDfJ8sOkJiWY3BEZZ+KXSIiIqXcK30bMKBFOAAz1scy/JuNpOfYL/IsESnPutarRNuaQdzf/iqqBnobHY6IiFzE8GsjqezvicPpZGvsSaPDKfNK1ZxdIiIicjZ3m5VX+jYgPNCbdxbvZdmeEwz6Yh2fD2lGlQAvo8MTkVKoXqg/X9/dgnyHZiwRESkLfD3deGfA1VSv6ENYBR3f/Vsa2SUiIlIGWCwWHupck7dvuxp3m4UdCWn0/2wtu4+mGR2aiJRSFosFN5sO90VEyorWVwWp0FVE9O0nIiJShvRtEsZXQ1sQ4OVG/Mls1h1MNjokESklnE4nT8zcwm/RiTidGtElIlKWpWblsT0u1egwyiwVu0RERMqYayKD+HZYa57sFsXga2oYHY6IlBJztyQwd0sCD03fxJ7EdKPDERGRK7Ry3wm6vbuCh6ZtIjsv3+hwyiQVu0RERMqg2pX9Ci1L7XQ6WbxTozlEyqvMXDtvLdwNFIwArVPF3+CIRETkSkUG+5CVl0/cyWy+WHHQ6HDKJBW7RERETODTZQd48JtNPDNnO7l2h9HhiEgJ+2LFQRJSc/B2tzKqR22jwxERkX+haqA397e/CoDPlh8gITXb2IDKIBW7REREyjin00liWg4AszfGMWzKX6Rl5xkclYiUlPiT2Xz+95n/BzpGEqpVWkVEyrxhHa+iSoAnWXkO3l64x+hwyhwVu0RERMo4i8XCC73r8dT1BaM5Vu1PYuDn64g/qbOAIuXBWwt3k53nIKyCF/f9PRJARETKNh8PN0b3qAPAj5vj2RiTYmxAZYyKXSIiIiZgsVgY1iGS9wY0xsPNyu7EdPpPWEN0vFbxETGzjYdTmLslAYCnetTG28NmcEQiIlJU+jQOpWn1CgCMnbcLh0Nzs14qFbtERERM5IarQ5l8dwsq+rhzNC2HQV+sY9W+E0aHJSLFJDffQUSQDy1qBNL76lCjwxERkSJksVh4rlddADbHntTorsvgZnQAIiIiUrRaRFRkxrDW3Df5L9Ky7YRV0Pw9ImZ1TWQQvzzSjuTMXCwWi9HhiIhIEWtaPZAnu0XRtlYwTapVMDqcMkPFLhEREROKDPFl5vBrOJKcRWSIr9HhiEgx8nSzalJ6ERETe7BTTaNDKHN0GaOIiIhJBft60PiMM4DH03N47ddd5NgdBkYlIkVh9sYjmpNPRKQcsuc7SM3SqtsXo2KXiIhIOZBrdzD8m41MXHWIe77eQEqmDpJEyqrY5Cye/2kHN3/yJ0t2HzM6HBERKSFrDiRx00ereWHuDqNDKfVU7BIRESkHPNys9GkchsUC6w4mc/vna4lJzjQ6LBG5Am8u2E2u3UF4BS/aRgYZHY6IiJSQY2k57D2WwS9bE/jrcIrR4ZRqKnaJiIiUE0PbRvDRwKZ4uVvZfzyD/hPWsiX2pNFhichlWHswiV+3HwXgqevr4OluMzgiEREpKb2vDqVFjUAAXpm3E4fDaWxApZiKXSIiIuVI9waVmXpvK4J83TmRkcvgietYvCPR6LBE5BLkO5yMnbcLgFZXVaRnwyoGRyQiIiXJYrHw3A11Adh6JJUfNscZHFHppWKXiIhIOdOkWgVmPnANNUN8yM5z8ND0Tfy5P8nosETkIgompU/DYoHnetXFYrEYHZKIiJSwq8MrcEuzqgCM+20vGTl2gyMqnVTsEhERKYdqBPkwY9g1tIwIpGNUCC0jAo0OSUQuIC3bzrjf9gLQv3k4DasGGByRiIgY5cnutfH1sJGYlsOE5QeMDqdUUrFLRESknAr0cWfS0Ba8d3tj3GynDwk0/4NI6bPraBq5+Q58PW083i3K6HBERMRAlf09efDaSAC+XHmI+JPZBkdU+rgZHYCIiIgYx9PdhucZtxdGH+WrVYf48I6mBPt6GBaXiBTWMqIiix7rQHR8GiF+nhd/goiImNo97SJYvvcEg1pXJzRA3wv/pJFdIiIiAkBKZh5Pzd7G+kMp3P7ZWg6eyDA6JBE5Q5CvBx2igo0OQ0RESgFPdxvf3NeK3leHag7Hc1CxS0RERICCyxrfHdAYHw8bh5IyGfDZWv46nGJ0WCLl2u6jaRxLyzE6DBERKQOcTk1FcYqKXSIiIuLSuU4lpt3Xikp+HiRn5jHkq/X8ui3B6LBEyqV8h5MnZ26l+3srmLdVeSgiIue271gG90/+i1l/HTE6lFJDxS4REREppGHVAGYNv4balX3JtTt49LstfLnyoM4WipSw7zbEsvNoOlm5+dSs5Gt0OCIiUkp9teoQS/ccZ9xve0nLthsdTqmgYpeIiIicpWqgN9/e35q2NYNwOuHthXvYfzzT6LBEyo3UrDzeWbQXgAEtq1Ev1N/giEREpLR6vGstfD1tnMjI5ZOl+40Op1RQsUtERETOKcDbnS+GNKdfs6q80rcBtTSyRKTEfLx0P8mZefh7ufFY1yijwxERkVIs2M+TkZ1rAjBp9SEOJekEpYpdIiIicl4eblbe6NeQW5uHu+7LsTs4ka4Js0WKy8ETGUz+8zAAD3euSbCvh8ERiYhIaXdXmwhqBHmTl+/kjfm7jQ7HcCp2iYiIyAWduZy1w+HkmdnbuG3CGvYmphsYlYh5vfbrbvLynVwV7MOd19QwOhwRESkDPN2sPNuzLgC/7Ujkz/1JBkdkLBW7RERE5JIdTs5kye7jxKZkc/vna1lzoHwfSIkUtQPHM1i65zgAz/Ssg4ebDtdFROTSdK1XiXY1gwAYO28n+Y7yu7iQvj1FRETkkl0V7Mu397ciNMCT1Gw793y9gZ82xxsdlohpRIb4MvehtjzUqSbX1a1kdDgiIlKGWCwWxvSqi7+XGzdcHapil4iIiMilqhvqz8zh11Av1J+8fCdPztrKJ0v343SW3wMqkaJUu4ofj3eLKnQJsYiIyKWoG+rP8lHXMqJTzXI9Orj8fnIRERG5YqEBXky/vxXX1g4GYPyivfz3x2jy8h0GRyZSNqXn2MnJyzc6DBERMQFfTzejQzBcqdkDmzZt4pdffiE2NhabzUbdunUZPHgwQUFB533Ohg0bmDFjBsnJyURERHDvvfdStWpVAGJjY/nkk09ITk6mc+fODBgwwPW8SZMmERkZSadOnYr9c4mIiJiVn6cbnw5uxv/m7uC7DUfYcDiF/ccysDucnDyZQYVsNyr6uFM10NvoUEVKrbiULJIz8/hixUHWHkjisa5R9G9ZzeiwRETEBHbGp/Hp8gP0vroKVSt4l6vjslJT7MrKyqJ3797Uq1cPi8XClClT+Oijj3j++efPuX18fDyffvopI0eOpEGDBvz888+89957vPrqq9hsNr777juuu+462rZtywsvvECrVq2IjIxk9+7dJCQkMHTo0BL+hCIiIubjbrPySt8GRFX2o3F4ALdOWEOO/fToLk83KwsfbV9uDqxELkdcShY93ltZKGeen7uD9lHByhkREflX4lKy6PPxapzAL1sTgPJ1XFZqil1t27YtdLtHjx688MIL5OfnY7PZztp+1apV1K9fn6ZNmwLQp08ffvvtN3bv3k39+vVJTEykQYMG+Pj4ULNmTRITE6levTpTp05lxIgRlzQHQl5eHna73XU7Kyvr331IERERE7JYLNzTLoLtcamFfrQD5NgdPDx901nD6e9pf5Vr8u3svHyGTfnrgu8x9uaG1AjyAWDrkZO8uWD3ebf1dLPxxV3NXbd/2BTH938dOe/2DasG8MzfS3UDvLVwN1tiT553+z5NqtK/Rbjr9vCpG8nMtZ93+ye716Zp9UCg4MDz6dnbzrstwCeDm+H39/5atuc4ny8/cN5twyp48eatV7tuf7nyIEt2HTvv9h2iQhh+baTr9rNzthObnHne7Qu1k93BkInrLhi72uncztdOGTn2s3Im3+EkOTOvXPwQERGR4pOcmcc/Z1PNsTvKzXdMqSl2/dOuXbuoWrXqOQtdADExMdSoUcN1283NjapVqxITE0P9+vWpVq0a27ZtIyAggIMHD9K3b1/mzZtHs2bNCAsLu6QYfv75Z3744QfX7by8PACSk5MLFcGkdEtNTTU6BClialPzUZuaw8mTGee8f2tc2ln3da4ZQFKlgu/4zNx8/jyQfMHXTjiehB/ZAMQmnrzg9t7uVpKSkly398QlXXB7R7690PZbY5L48+D5/ybrV/YiKen0QeK6g0mk5Zx/rqWYxCRq+BYUNBKSsi76WY+fSCLXq+AQ7UDChWOPqOhVKPYdsRfePtjbSlJSBdftjYeS2Hfi/CfzzmynkydT1U5nKM52OnnyJElexX+sqb63eGn/mo/a1HzM3KbnOy4rqe+Y4nSh6a5OKZXFrkOHDjF79mxGjhx53m1ycnLw8fEpdJ+vr69r9NUdd9zB5MmTWbZsGd27d8fNzY3169czZswYJk+e7CqW3XHHHbi5nXs33HjjjfTs2dN1OzU1lVmzZlGxYkUCAgKK4JNKSbmUZJCyRW1qPmrTsq9C9rm/Twe1qkaVAK9C97WpE0JQUMF3qZ/dweNdoy742rWqViLIzxOAhnjxeNfzr/zoZrMU+nvq3MCC7z+OGc5UraJ3oe0HtIqgTa3zF4BaRAQW2n5kl1rk5J1/Yv7GV4US9PdoJzxzL/pZQysF4+VeUGBqHeXO4/nnP1wL9HEvFMuNzfKpWSXwvNvXDfUrtP3d7SNJysg97/ZntlNevtrpTEXRTkdTs5m2Lvas7StUqODa78VNfW/x0v41H7Wp+Zi1Tc93XFaS3zFGsuTm5hqyTviqVauYNGkSAMHBwbz22mtAwYitt956i9tvv5327duf9/nvvvsu1atX59Zbb3Xd98orr9C6dWt69Ohx1vZvvvkmffv25ciRI+zfv5/777+fzz//nKioKLp06XJJMaemphISEsLx48dV7CpDkpKSTNuBlVdqU/NRm5rDueYfKk9zQ5id8rToGZ0zatPipf1rPmpT8zFzmxr9HWM0w0Z2tWvXjnbt2hW6LyYmhjfffJP+/ftfsNAFUL16dQ4fPuy6bbfbiYuLo1q1s1evWbFiBSEhIdStW5fVq1dTq1YtAKKiogq9hoiIiFy5qoHeLHy0PcmZeZw8eZIKFSqUq1V/RC7XmTlzinJGRESKQnn/jik1lzHGxsby5ptvcuutt3LttddedPt27doxf/58Nm/e7FqN0c/Pj7p16xbaLj09nXnz5vHcc88BULlyZaKjo+nYsSPR0dHUrFmzWD6PiIhIeVQ10Juqgd4kednLxRB5kX/rVM6IiIgUtfL8HVNqil2//voraWlpTJs2jWnTprnuf+211wgODmbXrl2MGzeOzz77DICwsDCGDx/ON998Q1JSEhERETz22GNnTWg/ffp0+vTpg6+vLwBdunRhx44dPPzww9SpU+eSL2EUEREREREREZHSz7A5u8oizdlVNpn5OuzySm1qPmpT81Gbmo/a1HzUpsVL+9d81KbmozY1L6vRAYiIiIiIiIiIiBQVFbtERERERERERMQ0VOwSERERERERERHTULFLRERERERERERMQ8UuERERERERERExDRW7RERERERERETENFTsEhERERERERER01CxS0RERERERERETEPFLhERERERERERMQ0Vu0RERERERERExDTcjA6gLHE6nQCkpaUZHIlcjrS0NNzc9KduJmpT81Gbmo/a1HzUpuajNi1e2r/mozY1H7Vp2eXv74/FYjnv42rVy5Ceng5AZGSkwZGIiIiIiIiIiJRPx48fJyAg4LyPW3Jzc50lGE+Z5nA4iI+Px8/P74IVRCk9srKyePzxx3nnnXfw9vY2OhwpAmpT81Gbmo/a1HzUpuajNi1e2r/mozY1H7Vp2aaRXUXIarUSHh5udBhyGdzd3XF3dycgIEAdmEmoTc1HbWo+alPzUZuaj9q0eGn/mo/a1HzUpuamCepFRERERERERMQ0VOwSERERERERERHTULFLTM3NzY2bb75ZK2yYiNrUfNSm5qM2NR+1qfmoTYuX9q/5qE3NR21qbpqgXkRERERERERETEMju0RERERERERExDRU7BIREREREREREdNQsUtERERERERERExDxS4RERERERERETENFbtERERERERERMQ0VOwSkVLFbrfjdGqRWDNyOBxGhyAiUm7k5+cbHYKIiKHUD5ZvbkYHIPJv2O123Nz0Z2wWP/74I1u2bCE0NJRmzZrRsmVLo0OSIpCZmclvv/3G1VdfTc2aNY0OR/6lxYsXExkZqbY0mYyMDKKjo2nVqpXRoUgRmDNnDrt37yYyMpImTZpQt25do0MyFfWD5qR+0FzUD4pGdkmZNWfOHF555RW++OILNmzYYHQ48i/s2rWLp556ii1bttC/f3/8/PyYMGEC6enpRocm/9KcOXN48sknmTNnDomJiQAauVdGHT58mP/7v/9jypQpzJgxA7vdbnRIUkTmzJnDE088wcGDB5WfZVxmZibvv/8+27dvp0OHDpw8eZJ3332XrVu3anRtEVA/aF7qB81D/aCcomKXlDl5eXl8/vnnbNu2jX79+uHn58eUKVNYvHixDjrKILvdTnx8PL169eL555+nXr161KxZk8DAQJKSkowOT67Qhg0bePTRR9m+fTtPPvkkXbt25cCBAwBYLBaDo5Mrcfz4cSIjI3n11VfZt28fa9euNTok+Zd27tzJf/7zH3bu3MkzzzxD//79lZ9l3LFjxzh+/DjPPfcc7du3Z9iwYdSqVYuZM2e6+mC5cuoHzUf9oPmoH5RTdP2XlDknTpzg4MGD/Pe//8Xb25smTZqQnZ3NwoULCQoKolmzZkaHKJfBzc2Na665Bm9vb+x2Ox988AFbtmyhQYMGHDx4EA8PD6pUqaIDjzIkISGB9evXM3DgQNq2bQvAL7/8QsWKFYGCubusVp1rKe3+2U7169enatWqhIaGcsMNN/D999/TuHFj/Pz8DIxS/o29e/eSmprK+++/D0BiYiJ2u52AgAD8/PxwOp3qe0u5f+Zpamoq7u7uxMfHU7VqVQAaNmzIzJkz2bx5M+Hh4Xh5eRkVbpmjftD81A+WfefqB93c3NQPiopdUvrl5+fjcDhwd3cHTl8CFRMTQ506dQCoW7cuS5cuZd26ddSqVYuAgADD4pULW7JkCR4eHrRq1crVpt7e3kDBqD1vb2/ee+89fHx8+Omnn1iyZAmDBg0iKirKyLDlMoSGhjJ8+HCgIH9tNhv169dn0aJFACp0lQELFixgy5YtVK1alXr16tGiRQu8vb1duXrLLbewZMkSfvvtN/r162dwtHKp/vmDoEePHixZssR1OdaGDRuoXLkyiYmJDB8+nNq1a+tHXil2Zp7WrVuXli1b4uPjg4eHB9u2baNKlSrYbDZSUlJo0KABu3btonfv3kaHXWbMmzePVatWERERQb169ejYsaP6QRNQP2guZ+Zp3bp1ufbaa6lQoQLu7u7qB0WXMUrpNm/ePJ599lk++ugj5s2bR25uLl5eXoSFhbFu3TqysrIAOHLkCM2bNyc1NVXzPJVSiYmJvP3220yaNIn58+e75m86xeFw4O3tzYMPPkhAQABubm7ccsstpKSkEBcXZ1DUcimWLFnCqlWryMvLA04XpB0OBzabDYDAwED8/f1JSEgwLE65uLi4OF5++WVWr15Nu3btyM/P55NPPuH48eNAQdueulx88ODBzJs3r1B+ap6T0mvBggWMGzeOb775xjXPpYeHB/379+fXX38lPT2d//3vfwwbNozGjRszY8YMjh07ZnDUci4XytNatWpx9dVXs3z5csaPH88zzzzD0aNHGTp0KDExMZw4ccLo8MuEOXPmsGzZMm677TYqV67MrFmz+PXXX8nOzgZwfd+pHyxb5s2bxwsvvMDnn3/O8uXLgYJ+8LbbblM/WAb9M0+///575s2bR40aNWjdujVLly5VP1jOaWSXlFoLFy5k6dKl3HvvvSQkJLBo0SKOHz/OHXfcQadOnZgxYwbjxo0jPT2d4OBg7rvvPl566SXXgYiULllZWVSoUIFXX32VTz/9lLVr19KrVy/XMGKr1eoaKn5qlc28vDz8/f01EqiUSkxMZPLkyWzbto0aNWoQERFBeHi46wzome1WsWJFjh49iq+vr1HhykXY7Xb27NlDVFQUgwYNAqB9+/bs37+flStX0rdvXywWC25ubjidTq655hoWLFjA/Pnz6datG/PmzaNPnz6uSwakdIiLi+OLL77A4XDQvXt39u3bxyeffMLrr79OSEgILVu2ZNSoUTRo0MCVs3fffTcPP/wwR48epUqVKgZ/AjnThfJ0+fLl9OvXj+7du9O8eXP27duHp6cnLVu2JD4+nvDwcHx8fHRZ1kVkZWWxbds27rjjDpo0aULTpk3x9PRk+/bt+Pj40KlTJ/WDZdCcOXNYs2YNAwcO5NChQ8yaNYv09HQ6d+5MmzZt8PHxoVGjRuoHy4jz5enWrVupWLEiXbt2pWHDhuoHyzkVu6RUstvtREdH07NnT+rVq0e9evXw9fVl6dKlzJ8/n5tuuolq1aoRGxuLw+GgSZMmZGZmEhISgpeXlzqwUigiIoIbb7yRsLAwrr/+eubOnUvDhg1dl6LC6YnL3dwKuqZp06bh7u5OgwYNDIlZLuxiBcwz1ahRA09PT7Zs2UL79u0NiFYuxs3NjfDwcNfS3KdGcPn4+LjmWzvlVB87ZMgQXnrpJVasWEH79u31A6+UuZQCps1mcxW6Tp1oyMjIICgoyODo5VwulKfBwcEA2Gw2QkNDCQ0NdT3vp59+onr16gQGBpZ4zGWNt7c32dnZrhGtAJ06deL48eNER0fTqFEjgoODXfmifrD0O19hZNu2ba4CZsOGDdUPliEXytNNmzYRFRWlflBU7JLSyc3NjZSUFFJSUlz3NWnShBMnTrBx40aaNWtGtWrVCAwMdBVIpk6dSqVKlQgLC1Ohq5QKCwsDCn5srVixguXLlxMaGkpAQIBr2P/OnTvZuXMnixcvpmbNmjz88MP6UiqlLqWAeUpubi5VqlRxXfohpdOZc+NZrVasVitZWVlUqlSp0HZWq5UlS5YwadIkWrVqxeDBg5WnpdClFjBPjWQ4daJh5syZ+Pn5UatWrRKOWC7FxfL0VHvm5OSwdOlS5syZQ61atVwFT7mwnJwc6tevz549e2jXrp1rnq5GjRrx66+/EhsbS3BwMG5ubuoHy4hLLWA6nU71g2XExfI0Li6OSpUqqR8s53RtkJRarVq1Ytu2bWRkZAAF19TXqVMHb29vdu3aBRSMLli4cCEjRowgNTWVQYMGqdBloDPnqTjfvx0OBwC33nor27dvd7WlxWLBYrFQqVIlvLy8+M9//sMTTzyhA8dS7swCZsWKFVm+fDmpqalA4Xb39/cnPT1dxa5S4FLyFAp+MMfFxZGWluZqZzidw1WqVOGxxx5j5MiRytNS7NTZbShoUzc3t3MWMGNjY/npp5946KGHSEpKYvjw4brs2ED/Nk8BPD09XXk6atQo/P39izfoMuZcc2s5HA48PT2pXr06mZmZbN682fVY06ZNSU1NLXQiVv1g2XBmYeTUfL+nCiMpKSnExsYCBXMAqx8sXf5tnnp6ehIaGqp+sJzSyC4xzJmXGp7571OrpNSoUYOdO3eybNkyevXqBUDNmjXJzc11fVFZrVZq1arF448/fs7RJFIyUlJSmD17Nl5eXtSpU4fmzZtjtVpxOBw4nU7XJOWA6/6oqCiuvvpq/vzzT/z8/Fi8eDHt2rWjefPmrvYW450vT8+Vs7feeisff/wxjRs3plWrVlgsFvLy8lyrbj777LOuVayk5F1Onp7y119/ERkZSWBgICdOnODHH3+kW7du1KhRg/r16xvwKeRcLiVP4cKFkVOr+D366KOukWBS8oo6T5s0aWLApyi90tLS2L17Ny1atDjrsVOrBwO0bNmSffv2sWbNGmrVquUqDleuXLnQj2/1g6XLuaYxObMwsmHDBjZv3kybNm2AgsLIjBkzSE5OBgoKI15eXuoHDVbUedq4ceOSCVxKHY3skhKXkpLCxIkTmT59OuvXr8fhcGCxWHA4HOTn57uG39etW5eoqChWr17tOuMCUKFChUIdWK1atVToMtCePXt48cUXcTqd5Ofn8/333zNp0iSg4IfVqS+kKVOmsGnTpkLP7devHxs2bOCNN97AarXStGnTkg1ezutiefrPH9D/LGDu2LGDjz76iK1bt7q2U6HLOFeapykpKdSuXZsff/yR//73v+Tm5lKtWjUDPoGcy+Xk6Sn/LIxMnDiRmJgYgoKC6Nmzp37gGUh5WrwWL17MqFGj+Oyzzzhw4IArV045tX9nzpzJ8ePH6d69O+7u7owbN44tW7Ywffp0YmNjVeAqZdLS0lwrzP7Tmb8rWrZsSWBgIGvWrCm0wmLlypVd/65UqRI9evRQP2gg5akUJRW7pERd6oHc5MmT2bdvH127diUiIoK33nqLP/74g2nTphEdHa0KfSkSHR1NvXr1uO+++xgyZAgPPfQQy5YtY8WKFUDBHFzDhw9n//79hIeHAwVtvXnzZp566ikaNGjAW2+9xUMPPaRVF0uJoipgWiwWFTBLiSvJU6fTyerVq5k2bRq7du3ixRdf5MEHH1SelhJFWRg51eZiLOVp8Tl48CAHDhzgpptuok2bNvz8889A4VWDt27dyvDhw9m9ezcVKlSgevXq3HfffdSrV4+lS5cSExPD6NGjtTJfKaLCiLkoT6Wo6TJGKVFnHsgBxMTE8Pzzz1OnTh06dOjAzp07eeeddwgLC+OGG27Az8+P++67jx9++IF9+/aRlpbGs88+W2hlDSl5py5bczgcZGRk4O3tjd1ux2q1Ur16dfr27euarNzpdDJixAhX0ePUc0NCQnjooYdUDCmFLjVPq1atSs+ePYHTBcyPPvqIBg0acM8995w1J5CUrH+Tp1BQFGnbti3NmjWjYcOGxn0QOacrydNThZGMjAwaNGjAiy++qO9TgylPi9ep/VupUiW6dOlCeHg4u3bt4vvvv2fNmjVcc801OBwOHA4HaWlpZx2veHp6cvfdd5Obm4uHh4exH0YKObMwcuzYMX7++WceeeSRswojH374ITVq1KB79+4EBgZy3333MX36dJYuXUpWVpYKI6WA8lSKi4pdUuz+zYHcqeuyb775ZtfriDGysrJYt24d1157LVarFafT6Ro9cOLECbKzs/Hz8wMKRvf89ttvbNy4keuuu871Gme2YXh4uEYTlCIqYJpDUeTpqX63YsWK3HnnnUZ9FDkHFUbMQXlavP65fwF8fX1dq+rVrFmTRo0a8dtvv9GyZUtsNhtWq5V27dq5XuOfx5z6AV16qDBiDspTKQmqHEixyMrKYtmyZQDnPZA71Tn169ePtLQ0Nm7cSP369Qt9If1zYnMxxurVq3n66aeZOHEi69evB04vYd+9e3d27NjBzp07gdOrpnTv3p01a9YUeh21YelSVHl6ZgFThS7jFFWenmsCbDFOUeRpfn4+gKswokKXcZSnxetc+/fU3/8p/v7+ron/T10mdaZTOSalxz/7QThdGPHy8ipUGDk1T5ebmxvt2rU75/EKqDBiJOWplBT9hUiRU2HEXJKSkjhw4AA9e/akT58+zJw5EwB3d3fsdjtBQUFcd911zJ49m/j4eNeEyPn5+a7Vvs6cP0FKB+WpuShPzUmFEXNRnhav8+1fm81WaGEjKDg507x5czZu3EhWVhbr16935c25FnYQ46gwYi7KUylJynopUjqQMx9/f386dOhAp06d6NixIxaLhR9//LHQNoMGDcLLy4vZs2e7VrnZvHlzoQnppfRQnpqP8tR8lKfmozwtXhfav2f+iHY6nXh7e9OhQwfc3NwYOXIkn3/+uUb6lEIqjJiP8lRKkr4xpUjpQK7s+udBwynu7u7UqFEDX19fgoOD6dmzJwsXLiQ9PR03Nzdyc3MBuPfee6lUqRILFizglVde4eqrr6Z79+4l+RHkEilPyy7lafmhPC27lKfF60r2r9VqdY0GOrVi38SJE9m3bx+33norEyZMoFmzZiX5MeQSqDBSdilPpTSw5ObmnvsvUeQCnE7nRc+SOBwOli1bxsyZM3njjTfw8/NzTQYZGxvLqlWr2LlzJydOnKB9+/YMGDCghKKXy/HPtk5LS+Pjjz8mICCAESNGnLV9SkoKXl5eeHl5lWSYcg7K0/JDeVp2KU/LD+Vp8bqc/btnzx7Wrl3LTTfdREBAQEmHKv9wpf3gqYUaTj3+4YcfsnHjRm677TZ69+5dEqHLZVKeSklSsUuKlA7kyp5t27bx8ccfM2DAADp37uy63+l0nnOOA4fDwbZt25gwYQLPP/88R48eJS4ujl69epVw5HKllKdlj/K0/FGelj3K0+JVFPv3yJEj3HDDDVrhu4xQYaTsUZ5KaaK/Hrks27Zt46GHHmLJkiWF7nc6nTgcjrPOyvj6+tKrVy+2bdtGQkICmzdv5tdff3U9HhgYqANzgyQlJTF+/Hg+++wz+vXrd9YXksViwWq1kpSUxOLFi13Diq1WK40bN6ZOnTo888wzvPfeexomXsooT81DeWpeylPzUJ4Wr6Lcv56enq7HxHhF0Q/OmzcPgFq1ajF48GAVugyiPJXSyM3oAKRsSEpKYtKkSRw8ePC8HZjFYiEpKYmNGzfSuXNnbDbbWR2Y1Wpl8ODBxn0QAQqWcH7ppZcIDg5m/PjxuLkVdAWnzqCcOriYPn06CxYs4NZbb3UNE3c6nXz55Zds3LiRvn370q9fP8M+hxSmPDUX5ak5KU/NRXlavLR/zak4+kEVRoyjPJXSSsUuuSh1YObj7e1Nu3btiI+PJzs7m/Xr17N+/XoCAwOpV68erVu3JiEhgdzcXN5++21CQkJcz01LSyMoKIi33nqLSpUqGfgp5EzKU/NRnpqP8tR8lKfFS/vXfNQPmo/yVEorzdkll2TGjBnEx8dz//33n7cD++OPP+jdu3ehDiw1NZVFixbRsWNHdWAG2r9/P3FxcVSqVIm6desCkJeXx5gxY8jKyiIoKIjrr7+eAwcOsGfPHpo2bVroAOLU8vU6a1a6KU/LNuVp+aA8LduUp8VL+7d8UD9YtilPpaxQsUvOog7MPDIzM5k0aRLR0dE0bdqUP//8k379+tGxY0cCAgJYt24d+/fv5/bbb3c9Z/HixWzfvp3BgwcTHBysySFLKeWpeShPzUt5ah7K0+Kl/Wte6gfNQ3kqZY0uYxSXf3ZgkyZNKtSBDRgwoFAH1r59e1cHduLECXVgpUx2djbTpk3DYrHw3nvvYbPZqFOnDr/++itt27YFoFWrVjRo0AAoGFbu7e1NcHAw0dHRriHjas/SRXlqLspTc1KemovytHhp/5qT+kFzUZ5KWaRilwDqwMzI3d2devXqER4ejs1mw+l0cu211/L999+TlJREUFAQULCyDRRcbw8QExNDmzZttJpNKaQ8NR/lqfkoT81HeVq8tH/NR/2g+ShPpSxSsUsAdWBmcOjQIdasWUNYWBgNGzYkKCiI1q1bu5Yxt1gsxMbG4ufnR5UqVVyr3TgcDpKTkzlw4ADz5s0jMzOTYcOG6QCjFFKeln3KU/NTnpZ9ytPipf1rfuoHyz7lqZiBil3llDowczjVLvPmzePHH3+kY8eOzJs3j7Vr19K1a1eaNm1Kfn4+FosFq9VKTEwMvr6+eHp6ula7ATh+/Dg///wzzZs3p0+fPgZ+IjmT8tQclKfmpjw1B+Vp8dL+NTf1g+agPBWzUbGrHFEHZj4WiwWn00l0dDT3338/rVq1IiEhgRUrVjBlyhQaN26MzWYjLy8Pq9XKjh07iIiIcB18bN++ncjISOrWrct///tf1/LPYhzlqfkoT81HeWo+ytPipf1rPuoHzUd5Kmajsnk58s8O7M477+TRRx8lIiKCKVOm4HA4sNls5OfnA5yzA8vOznZ1YPpCMs7Jkyex2+0AxMfHc+zYMUJCQnA6nYSGhtKtWzd8fHyYOnUqUDCcHODEiRN07tyZHTt28Mgjj/D777/jdBYsyKovpNJBeWoeylPzUp6ah/K0eGn/mpf6QfNQnopZ6a+wHDh58iS+vr64ubmdtwPbvHkzU6dO5a677irUgQ0aNIgdO3bw8ccfU6dOHe69915AHZhRjh49yqRJk1zLMPfu3ZvGjRtjt9vZt28fkZGROBwOAgMD6dOnD19//TU33ngjQUFB7N+/nwMHDjB+/Hjy8vIYOHAg7du3N/gTySnKU/NQnpqX8tQ8lKfFS/vXvNQPmofyVMxOPYuJqQMzl2XLlvHDDz/Qpk0bevTowYwZM1i0aBEVK1akW7duzJ07l27durnmOYiMjOSqq65i586dtGvXDqfTSX5+Pp07d+amm24y+NPIKcpTc1GempPy1FyUp8VL+9ec1A+ai/JUygNdxmhSy5Yt44033iAyMpIRI0YQFBTEokWLiImJcXVgwDk7MKBQB/b+++/rC6kUOHbsGDfeeCMDBgwgMDCQO++8kyNHjpCVlUWHDh3w8vLi22+/dW0fEBBAYmKia6WbsLAw3nvvPX0hlSLKU/NRnpqP8tR8lKfFS/vXfNQPmo/yVMoDFbtMSh2Y+XTp0oWWLVsCkJubi4+PD15eXmRlZeHv78+gQYOYP38+ixYtIj09nf379+Pj40NwcDCAa3spPZSn5qM8NR/lqfkoT4uX9q/5qB80H+WplAe6jNGkunTp4rr+/Xwd2DvvvENISAht2rQhNjb2rA5MSpegoCCg4OyYh4cHCQkJ5OXlUadOHZxOJ02aNGHgwIH89ddfLF26lBMnTjBw4ECqVatmcORyPspT81Gemo/y1HyUp8VL+9d81A+aj/JUygMVu0xKHZh5nVqueffu3VSqVAlvb2/XYz179uT6669n//791KpVy6gQ5RIpT81LeWoeylPzUp4WL+1f81A/aF7KUzEzFbtMTh2Y+TgcDqxWK7t27aJhw4YA/PHHH6xdu5YBAwYQGRmp9ixjlKfmozw1H+Wp+ShPi5f2r/moHzQf5amYmYpdJqcOzHysVit2u53k5GSCg4MZO3YsycnJDB06lMjISKPDkyugPDUf5an5KE/NR3lavLR/zUf9oPkoT8XMVOwyOXVg5pSQkEB0dDRHjhzh+uuv54YbbjA6JPkXlKfmpDw1F+WpOSlPi5f2r7moHzQn5amYlYpd5YA6MPMJDQ1l4MCBdO3aFXd3d6PDkSKgPDUf5an5KE/NR3lavLR/zUf9oPkoT8WsLLm5uU6jg5DiZbfbWbRokTowkVJMeSpS+ilPRaS8Uz8oImWFil0iIiIiIiIiImIaVqMDEBERERERERERKSoqdomIiIiIiIiIiGmo2CUiIiIiIiIiIqahYpeIiIiIiIiIiJiGil0iIiIiIiIiImIaKnaJiIiIiIiIiIhpqNglIiIiIiIiIiKmoWKXiIiIlFqbN2+mR48epKenl/h7jxo1ik8++aTE3/eUmJgYbr/9djIzM133rVy5krvvvpuePXsaGpvZ9ejRg5UrV/7r1xkyZAizZ88ugohOGzt2LLNmzSrS1xQRETEbFbtERETkglJSUnj//fcZPHgwvXv35vbbb+fZZ59l+/btxf7eDRo04Ntvv8XX17fY3+vfKurC3MSJE+nbty8+Pj6u+9577z06duzIN998w9ChQ4vkfYwwefJkHnzwQaPDKDILFy6kX79+Z93/wQcfcMMNNxTpew0aNIjp06eTkZFRpK8rIiJiJm5GByAiIiKl28svv4zdbmf06NGEhYWRnJzMxo0bSU1NLfb3dnd3JygoqNjfp7RJTExkzZo1jBw50nVfVlYWKSkptGjRguDg4Ct+7by8PNzd3YsizBJX1mIPDAws8teMjIwkLCyMxYsX06dPnyJ/fRERETNQsUtERETOKz09nW3btvH222/TuHFjAKpUqUK9evXO2u6zzz5j9erV5OXlUbt2bR588EFq1aoFwL59+/j000/ZvXs3FouFqlWr8thjj1GnTh2OHj3Khx9+yPbt28nLyyM0NJRhw4bRunVrNm/ezOjRo5k9ezZ+fn4ALF++nMmTJxMXF0dQUBB9+/bltttuc8UyZMgQbrjhBuLi4li2bBl+fn4MGjSI3r17n/dzZmVl8cEHH7BixQp8fHwKvd4pixYtYs6cOcTGxuLl5UXTpk158MEHqVixIgkJCYwePRqAW265BYDu3bszevRoHA4HM2bMYN68eSQnJ1OtWjUGDRrEtddee954li5dSs2aNQkJCQFw7QeAp556CoC33nqLJk2aXNL+uP7664mLi2PVqlW0b9+e0aNHs23bNiZOnMju3bsJCAigffv23HvvvXh7ewOQm5vL5MmT+f333zl58iSVKlXi9ttvp1evXuTn5/Puu++yadMmkpOTqVy5MjfddFOh0U2bN2/miy++4ODBg7i5uREREcGzzz7L5s2bmTp1KlBwuSAUXDJ66t9neuutt0hPT6du3br89NNPuLu7M2XKFBITE/nss8/YsGEDVquVRo0aMWLECEJDQy/43lWqVAFg7ty5zJo1i2PHjhEaGsqgQYPo1q3bOdviXH+D+/btY8SIEUyePJmjR4/y9ttvF/o8d955J3fddRdDhgyhX79+rr+JxMREPvroIzZu3IjVaqVly5aMHDmSihUrAgUj3latWsVtt93G119/TVpaGq1ateLxxx8vNMKvTZs2LFmyRMUuERGR81CxS0RERM7L29sbb29vVq5cSb169fDw8Djndv/3f/+Hp6cnY8eOxdfXl19++YWnn36aiRMnEhAQwOuvv05UVBSPPPIINpuNffv2YbPZAPjwww/Jy8tj3LhxeHl5cejQIby8vM75Prt372bs2LHceeeddO7cmejoaD744AMCAgIKFUtmzZrF0KFDGThwIMuXL+eDDz6gcePGVK9e/Zyv+/nnn7NlyxZeeuklAgMDmThxInv37nUV6wDsdjtDhw6levXqpKSkMGHCBN5++23Gjh1LpUqVeOGFF3j55ZeZOHEiPj4+eHp6AvDtt9+yePFiHn30UcLDw9m6dStvvPEGgYGBrgLiP23bto06deq4bjdo0ICJEydy77338sILL9CgQQP8/f0va3/ceeed3HnnnQDExcUxZswY7r77bp588klSUlL46KOP+Oijjxg1ahRQUGiKjo7moYceolatWiQkJHDy5EkAnE4nlSpV4vnnn8ff35/o6GjeffddgoKC6NSpE/n5+fzvf/+jV69ePPvss9jtdnbu3InFYqFTp04cPHiQdevW8cYbbwBc8DLVTZs24evry+uvv+5qhzFjxlC/fn3Gjx+PzWbjm2++YcyYMUyYMAGr1Xre9wZYsWIFn3zyCQ8++CDNmzfnzz//5O233yYkJISmTZueN47zadCgASNGjODrr79m4sSJAK6C4ZkcDgcvvvgi3t7evP322+Tn5/Phhx8yduxYV7EMID4+nlWrVvHyyy+Tnp7O2LFjmTFjBvfcc49rm7p16zJ9+nRyc3PPm5MiIiLlmYpdIiIicl42m41Ro0bx7rvv8ssvvxAVFUXjxo3p3LkzNWvWBAoKM7t27eK7775z/fB+4IEHWLVqFcuXL6d3794cO3aM/v37U6NGDQDCw8Nd75GYmEiHDh2IjIwEICws7LzxfP/99zRt2tRVtKlWrRqHDh1i5syZhYo7rVu3do16uf3225k9ezabN28+Z7ErKyuLBQsW8PTTT9OsWTMARo8ezeDBgwtt17NnT9e/w8LCeOihh3j44YfJysrC29sbf39/oODStVMjgHJzc5k+fTpvvPEGDRo0cD1327Zt/PLLL+ctdh09epTatWu7bru7u7suifP393dd2nmp+6Np06aFRnuNHz+e6667zjXiKDw8nIceeohRo0bxn//8h8TERJYuXcrrr79O8+bNXXGf4ubmxl133VVof+zYsYOlS5fSqVMnMjIyyMjIoE2bNlStWhXA1fYAXl5e2Gy2S7pE1cvLi8cff9x1+eKiRYtwOBw88cQTrgLWqFGjuOWWW9i8eTN16tS54HvPmjWL7t27u/4+brvtNnbu3MmsWbOuqNjl7u6Or68vFovlgp9n48aNHDhwgMmTJ1O5cmWgYJTesGHD2LVrF3Xr1gUKimKjRo1yjeTq2rUrGzduLFTsCg4OJi8vj+TkZNdoNRERETlNxS4RERG5oI4dO3LNNdewdetWduzYwfr16/nuu+944okn6NGjB/v37yc7O/usS/9yc3OJj48HCi7te+edd1i8eDHNmjXj2muvdRUibr75Zt5//302bNhA8+bN6dChg6uQ9k8xMTG0bdu20H0NGzZkzpw55Ofnu0aLnSqcAa4iREpKyjlfMy4ujry8vEKXZgYEBFCtWrVC2+3evZspU6awf/9+0tPTcTgcQEGxLiIi4ryvnZOTwzPPPFPofrvdXmjU2D9d6oidS90fZ44SA9i/fz8HDhzg999/d93ndDpxOBwkJCRw4MABrFbreYtxAD/99BPz58/n2LFj5OTkFPpMp0aWPfvsszRv3pzmzZtz7bXXXtFcY1dddVWhebr2799PXFwcffv2LbTdqb+3li1bXvC9Y2Jizpo0/tQ+K06HDx+mUqVKrkIXQEREBH5+fhw+fNhV7AoNDS10yeK5/nZPjRrMyckp1phFRETKKhW7RERE5KI8PDxo0aIFLVq04M4772T8+PFMnjyZHj16kJWVRVBQEG+99dZZzzs1wumuu+7iuuuuY82aNaxbt44pU6bw7LPP0qFDB3r16kWLFi1Yu3YtGzZs4Ntvv+WBBx7g5ptvvuJ43dzOPsQ5VZy6EllZWYwZM4YWLVrwzDPPUKFCBRITExkzZgx2u/2CzwN45ZVXzir0XGii9YCAgCJb1RE467LQrKwsbrjhhnPu48qVK3PkyJELvt4ff/zBZ599xgMPPECDBg3w9vZm5syZ7Ny507XNqFGjuPnmm1m3bh1Lly5l0qRJvP7669SvX/9fxZ6dnU3t2rXPKiDC6Qnhi+q9AdfoMafT6brvQm3+b50qUJ75/me+N+BaHKJChQrFFoeIiEhZpmKXiIiIXLYaNWqwatUqAKKiokhKSsJms7kmCD+XatWqUa1aNW699VZeffVVFi5cSIcOHYCCAsuNN97IjTfeyJdffsmvv/56zkJM9erV2b59e6H7tm/fTnh4+FlFgktVtWpV3Nzc2Llzp2vUTVpaGrGxsa6RTTExMaSmpnLfffe5ttm9e3eh1zlVYDuzqBYREYG7uzuJiYkXHCX1T1FRURw6dOii213p/oiKiuLw4cOFLic9U2RkJE6nky1btrguYzxTdHQ0DRo0KDRB+qlRfP98n6ioKO644w4effRRfv/9d+rXr4+7u/sVFx+joqJYunQpgYGBF5zr63zvfWqfnXmZ5/bt2887Ou9UAS0pKcl1qeq+ffsKbePm5nbRz1OjRg2OHTtGYmKi62/o0KFDpKenn/e9z+fgwYOEhISo2CUiInIeVqMDEBERkdIrNTWV0aNHs2jRIvbv3098fDzLli1j5syZrsvnmjdvToMGDfjf//7H+vXrSUhIYPv27Xz11Vfs3r2bnJwcPvzwQzZv3szRo0fZvn07u3fvds2j9Mknn7B+/Xri4+PZs2cPmzdvLjTH0pluu+02Nm3axNSpU4mNjWXhwoX89NNP9O/f/4o/o7e3Nz179uTzzz93zav09ttvY7WePkyqXLky7u7u/Pjjj8THx7N69WqmTZtW6HWqVKmCxWLhzz//JCUlhaysLNfKjp9++ikLFy4kLi6OPXv28MMPP7Bw4cLzxtSiRQt27NhBfn7+BWO/0v1x++23Ex0dzYcffsi+ffs4cuQIq1at4sMPPwQKLqXr3r0748aNY+XKlcTHx7N582aWLl0KFBQId+/ezfr164mNjWXSpEns2rXL9frx8fF8+eWXREdHc/ToUdavX8+RI0dc7VqlShUSEhLYt28fJ0+eJDc394Lxnum6664jICCA//3vf2zdutUV20cffcSxY8cu+t79+/fnt99+Y+7cuRw5coRZs2axYsWKc67AeeqzVqpUiSlTpnDkyBHWrFnDrFmzCm1TpUoVsrKy2LhxIydPniQ7O/us12nevDmRkZG8/vrr7Nmzh507d/Lmm2/SuHHjsy4zvZht27bRokWLy3qOiIhIeaKRXSIiInJeXl5e1KtXj9mzZxMfH4/dbqdSpUr06tWLO+64Ayi4zOqVV17hq6++Yty4cZw8eZKKFSty9dVXExgYiNVqJTU1lTfffJOUlBQCAgLo0KGDa4Jzh8PBhx9+yLFjx/Dx8aFVq1Y8+OCD54yndu3aPPfcc0yePJlp06YRFBTEXXfdVWiUzpUYNmwYWVlZvPDCC/j4+HDrrbeSkZHhejwwMJBRo0YxceJEfvjhB2rXrs2wYcN48cUXXduEhIQwZMgQJk6cyLhx4+jWrRujR4/m7rvvJjAwkG+//ZaEhAR8fX2pXbs2AwcOPG88rVu3xmazsXHjRlq2bHne7a50f9SsWZO3336br776iieeeAKn00nVqlXp1KmTa5v//Oc/TJw4kQ8++IC0tDQqVarkavPevXuzb98+xo4di8VioXPnztx0002sW7cOKPi7iYmJ4bfffiMtLY2goCD69OlD7969AejQoQMrVqxg9OjRpKenM2rUqEtuQy8vL8aNG8cXX3zByy+/TGZmpmslRR8fH3Jzcy/43u3bt2fEiBHMmjWLTz75hNDQUEaNGkWTJk3O+X5ubm6MGTOG999/n+HDh1OnTh3uvvtuXnnlFdc2DRs25MYbb2Ts2LGkpqZy5513FprAHwry5KWXXuKjjz7iySefxGq10rJlS0aOHHlJn/uU3NxcVq1axauvvnpZzxMRESlPLLm5uc6LbyYiIiIiJemnn35i9erVvPbaa0aHIqXI3LlzWblyJa+//rrRoYiIiJRaGtklIiIiUgr17t2b9PR0MjMzC63OJ+Wbm5vbZY8GExERKW80sktERERERERERExDE9SLiIiIiIiIiIhpqNglIiIiIiIiIiKmoWKXiIiIiIiIiIiYhopdIiIiIiIiIiJiGip2iYiIiIiIiIiIaajYJSIiIiIiIiIipqFil4iIiIiIiIiImIaKXSIiIiIiIiIiYhoqdomIiIiIiIiIiGn8P4XDq0j6p+4jAAAAAElFTkSuQmCC", + "image/png": 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01baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.019208800.5000.2500.3330.5000.3758101baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov1.900288800.5000.5000.5000.5000.68881
11baa10y_change_1bNaiveFalse0—last_value_naive0.030008801.0000.5000.6670.7500.5628111baa10y_change_1bNaiveFalse0—last_value_naive3.000008801.0000.5000.6670.7500.56281
25baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037028800.6670.3330.4440.3750.3338125baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov3.404858800.7500.5000.6000.5000.41781
35baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.3338135baa10y_change_5bNaiveFalse0—last_value_naive6.500008800.6670.6670.6670.5000.33381
421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.079228800.0000.0000.0000.5000.37581421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov8.193818800.0000.0000.0000.5000.62581
521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.25081521baa10y_change_21bNaiveFalse0—last_value_naive16.500008800.3330.5000.4000.2500.25081
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 horizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalrun_numberhorizontargetmodeluses_covariatesn_covariatescovariatespredictor_idmean_crpsn_scoresn_predictionsskipped_originsdir_precision_updir_recall_updir_f1_updir_accuracydir_roc_auc_prob_updir_n_evalrun_number
01baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.019208800.5000.2500.3330.5000.3758101baa10y_change_1bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov1.900288800.5000.5000.5000.5000.68881
11baa10y_change_1bNaiveFalse0—last_value_naive0.030008801.0000.5000.6670.7500.5628111baa10y_change_1bNaiveFalse0—last_value_naive3.000008801.0000.5000.6670.7500.56281
25baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.037028800.6670.3330.4440.3750.3338125baa10y_change_5bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov3.404858800.7500.5000.6000.5000.41781
35baa10y_change_5bNaiveFalse0—last_value_naive0.065008800.6670.6670.6670.5000.3338135baa10y_change_5bNaiveFalse0—last_value_naive6.500008800.6670.6670.6670.5000.33381
421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov0.079228800.0000.0000.0000.5000.37581421baa10y_change_21bLightGBM + covTrue10vix_level_l1b, vix_log_ret_1b_l1b, ust10y_level_l1b, ust2y10y_spread_l1b, fed_funds_level_l1b, cpi_mom_logdiff_l1b, unemployment_rate_l1b, oil_log_ret_1b_l1b, dollar_index_log_ret_1b_l1b, nasdaq_log_ret_1b_l1bdarts_lightgbm_cov8.193818800.0000.0000.0000.5000.62581
521baa10y_change_21bNaiveFalse0—last_value_naive0.165008800.3330.5000.4000.2500.25081521baa10y_change_21bNaiveFalse0—last_value_naive16.500008800.3330.5000.4000.2500.25081
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -7141,7 +7819,7 @@ "showlegend": false, "type": "bar", "x": { - "bdata": "xKCYBYKpkz+4HoXrUbiePw==", + "bdata": "oiL29opn/j8AAAAAAAAIQA==", "dtype": "f8" }, "xaxis": "x", @@ -7163,7 +7841,7 @@ "showlegend": false, "type": "bar", "x": { - "bdata": "79L/j6Xzoj+qcD0K16OwPw==", + "bdata": "8g1kSiE9C0AKAAAAAAAaQA==", "dtype": "f8" }, "xaxis": "x2", @@ -7185,7 +7863,7 @@ "showlegend": false, "type": "bar", "x": { - "bdata": "9gN6wuBHtD8ihetRuB7FPw==", + "bdata": "AAauizpjIEACAAAAAIAwQA==", "dtype": "f8" }, "xaxis": "x3", @@ -8099,6 +8777,361 @@ "plotly_display_multivariate_backtest_leaderboard(EVAL_DF)" ] }, + { + "cell_type": "markdown", + "id": "33ecd208", + "metadata": {}, + "source": [ + "## 8. Comparison between with HYOAS and without generated by ChatGTP 5.6\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "4426ffda", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " baa10y_change_1b mean CRPS = 0.92892 (6 predictions)\n", + " baa10y_change_5b mean CRPS = 4.31207 (6 predictions)\n", + " baa10y_change_21b mean CRPS = 7.13714 (6 predictions)\n" + ] + } + ], + "source": [ + "\n", + "BASELINE_PREDICTOR_ID = lightgbm_cov.predictor_id\n", + "# Expected: \"darts_lightgbm_cov\"\n", + "\n", + "if BASELINE_PREDICTOR_ID not in results_by_predictor:\n", + " raise RuntimeError(\n", + " \"The existing LightGBM + covariates result is unavailable. \"\n", + " \"Run the main backtest section above first.\"\n", + " )\n", + "\n", + "without_hyoas_results = results_by_predictor[BASELINE_PREDICTOR_ID]\n", + "for task_id, result in without_hyoas_results.items():\n", + " print(\n", + " f\" {task_id:20s} \"\n", + " f\"mean CRPS = {result.mean_score:.5f} \"\n", + " f\"({len(result.predictions)} predictions)\"\n", + " )\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "7f81d499", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "HYOAS model covariates: 12\n", + "Added HYOAS covariates: ['hyoas_observed_change_1b_bps_l1b', 'hyoas_hyg_dgs3_proxy_change_1b_bps_l1b']\n" + ] + }, + { + "data": { + "text/html": [ + "
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experimenttaskmean_crpspredictions
0LightGBM without HYOASbaa10y_change_1b0.9289246
1LightGBM without HYOASbaa10y_change_5b4.3120666
2LightGBM without HYOASbaa10y_change_21b7.1371426
3LightGBM with HYOASbaa10y_change_1b0.9723996
4LightGBM with HYOASbaa10y_change_5b4.2549506
5LightGBM with HYOASbaa10y_change_21b8.0347776
\n", + "
" + ], + "text/plain": [ + " experiment task mean_crps predictions\n", + "0 LightGBM without HYOAS baa10y_change_1b 0.928924 6\n", + "1 LightGBM without HYOAS baa10y_change_5b 4.312066 6\n", + "2 LightGBM without HYOAS baa10y_change_21b 7.137142 6\n", + "3 LightGBM with HYOAS baa10y_change_1b 0.972399 6\n", + "4 LightGBM with HYOAS baa10y_change_5b 4.254950 6\n", + "5 LightGBM with HYOAS baa10y_change_21b 8.034777 6" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# ---------------------------------------------------------------------------\n", + "# Build a service containing baseline covariates plus HYOAS\n", + "# ---------------------------------------------------------------------------\n", + "\n", + "HYOAS_COVARIATES = list(\n", + " dict.fromkeys([\n", + " *DEFAULT_COVARIATE_SERIES_IDS,\n", + " *HYOAS_OPTIONAL_COVARIATE_SERIES_IDS,\n", + " ])\n", + ")\n", + "\n", + "svc_hyoas = build_baa10y_multivariate_service(\n", + " windows=(1, 5, 21),\n", + " include_covariates=True,\n", + " covariate_series_ids=HYOAS_COVARIATES,\n", + " start=DATA_HISTORY_START,\n", + " refresh=REFRESH_CACHE,\n", + ")\n", + "\n", + "registered_series = set(svc_hyoas.series_ids)\n", + "\n", + "missing_hyoas = [\n", + " series_id\n", + " for series_id in HYOAS_OPTIONAL_COVARIATE_SERIES_IDS\n", + " if series_id not in registered_series\n", + "]\n", + "\n", + "if missing_hyoas:\n", + " raise RuntimeError(\n", + " f\"Missing HYOAS covariates: {missing_hyoas}\"\n", + " )\n", + "\n", + "HYOAS_COVARIATES = [\n", + " series_id\n", + " for series_id in HYOAS_COVARIATES\n", + " if series_id in registered_series\n", + "]\n", + "\n", + "print(f\"\\nHYOAS model covariates: {len(HYOAS_COVARIATES)}\")\n", + "print(\n", + " \"Added HYOAS covariates:\",\n", + " list(HYOAS_OPTIONAL_COVARIATE_SERIES_IDS),\n", + ")\n", + "ab_hyoas_predictor = DartsLightGBMPredictor(\n", + " lags=LAGS,\n", + " lags_past_covariates=LAGS,\n", + " covariate_series_ids=HYOAS_COVARIATES,\n", + " num_samples=NUM_SAMPLES,\n", + " lgbm_kwargs=LGBM_KWARGS,\n", + ")\n", + "\n", + "with_hyoas_results = cached_multi_backtest(\n", + " predictor=ab_hyoas_predictor,\n", + " spec=backtest_spec,\n", + " data_service=svc_hyoas,\n", + " store_dir=PREDICTIONS_DIR / \"hyoas_ab\" / \"with_hyoas\",\n", + ")\n", + "\n", + "\n", + "# Combine the existing baseline and new HYOAS results\n", + "hyoas_comparison_results = {\n", + " \"LightGBM without HYOAS\": without_hyoas_results,\n", + " \"LightGBM with HYOAS\": with_hyoas_results,\n", + "}\n", + "\n", + "\n", + "# Display CRPS\n", + "summary_rows = []\n", + "\n", + "for experiment, task_results in hyoas_comparison_results.items():\n", + " for task_id, result in task_results.items():\n", + " summary_rows.append({\n", + " \"experiment\": experiment,\n", + " \"task\": task_id,\n", + " \"mean_crps\": result.mean_score,\n", + " \"predictions\": len(result.predictions),\n", + " })\n", + "\n", + "HYOAS_AB_SUMMARY = pd.DataFrame(summary_rows)\n", + "display(HYOAS_AB_SUMMARY)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "e5953900", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "crps_pivot = HYOAS_AB_SUMMARY.pivot(\n", + " index=\"task\",\n", + " columns=\"experiment\",\n", + " values=\"mean_crps\",\n", + ")\n", + "\n", + "ax = crps_pivot.plot(\n", + " kind=\"bar\",\n", + " figsize=(11, 5),\n", + " color=[\"tab:blue\", \"tab:orange\"],\n", + ")\n", + "\n", + "ax.set_title(\n", + " \"BAA10Y LightGBM Backtest: CRPS With Versus Without HYOAS\"\n", + ")\n", + "ax.set_xlabel(\"BAA10Y target\")\n", + "ax.set_ylabel(\"Mean CRPS — lower is better\")\n", + "ax.tick_params(axis=\"x\", rotation=0)\n", + "ax.grid(\n", + " axis=\"y\",\n", + " linestyle=\"--\",\n", + " alpha=0.25,\n", + ")\n", + "ax.legend(title=\"Experiment\")\n", + "\n", + "for container in ax.containers:\n", + " ax.bar_label(\n", + " container,\n", + " fmt=\"%.4f\",\n", + " padding=3,\n", + " )\n", + "\n", + "plt.tight_layout()\n", + "plt.show()\n", + "\n", + "ab_task_ids = [\n", + " \"baa10y_change_1b\",\n", + " \"baa10y_change_5b\",\n", + " \"baa10y_change_21b\",\n", + "]\n", + "\n", + "for task_id in ab_task_ids:\n", + " compare_by_run = {}\n", + "\n", + " for label, task_results in hyoas_comparison_results.items():\n", + " result = task_results.get(task_id)\n", + "\n", + " if result is None:\n", + " continue\n", + "\n", + " compare_by_run[label] = build_spread_change_compare_frame(\n", + " result.predictions,\n", + " svc_hyoas,\n", + " task_id,\n", + " )\n", + "\n", + " if not compare_by_run:\n", + " print(f\"No backtest results found for {task_id}\")\n", + " continue\n", + "\n", + " fig, _ = plot_spread_change_forecast_vs_actual_multi(\n", + " compare_by_run,\n", + " title=(\n", + " f\"{task_id}: Forecast vs Actual — \"\n", + " \"With and Without HYOAS\"\n", + " ),\n", + " )\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "25bccd4f", + "metadata": {}, + "source": [] + }, { "cell_type": "markdown", "id": "25797985", diff --git a/implementations/BAA10Y_forecasting/99_starter_agent.ipynb b/implementations/BAA10Y_forecasting/99_starter_agent.ipynb index 4d75362a..8a41e582 100644 --- a/implementations/BAA10Y_forecasting/99_starter_agent.ipynb +++ b/implementations/BAA10Y_forecasting/99_starter_agent.ipynb @@ -14,12 +14,17 @@ "- **optional code execution** — an E2B Python sandbox\n", "- **two lightweight skills** — *tool-usage playbooks* in `starter_agent/skills/`\n", "\n", - "It does two things: lets you **talk to the agent** (open-ended, Track 2) and **score one real forecast** (Track 1). The live cells are gated by `RUN_AGENT` so a fresh `Run All` is safe and free; flip it to `True` to actually call the model.\n" + "It does two things: lets you **talk to the agent** (open-ended, Track 2) and **score one real forecast** (Track 1). The live cells are gated by `RUN_AGENT` so a fresh `Run All` is safe and free; flip it to `True` to actually call the model.\n", + "use commend line:cd /home/coder/agentic-forecasting/implementations/BAA10Y_forecasting \n", + "\n", + "uv run adk web starter_agent/ \n", + "\n", + "In the personal terminal coder login https://platform.vectorinstitute.ai/ " ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 2, "id": "cell-01", "metadata": {}, "outputs": [ @@ -80,7 +85,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "id": "cell-03", "metadata": {}, "outputs": [ @@ -101,10 +106,14 @@ "\n", "keep your reasoning transparent and your claims honest, BAA10Y spread changes are often centered near zero, while volatility and tail risk vary over time. Remain conservative about directional predictability.\n", "\n", - "## How to respond\n", + "## HYOAS information\n", + "\n", + "The covariate snapshot may contain two HYOAS-related series:\n", + "- `hyoas_observed_change_1b_bps_l1b` is the observed FRED HYOAS one-business-day change.\n", + "- `hyoas_hyg_dgs3_proxy_change_1b_bps_l1b` is the HYG-DGS3 duration-based proxy.\n", + "Both series are measured in basis points and lagged by one business day.\n", "\n", - "- For open-ended questions, scenario analysis, or anything conversational, answer directly and concisely — do NOT ask for a JSON payload.\n", - "- When you are handed a task that asks for a structured probabilistic forecast, produce a calibrated one. ...\n" + "Prefer observed HYOAS when it is available. Use the proxy as supporting information or as a fallback when observed HYOAS is unavailable. Do not count them as two independent signals because they represent the same high-yield credit-risk factor. If they disagree mat ...\n" ] } ], @@ -136,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "id": "cell-05", "metadata": {}, "outputs": [ @@ -144,29 +153,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "The BAA10Y spread is currently governed by a tug-of-war between resilient economic fundamentals and lingering policy-induced volatility.\n", + "The BAA10Y spread (Moody's Baa Corporate yield vs. 10-Year Treasury) is currently influenced by a tension between resilient corporate fundamentals and macro-uncertainty.\n", "\n", "### Macroeconomic and Credit-Market Risks\n", - "* **Tightening Risks (Spread Compression):**\n", - " * **Economic Soft Landing:** Continued strength in employment and GDP growth reduces default probability, sustaining investor demand for credit risk premium.\n", - " * **Easing Financial Conditions:** If inflation prints remain benign, the Federal Reserve’s trajectory for rate cuts sustains liquidity and suppresses volatility.\n", - "* **Widening Risks (Spread Expansion):**\n", - " * **Policy Miscalculation:** Persistent or re-accelerating inflation forcing a hawkish Fed pivot or delayed cuts, driving Treasury volatility and investor risk aversion.\n", - " * **Refinancing/Liquidity Shocks:** While many firms have termed out debt, a sudden \"risk-off\" event in broader markets could lead to temporary liquidity dry-ups, forcing credit spreads wider as investors demand higher compensation for illiquidity.\n", - " * **Geopolitical/Fiscal Shocks:** Unanticipated fiscal deterioration or trade/tariff policies that weigh on corporate earnings outlooks.\n", + "\n", + "* **Widening Risks (Stress/Risk-Off):**\n", + " * **Monetary Policy:** Hawkish signals or actual rate hikes by the Fed, particularly if inflation proves sticky, raise the \"cost of carry\" and refinance risk for lower-rated corporates.\n", + " * **Economic Slowdown/Stagflation:** Any data indicating a sharper-than-expected cooling in employment or business investment could trigger a flight to quality, driving Treasury yields down while pushing corporate spreads wider due to default risk concerns.\n", + " * **Refinancing/Liquidity:** Should the \"maturity wall\" become a focal point or if private credit volatility spills into public markets (e.g., liquidity gating), risk premiums would naturally widen.\n", + "* **Tightening Risks (Optimism/Risk-On):**\n", + " * **\"Soft Landing\" Confirmation:** Continued evidence of robust earnings and manageable inflation expectations keeps credit spreads compressed as investors \"reach for yield\" in a stable growth environment.\n", + " * **Geopolitical Stabilization:** De-escalation of regional conflicts would lower the risk-premium component embedded in credit spreads.\n", "\n", "### Impact on Forecast Characteristics\n", - "Whether forecasting at a 5-business-day or 21-business-day horizon, the primary impacts are as follows:\n", "\n", - "* **Location:** The median expected change remains near zero. The \"drift\" is negligible because the spread is a mean-reverting series; absent a specific shock, the most likely outcome is that the current risk premium remains stable.\n", - "* **Uncertainty (Standard Deviation):** \n", - " * **5-day:** Primarily driven by daily market \"noise\" and high-frequency sentiment shifts (e.g., equity volatility).\n", - " * **21-day:** Heavily influenced by the \"macro calendar\"—the arrival of non-farm payrolls, CPI prints, or FOMC communications—which can fundamentally shift the regime, widening the distribution of outcomes.\n", - "* **Tail Risk:** \n", - " * **Asymmetry:** The distribution of BAA10Y changes is typically **positively skewed (leptokurtic)**. While compression has limits (the floor), widening events (the tails) can be rapid and extreme during \"risk-off\" episodes.\n", - " * **Time Sensitivity:** 21-day forecasts exhibit \"fat tails\" significantly more than 5-day forecasts. Over a month, the cumulative probability of a \"shock\" event (e.g., a geopolitical crisis) increases non-linearly, thickening the left tail (widening risk) relative to a short-term 5-day view.\n", + "| Horizon | Impact on Location (Mean) | Impact on Uncertainty (Width) | Impact on Tail Risk (Skew/Kurtosis) |\n", + "| :--- | :--- | :--- | :--- |\n", + "| **5-Day** | Highly sensitive to immediate market shocks/news flow. | Relatively constrained; follows short-term volatility regimes. | Higher probability of \"jump\" risk if unexpected economic data releases occur. |\n", + "| **21-Day** | More aligned with macroeconomic trends (Fed policy, inflation data). | Significantly wider; captures potential for structural changes or policy shifts. | Higher probability of fat tails (e.g., credit event) as more data points introduce regime-shift potential. |\n", + "\n", + "**Strategic Considerations:**\n", + "* **Location:** Given the current state, forecasts should lean toward stability unless high-frequency signals (e.g., widening high-yield spreads or equity volatility spikes) suggest a regime shift.\n", + "* **Uncertainty:** In a \"higher-for-longer\" rate environment, maintain wider confidence intervals than historical averages, as market sensitivity to inflation prints remains elevated.\n", + "* **Tail Risk:** Always account for asymmetric left-tail risk (sudden widening), as credit markets are prone to rapid repricing compared to the more gradual pace of tightening. \n", "\n", - "**Summary:** For any horizon, be wary of mean-reversion assumptions during periods of macro-regime uncertainty. The risk is asymmetric; the downside (spread widening) is historically faster and more volatile than the upside (tightening).\n" + "*Disclaimer: Directional predictability in the BAA10Y spread is low. This analysis is intended for calibration of your expectations and risk models.*\n" ] }, { @@ -213,7 +224,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 12, "id": "cell-07", "metadata": {}, "outputs": [ @@ -221,13 +232,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Origin as_of=2026-07-08 horizon=5b (1 week) (latest data 2026-07-16)\n", + "Origin as_of=2026-07-23 horizon=5b (1 week) (latest data 2026-07-31)\n", "\n", - " agent point : +0.0000 (+0.00%)\n", - " agent 80% CI : [-0.0700, +0.0700]\n", - " actual : +0.0400 (+4.00%) in 80% band? yes ✓\n", + " agent point : +1.50 (+0.0150%)\n", + " agent 80% CI : [-4.00, +9.00]\n", + " actual : +6.00 (+0.0600%) in 80% band? yes ✓\n", "\n", - "Rationale: The forecast reflects a cautious outlook, acknowledging recent slight upward pressure on the spread while adhering to the principle that spread changes are unpredictable and generally centered around zero. Market stability indicators (VIX, yield curve) suggest no immediate catalyst for a significant\n" + "Rationale: The BAA10Y spread has undergone a regime shift from persistent tightening in late spring to a period of higher volatility and recent widening pressure in July. Market signals are mixed; while HYOAS shows minor tightening, the frequency of 4-5 bps daily moves has increased, reflecting investor indeci\n" ] } ], @@ -239,15 +250,18 @@ " from aieng.forecasting.evaluation.task import ForecastingTask\n", " from BAA10Y_forecasting import build_baa10y_multivariate_service\n", " from BAA10Y_forecasting.data import baa10y_change_series_id\n", - " from BAA10Y_forecasting.data import DEFAULT_COVARIATE_SERIES_IDS\n", - "\n", + " from BAA10Y_forecasting.data import (\n", + " DEFAULT_COVARIATE_SERIES_IDS,\n", + " HYOAS_OPTIONAL_COVARIATE_SERIES_IDS,\n", + " )\n", "\n", " # Valid BAA10Y horizons are 1, 5, and 21 business days.\n", "\n", " HORIZON = 5\n", - " COVARIATES = list(\n", - " DEFAULT_COVARIATE_SERIES_IDS\n", - " )\n", + " COVARIATES = [\n", + " *DEFAULT_COVARIATE_SERIES_IDS,\n", + " *HYOAS_OPTIONAL_COVARIATE_SERIES_IDS,\n", + " ]\n", " svc = build_baa10y_multivariate_service(covariate_series_ids=COVARIATES)\n", " now = datetime.now(tz=timezone.utc).replace(tzinfo=None)\n", " tgt = baa10y_change_series_id(HORIZON)\n", @@ -270,23 +284,161 @@ "\n", " rows = full[full[\"timestamp\"] >= AS_OF + pd.offsets.BDay(HORIZON)]\n", " actual = float(rows[\"value\"].iloc[0]) if not rows.empty else None\n", - "\n", " fc = pred.payload\n", " lo, hi = fc.quantiles[0.10], fc.quantiles[0.90]\n", + " hyoas_result = {\n", + " \"point_forecast\": fc.point_forecast,\n", + " \"q10\": fc.quantiles[0.10],\n", + " \"q90\": fc.quantiles[0.90],\n", + " }\n", + " print(f\"Origin as_of={AS_OF.date()} horizon={HORIZON}b (1 week) (latest data {last_date.date()})\\n\")\n", + " print(f\" agent point : {fc.point_forecast:+.2f} ({fc.point_forecast / 100:+.4f}%)\")\n", + " print(f\" agent 80% CI : [{lo:+.2f}, {hi:+.2f}]\")\n", + " if actual is None:\n", + " print(\" actual : N/A (not yet resolved)\")\n", + " else:\n", + " inb = \"yes ✓\" if lo <= actual <= hi else \"no ✗\"\n", + " print(f\" actual : {actual:+.2f} ({actual / 100:+.4f}%) in 80% band? {inb}\")\n", + " if pred.metadata.get(\"rationale\"):\n", + " print(\"\\nRationale:\", pred.metadata[\"rationale\"][:300])\n", + "else:\n", + " print(\"RUN_AGENT is False — set it to True to score a live forecast against a known outcome.\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "41fb3135", + "metadata": {}, + "source": [ + "## 4 without HYOAS" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "2c6def50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Origin as_of=2026-07-23 horizon=5b (1 week) (latest data 2026-07-31)\n", + "\n", + " agent point : +1.50 (+0.0150%)\n", + " agent 80% CI : [-4.00, +9.00]\n", + " actual : +6.00 (+0.0600%) in 80% band? yes ✓\n", + "\n", + "Rationale: The BAA10Y spread has undergone a regime shift from persistent tightening in late spring to a period of higher volatility and recent widening pressure in July. Market signals are mixed; while HYOAS shows minor tightening, the frequency of 4-5 bps daily moves has increased, reflecting investor indeci\n" + ] + } + ], + "source": [ + "\n", + "if RUN_AGENT:\n", + "\n", + " # Valid BAA10Y horizons are 1, 5, and 21 business days.\n", + " BASELINE_COVARIATES = list(DEFAULT_COVARIATE_SERIES_IDS)\n", + "\n", + " baseline_svc = build_baa10y_multivariate_service(\n", + " covariate_series_ids=BASELINE_COVARIATES\n", + " )\n", + "\n", + " baseline_ctx = baseline_svc.context(as_of=AS_OF)\n", + "\n", + " baseline_pred = build_starter_agent_predictor(\n", + " config,\n", + " covariate_series_ids=BASELINE_COVARIATES,\n", + " ).predict(task, baseline_ctx)[0]\n", + "\n", + " baseline_fc = baseline_pred.payload\n", + "\n", + " baseline_result = {\n", + " \"point_forecast\": baseline_fc.point_forecast,\n", + " \"q10\": baseline_fc.quantiles[0.10],\n", + " \"q90\": baseline_fc.quantiles[0.90],\n", + " }\n", + "\n", " print(f\"Origin as_of={AS_OF.date()} horizon={HORIZON}b (1 week) (latest data {last_date.date()})\\n\")\n", - " print(f\" agent point : {fc.point_forecast:+.4f} ({fc.point_forecast * 100:+.2f}%)\")\n", - " print(f\" agent 80% CI : [{lo:+.4f}, {hi:+.4f}]\")\n", + " print(f\" agent point : {fc.point_forecast:+.2f} ({fc.point_forecast / 100:+.4f}%)\")\n", + " print(f\" agent 80% CI : [{lo:+.2f}, {hi:+.2f}]\")\n", " if actual is None:\n", " print(\" actual : N/A (not yet resolved)\")\n", " else:\n", " inb = \"yes ✓\" if lo <= actual <= hi else \"no ✗\"\n", - " print(f\" actual : {actual:+.4f} ({actual * 100:+.2f}%) in 80% band? {inb}\")\n", + " print(f\" actual : {actual:+.2f} ({actual / 100:+.4f}%) in 80% band? {inb}\")\n", " if pred.metadata.get(\"rationale\"):\n", " print(\"\\nRationale:\", pred.metadata[\"rationale\"][:300])\n", "else:\n", " print(\"RUN_AGENT is False — set it to True to score a live forecast against a known outcome.\")" ] }, + { + "cell_type": "code", + "execution_count": 15, + "id": "27bad88f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "labels = [\"Without HYOAS\", \"With HYOAS\", \"Actual\"]\n", + "\n", + "values = [\n", + " baseline_result[\"point_forecast\"],\n", + " hyoas_result[\"point_forecast\"],\n", + " actual,\n", + "]\n", + "\n", + "colors = [\"tab:blue\", \"tab:orange\", \"black\"]\n", + "\n", + "\n", + "fig, ax = plt.subplots(figsize=(9, 5))\n", + "\n", + "bars = ax.bar(\n", + " labels,\n", + " values,\n", + " color=colors,\n", + " alpha=0.85,\n", + ")\n", + "\n", + "ax.axhline(0, color=\"gray\", linewidth=1)\n", + "\n", + "ax.set_title(\n", + " f\"BAA10Y {HORIZON}-Business-Day Forecast \"\n", + " f\"at {AS_OF.date()}\"\n", + ")\n", + "ax.set_ylabel(\"BAA10Y spread change (basis points)\")\n", + "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.25)\n", + "\n", + "\n", + "for bar, value in zip(bars, values):\n", + " ax.annotate(\n", + " f\"{value:+.3f}\",\n", + " xy=(bar.get_x() + bar.get_width() / 2, value),\n", + " xytext=(0, 3 if value >= 0 else -12),\n", + " textcoords=\"offset points\",\n", + " ha=\"center\",\n", + " va=\"bottom\" if value >= 0 else \"top\",\n", + " )\n", + "\n", + "\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, { "cell_type": "markdown", "id": "cell-08", diff --git a/implementations/BAA10Y_forecasting/__init__.py b/implementations/BAA10Y_forecasting/__init__.py index 26490bef..43b29404 100644 --- a/implementations/BAA10Y_forecasting/__init__.py +++ b/implementations/BAA10Y_forecasting/__init__.py @@ -34,30 +34,36 @@ BAA10Y_FRED_ID, baa10y_change_series_id, build_baa10y_multivariate_service, + HYOAS_OPTIONAL_COVARIATE_SERIES_IDS, + SERIES_ID_HYOAS_OBSERVED_CHANGE, + SERIES_ID_HYOAS_PROXY_CHANGE, ) -__all__ = [ - DEFAULT_COVARIATE_SERIES_IDS, - FRED_PREFETCH_REGISTRY, - FRED_SERIES_IDS_FOR_PREFETCH, - SERIES_ID_2Y10Y_SPREAD, - SERIES_ID_10Y_YIELD, - SERIES_ID_CPI_INFLATION_CHANGE, - SERIES_ID_DOLLAR_INDEX_RETURN, - SERIES_ID_FED_FUNDS, - SERIES_ID_GOLD_RETURN, - SERIES_ID_NASDAQ_RETURN, - SERIES_ID_OIL_RETURN, - SERIES_ID_UNEMPLOYMENT, - SERIES_ID_VIX_CHANGE, - SERIES_ID_VIX_LEVEL, - BAA10Y_CHANGE_SERIES_ID, - BAA10Y_CHANGE_TARGETS, - BAA10Y_CHANGE_WINDOWS, - BAA10Y_FRED_ID, - baa10y_change_series_id, - build_baa10y_multivariate_service, - -] +__all__ = ["DEFAULT_COVARIATE_SERIES_IDS", + "FRED_PREFETCH_REGISTRY", + "FRED_SERIES_IDS_FOR_PREFETCH", + "SERIES_ID_10Y_YIELD", + "SERIES_ID_2Y10Y_SPREAD", + "SERIES_ID_CPI_INFLATION_CHANGE", + "SERIES_ID_DOLLAR_INDEX_RETURN", + "SERIES_ID_FED_FUNDS", + "SERIES_ID_GOLD_RETURN", + "SERIES_ID_NASDAQ_RETURN", + "SERIES_ID_OIL_RETURN", + "SERIES_ID_UNEMPLOYMENT", + "SERIES_ID_VIX_CHANGE", + "SERIES_ID_VIX_LEVEL", + "BAA10Y_FRED_ID", + "BAA10Y_CHANGE_SERIES_ID", + "BAA10Y_CHANGE_TARGETS", + "BAA10Y_CHANGE_WINDOWS", + "BAA10Y_WINDOW_LABELS", + "StaticFrameAdapter", + "baa10y_change_series_id", + "build_baa10y_target_service", + "build_baa10y_multivariate_service", + "HYOAS_OPTIONAL_COVARIATE_SERIES_IDS", + "SERIES_ID_HYOAS_OBSERVED_CHANGE", + "SERIES_ID_HYOAS_PROXY_CHANGE",] \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/data.py b/implementations/BAA10Y_forecasting/data.py index b80a7b0e..4a69de9a 100644 --- a/implementations/BAA10Y_forecasting/data.py +++ b/implementations/BAA10Y_forecasting/data.py @@ -8,11 +8,11 @@ horizons, such as 1, 5, and 21 business days. -- (forecast 1 step ahead) → next-session return. -- (forecast 5 steps ahead) → forward 1-week return. -- (forecast 21 steps ahead)→ forward 1-month return. +- (forecast 1 step ahead) → next-session spread return. +- (forecast 5 steps ahead) → forward 1-week spread return. +- (forecast 21 steps ahead)→ forward 1-month spread return. -Using returns (rather than the index level) keeps the target stationary, which +Using spread changes (rather than the spread level) helps make the target more stationary, which is the appropriate setup for a conventional-methods comparison. Covariates supported (daily business-day frame): @@ -26,6 +26,7 @@ - Gold returns - Dollar index returns - NASDAQ returns +- HYOAS and HYOAS proxy by high yield bond and treasury Anti-leakage policy: - Every covariate is transformed and then lagged by one business day. @@ -47,7 +48,12 @@ from collections.abc import Callable from pathlib import Path from typing import Any - +from hyoas_proxy import ( + DGS3_FRED_ID, + HYOAS_FRED_ID, + HYG_TICKER, + build_hyg_dgs3_proxy, + ) import numpy as np import pandas as pd from aieng.forecasting.data import DataService, SeriesMetadata @@ -156,7 +162,7 @@ def _build_cumulative_spread_change_frame(spread_df: pd.DataFrame, window: int) # BAA10Y is measured in basis. # spread widening/tightening in basis points - frame["value"] = frame["value"] - frame["value"].shift(window) + frame["value"] = (frame["value"] - frame["value"].shift(window))*100 frame = frame.dropna(subset=["value"]).reset_index(drop=True) frame["released_at"] = pd.to_datetime(frame["timestamp"]) @@ -171,7 +177,7 @@ def build_baa10y_target_service( end: str = "2026-07-01", fred_cache_dir: Path | None = None, ) -> DataService: - """Register one close-to-close cumulative log-return target per window in ``windows``.""" + """Register one close-to-close cumulative spread change target per window in ``windows``.""" _load_fred_dotenv() fred_dir = _as_absolute_cache( @@ -260,6 +266,13 @@ def _default_cache_dir() -> Path: "Percent", "D", ), + ## include HYOAS + "BAMLH0A0HYM2": ( + "ICE BofA US High Yield Index Option-Adjusted Spread", + "Percent", + "D", + ), + "DGS3": ( "3-Year Treasury Constant Maturity Rate","Percent", "D",), "DGS10": ("10-Year Treasury Constant Maturity Rate", "Percent", "D"), "DGS2": ("2-Year Treasury Constant Maturity Rate", "Percent", "D"), @@ -295,6 +308,10 @@ def _default_cache_dir() -> Path: VIX_TICKER = "^VIX" NASDAQ_TICKER = "^IXIC" +# HYOAS observed series and approved HYG-DGS3 proxy. +SERIES_ID_HYOAS_OBSERVED_CHANGE = "hyoas_observed_change_1b_bps_l1b" +SERIES_ID_HYOAS_PROXY_CHANGE = "hyoas_hyg_dgs3_proxy_change_1b_bps_l1b" + SERIES_ID_VIX_LEVEL = "vix_level_l1b" SERIES_ID_VIX_CHANGE = "vix_log_ret_1b_l1b" SERIES_ID_10Y_YIELD = "ust10y_level_l1b" @@ -307,6 +324,10 @@ def _default_cache_dir() -> Path: SERIES_ID_DOLLAR_INDEX_RETURN = "dollar_index_log_ret_1b_l1b" SERIES_ID_NASDAQ_RETURN = "nasdaq_log_ret_1b_l1b" +HYOAS_OPTIONAL_COVARIATE_SERIES_IDS: list[str] = [ + SERIES_ID_HYOAS_OBSERVED_CHANGE, + SERIES_ID_HYOAS_PROXY_CHANGE, +] DEFAULT_COVARIATE_SERIES_IDS: list[str] = [ SERIES_ID_VIX_LEVEL, @@ -320,6 +341,7 @@ def _default_cache_dir() -> Path: SERIES_ID_GOLD_RETURN, SERIES_ID_DOLLAR_INDEX_RETURN, SERIES_ID_NASDAQ_RETURN, + ] @@ -355,6 +377,30 @@ def _fred_frame( return _canonical_three_col(adapter.fetch()) +def _build_observed_hyoas_change_feature( + *, + cache_dir: Path, + refresh: bool, +) -> pd.DataFrame: + """Build observed daily HYOAS changes in basis points, lagged by 1B.""" + hyoas = _fred_frame( + HYOAS_FRED_ID, + cache_dir=cache_dir, + refresh=refresh, + ) + hyoas = _drop_weekend_timestamp_rows(hyoas) + hyoas = _business_daily_ffill(hyoas) + hyoas = hyoas.sort_values("timestamp").reset_index(drop=True) + + hyoas["value"] = (pd.to_numeric(hyoas["value"], errors="coerce").diff() * 100.0) + hyoas = hyoas.dropna(subset=["value"]).reset_index(drop=True) + + hyoas["released_at"] = pd.to_datetime(hyoas["timestamp"]) + + return _apply_one_business_day_feature_lag( + hyoas[["timestamp", "value", "released_at"]] + ) + def _build_monthly_cpi_mom_feature( *, cache_dir: Path, @@ -737,6 +783,84 @@ def _handle_covariate_error(series_id: str, exc: Exception) -> None: except (RuntimeError, ValueError) as exc: _handle_covariate_error(SERIES_ID_DOLLAR_INDEX_RETURN, exc) + ## add for HYOAS + if SERIES_ID_HYOAS_OBSERVED_CHANGE in desired_set: + try: + hyoas_observed = _build_observed_hyoas_change_feature( + cache_dir=fred_dir, + refresh=refresh, + ) + svc.register( + SERIES_ID_HYOAS_OBSERVED_CHANGE, + StaticFrameAdapter(hyoas_observed), + SeriesMetadata( + series_id=SERIES_ID_HYOAS_OBSERVED_CHANGE, + description=( + "Observed ICE BofA US High Yield OAS daily change, " + "lagged 1 business day" + ), + source=f"FRED ({HYOAS_FRED_ID}), derived", + units="basis-points", + frequency="B", + table_id=f"fred:{HYOAS_FRED_ID}:change-1b-bps-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error( + SERIES_ID_HYOAS_OBSERVED_CHANGE, + exc, + ) + if SERIES_ID_HYOAS_PROXY_CHANGE in desired_set: + try: + hyg_prices = _load_yahoo_close_frame( + HYG_TICKER, + start=start, + end=end, + cache_dir=yahoo_dir, + refresh=refresh, + ) + + dgs3_yield = _fred_frame( + DGS3_FRED_ID, + cache_dir=fred_dir, + refresh=refresh, + ) + dgs3_yield = _drop_weekend_timestamp_rows(dgs3_yield) + dgs3_yield = _business_daily_ffill(dgs3_yield) + + hyoas_proxy = build_hyg_dgs3_proxy( + hyg_prices=hyg_prices, + dgs3_yield=dgs3_yield, + ) + hyoas_proxy = _apply_one_business_day_feature_lag( + hyoas_proxy + ) + + svc.register( + SERIES_ID_HYOAS_PROXY_CHANGE, + StaticFrameAdapter(hyoas_proxy), + SeriesMetadata( + series_id=SERIES_ID_HYOAS_PROXY_CHANGE, + description=( + "HYG-DGS3 duration-based high-yield spread-change " + "proxy, lagged 1 business day" + ), + source=( + f"Yahoo Finance ({HYG_TICKER}) and " + f"FRED ({DGS3_FRED_ID}), derived" + ), + units="basis-points", + frequency="B", + table_id="proxy:HYG-DGS3:change-1b-bps-l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error( + SERIES_ID_HYOAS_PROXY_CHANGE, + exc, + ) + + return svc @@ -764,4 +888,6 @@ def _handle_covariate_error(series_id: str, exc: Exception) -> None: "baa10y_change_series_id", "build_baa10y_target_service", "build_baa10y_multivariate_service", -] + "HYOAS_OPTIONAL_COVARIATE_SERIES_IDS", + "SERIES_ID_HYOAS_OBSERVED_CHANGE", + "SERIES_ID_HYOAS_PROXY_CHANGE",] \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/hyoas_proxy.py b/implementations/BAA10Y_forecasting/hyoas_proxy.py new file mode 100644 index 00000000..e8ceffe7 --- /dev/null +++ b/implementations/BAA10Y_forecasting/hyoas_proxy.py @@ -0,0 +1,52 @@ +"""Approved HYOAS proxy calculations. generated by ChatGtP""" +import numpy as np +import pandas as pd + +HYOAS_FRED_ID = "BAMLH0A0HYM2" +HYG_TICKER = "HYG" +DGS3_FRED_ID = "DGS3" +HYG_DURATION_YEARS = 3 + + +def build_hyg_dgs3_proxy( + hyg_prices: pd.DataFrame, + dgs3_yield: pd.DataFrame, + *, + duration: float = HYG_DURATION_YEARS, +) -> pd.DataFrame: + """Construct a daily HYOAS-change proxy in basis points. + + Expected columns: + hyg_prices: timestamp, value + dgs3_yield: timestamp, value + + HYG returns are decimals. + DGS3 levels are percentages. + """ + + hyg = hyg_prices[["timestamp", "value"]].copy() + treasury = dgs3_yield[["timestamp", "value"]].copy() + + hyg["timestamp"] = pd.to_datetime(hyg["timestamp"]) + treasury["timestamp"] = pd.to_datetime(treasury["timestamp"]) + + hyg["hyg_return"] = np.log( + hyg["value"] / hyg["value"].shift(1) + ) + + # DGS3 is quoted in percent: 4.25 means 4.25%. + treasury["dgs3_change_decimal"] = ( + treasury["value"].diff() / 100.0 + ) + result = hyg[["timestamp", "hyg_return"]].merge( + treasury[["timestamp", "dgs3_change_decimal"]], + on="timestamp", + how="inner", + ) + result["value"] = 10_000 * ( + -result["hyg_return"] / duration + - result["dgs3_change_decimal"] + ) + result = result.dropna(subset=["value"]) + result["released_at"] = result["timestamp"] + return result[["timestamp", "value", "released_at"]] \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/plots.py b/implementations/BAA10Y_forecasting/plots.py index fa521dbd..8eb1a88e 100644 --- a/implementations/BAA10Y_forecasting/plots.py +++ b/implementations/BAA10Y_forecasting/plots.py @@ -455,7 +455,7 @@ def plot_spread_change_forecast_vs_actual_multi( ax.axhline(0.0, color="0.5", linewidth=0.8, linestyle="--", zorder=1) ax.plot( base["session"].to_numpy(), - 100.0 * base["actual_spread_change"].to_numpy(dtype=float), + base["actual_spread_change"].to_numpy(dtype=float), color="#0d47a1", linewidth=2.2, marker="o", @@ -471,7 +471,7 @@ def plot_spread_change_forecast_vs_actual_multi( d = d.sort_values("session") ax.plot( d["session"].to_numpy(), - 100.0 * d["forecast_spread_change"].to_numpy(dtype=float), + d["forecast_spread_change"].to_numpy(dtype=float), color=cmap(i % 10), linewidth=1.6, linestyle="--", @@ -488,7 +488,7 @@ def plot_spread_change_forecast_vs_actual_multi( ax.legend(loc="upper left", framealpha=0.92, fontsize=8, ncol=2) ax.grid(True, alpha=0.28, linestyle="-", linewidth=0.6) ax.tick_params(axis="both", labelsize=9, colors="0.35") - ax.yaxis.set_major_formatter(FuncFormatter(lambda v, _: f"{v:,.1f}%")) + ax.yaxis.set_major_formatter(FuncFormatter(lambda v, _: f"{v:,.1f}")) fig.autofmt_xdate() for spine in ("top", "right"): ax.spines[spine].set_visible(False) @@ -582,7 +582,7 @@ def plotly_spread_change_forecast_vs_actual_multi( dash="dash", ), marker=dict(size=5, symbol="square"), - hovertemplate=f"{label}: " + "%{y:.2%}", + hovertemplate=f"{label}: " + "%{y:+.2%}", ) ) @@ -614,7 +614,12 @@ def plotly_spread_change_forecast_vs_actual_multi( spikecolor="rgba(120, 120, 120, 0.5)", spikethickness=1, ) - fig.update_yaxes(showgrid=True, gridcolor="rgba(0, 0, 0, 0.08)", tickformat=".1%") + fig.update_yaxes( + showgrid=True, + gridcolor="rgba(0, 0, 0, 0.08)", + tickformat=".1f", + ticksuffix=" bp", + ) return fig diff --git a/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py b/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py index 5022e38a..2421c5eb 100644 --- a/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py +++ b/implementations/BAA10Y_forecasting/predictors/llmp_sampled_trajectory.py @@ -33,7 +33,7 @@ _SERIES_DESCRIPTION = ( "Series: change in the FRED BAA10Y corporate credit spread over " "a fixed number of business days. BAA10Y spread between Moody;s Seasoned Baa Corporate Bond and 10-Year Treasury Constant Maturity.\n" - "Units: percentage points. A positive value means spread widening;" + "Units: basis points. A positive value means spread widening;" "a negative value means spread tightening.\n" "Frequency: business days (Mon-Fri)." ) diff --git a/implementations/BAA10Y_forecasting/starter_agent/agent.py b/implementations/BAA10Y_forecasting/starter_agent/agent.py index 5297c9ce..c87fdc64 100644 --- a/implementations/BAA10Y_forecasting/starter_agent/agent.py +++ b/implementations/BAA10Y_forecasting/starter_agent/agent.py @@ -133,6 +133,24 @@ def _build_starter_instruction() -> str: "keep your reasoning transparent and your claims honest, BAA10Y spread " "changes are often centered near zero, while volatility and tail risk vary " "over time. Remain conservative about directional predictability.\n\n" + + "## HYOAS information\n\n" + "The covariate snapshot may contain two HYOAS-related series:\n" + "- `hyoas_observed_change_1b_bps_l1b` is the observed FRED HYOAS " + "one-business-day change.\n" + "- `hyoas_hyg_dgs3_proxy_change_1b_bps_l1b` is the HYG-DGS3 " + "duration-based proxy.\n" + "Both series are measured in basis points and lagged by one business day.\n\n" + + "Prefer observed HYOAS when it is available. Use the proxy as supporting " + "information or as a fallback when observed HYOAS is unavailable. Do not " + "count them as two independent signals because they represent the same " + "high-yield credit-risk factor. If they disagree materially, reduce " + "directional confidence instead of combining them mechanically.\n\n" + + "A positive HYOAS change indicates spread widening and increasing credit " + "stress. A negative change indicates spread tightening. HYOAS is supporting " + "information for forecasting BAA10Y; it is not the BAA10Y target itself.\n\n" "## How to respond\n\n" "- For open-ended questions, scenario analysis, or anything " "conversational, answer directly and concisely — do NOT ask for a JSON " diff --git a/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md b/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md index c5363ca5..15a470a1 100644 --- a/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md +++ b/implementations/BAA10Y_forecasting/starter_agent/skills/forecasting/SKILL.md @@ -12,12 +12,34 @@ description: >- Load this when your task payload asks for a structured forecast. For open-ended questions, ignore it and just answer. +### Using HYOAS information + +The covariate snapshot may include: + +- `hyoas_observed_change_1b_bps_l1b`: observed HYOAS change; +- `hyoas_hyg_dgs3_proxy_change_1b_bps_l1b`: HYG-DGS3 proxy change. + +Both are measured in basis points and lagged by one business day. + +Prefer observed HYOAS when it is available. Use the proxy as confirmation or +as a fallback when observed HYOAS is unavailable. Do not count observed HYOAS +and the proxy as two independent signals because they measure the same +high-yield credit-risk factor. + +Positive HYOAS changes indicate spread widening and increasing credit stress; +negative changes indicate spread tightening. If observed HYOAS and the proxy +disagree materially, reduce directional confidence or widen the forecast +interval rather than averaging them mechanically. + +In the rationale, briefly state whether observed HYOAS, the proxy, both, or +neither influenced the BAA10Y forecast. + ## What you'll receive A JSON payload describing the task: a `task` id, the `as_of` cutoff date, `horizons` (steps ahead), the `standard_quantiles` grid, a `target_summary`, the -recent `target_history_csv`, and an `output_schema` showing the exact JSON to -return. +recent `target_history_csv`, an optional `covariate_snapshot`, and an +`output_schema` showing the exact JSON to return. ## The output contract diff --git a/implementations/sp500_forecasting/specs/sp500_backtest_2025.yaml b/implementations/sp500_forecasting/specs/sp500_backtest_2025.yaml index e1ab30ed..92f704d2 100644 --- a/implementations/sp500_forecasting/specs/sp500_backtest_2025.yaml +++ b/implementations/sp500_forecasting/specs/sp500_backtest_2025.yaml @@ -44,5 +44,5 @@ tasks: start: "2025-01-06" end: "2025-12-22" -stride: 5 # weekly origins (~50) +stride: 1 # weekly origins (~50) warmup: 250 diff --git a/implementations/sp500_forecasting/specs/sp500_smoke.yaml b/implementations/sp500_forecasting/specs/sp500_smoke.yaml index bc873e54..81690bd7 100644 --- a/implementations/sp500_forecasting/specs/sp500_smoke.yaml +++ b/implementations/sp500_forecasting/specs/sp500_smoke.yaml @@ -49,5 +49,5 @@ tasks: start: "2025-10-06" end: "2025-11-14" -stride: 5 # weekly origins (~6) +stride: 1 # weekly origins (~6) warmup: 250 From 8caaf5fdfedd888213284519a58b082867454c89 Mon Sep 17 00:00:00 2001 From: default Date: Sun, 9 Aug 2026 02:09:17 +0000 Subject: [PATCH 21/24] enable special query pattern for E5 family embedding model --- .../BAA10Y_forecasting/00_RagPrototype.ipynb | 72 +++++++++++++++---- 1 file changed, 57 insertions(+), 15 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb index 1e415055..7f83dfde 100644 --- a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb +++ b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": { "cellView": "form", "colab": { @@ -71,6 +71,8 @@ "name": "stderr", "output_type": "stream", "text": [ + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" ] }, @@ -111,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -133,6 +135,7 @@ "# @title Configure Embedding Model\n", "EMBEDDING_MODEL_NAME = \"intfloat/e5-base\" # @param [\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\", \"all-MiniLM-L6-v2\"]\n", "MODEL_KWARGS = {} # @param {type:\"raw\"}\n", + "E5_MODEL_NAMES = {\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\"}\n", "\n", "#Do NOT use in bootcamp because of potential license issue\n", "# If using FinanceMTEB/fin-e5-base, it might require trust_remote_code=True\n", @@ -143,6 +146,32 @@ "print(f\"Model kwargs: {MODEL_KWARGS}\")" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#v001\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from typing import List\n", + "\n", + "class E5Embeddings(HuggingFaceEmbeddings):\n", + " \"\"\"HuggingFaceEmbeddings + E5's required 'query: '/'passage: ' prefixes.\n", + "\n", + " E5-family models (intfloat/e5-*, FinanceMTEB/fin-e5-*) are trained with\n", + " these prefixes; omitting them degrades retrieval. Only use this wrapper\n", + " for E5-family models — all-MiniLM-L6-v2 was not trained with this\n", + " convention and should be embedded unprefixed.\n", + " \"\"\"\n", + "\n", + " def embed_documents(self, texts: List[str]) -> List[List[float]]:\n", + " return super().embed_documents([f\"passage: {t}\" for t in texts])\n", + "\n", + " def embed_query(self, text: str) -> List[float]:\n", + " return super().embed_query(f\"query: {text}\")" + ] + }, { "cell_type": "code", "execution_count": null, @@ -170,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -314,6 +343,14 @@ "outputId": "acaab241-0576-44dc-a078-9d5d3689a95b" }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_4625/1185560718.py:11: DeprecationWarning: `langchain-community` is being sunset and is no longer actively maintained. See https://github.com/langchain-ai/langchain-community/issues/674 for details and migration guidance toward standalone integration packages.\n", + " from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -326,7 +363,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "Loading weights: 100%|██████████| 199/199 [00:00<00:00, 3435.65it/s]\n" + "Loading weights: 100%|██████████| 199/199 [00:00<00:00, 1015.66it/s]\n" ] }, { @@ -442,7 +479,12 @@ "if raw_docs:\n", " text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50) # Using a reasonable chunk size for E5 models\n", " splits = text_splitter.split_documents(raw_docs)\n", - " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n", + " #embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n", + " #20260808\n", + " if EMBEDDING_MODEL_NAME in E5_MODEL_NAMES:\n", + " embeddings = E5Embeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", + " else:\n", + " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", "\n", " # Explicitly create the ChromaDB client\n", " try:\n", @@ -492,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "id": "4970b54e" }, @@ -564,7 +606,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -580,12 +622,12 @@ "BM25 Document DB initialized.\n", "[DEBUG] Query: 'holdings of Treasury securities'\n", "[DEBUG] Search arguments for ChromaDB: {'k': 3}\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'source_file': 'monetary20250917a1.pdf', 'total_pages': 4, 'creator': 'Acrobat PDFMaker 25 for Word', 'page': 2, 'title': 'Federal Reserve issues FOMC statement', 'file_date': 202509170000, 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'page_label': '3'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7595457597041031), (Document(metadata={'author': 'Federal Reserve', 'page_label': '3', 'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'title': 'Federal Reserve issues FOMC statement', 'source_file': 'monetary20250917a1.pdf', 'page': 2, 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7536895393972114), (Document(metadata={'file_date': 202509170000, 'source': './unstructured_data/monetary20250917a1.pdf', 'moddate': '2025-09-17T11:53:16-04:00', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page': 0, 'creator': 'Acrobat PDFMaker 25 for Word', 'page_label': '1', 'producer': 'Adobe PDF Library 25.1.97', 'creationdate': '2025-09-17T11:48:45-04:00', 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7255547689219765)]\n", - "Confidence: 0.7463 | Needs Web Search: False\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", + "Confidence: 0.7548 | Needs Web Search: False\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'source_file': 'monetary20250917a1.pdf', 'total_pages': 4, 'creator': 'Acrobat PDFMaker 25 for Word', 'page': 2, 'title': 'Federal Reserve issues FOMC statement', 'file_date': 202509170000, 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'page_label': '3'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7595457597041031), (Document(metadata={'author': 'Federal Reserve', 'page_label': '3', 'moddate': '2025-09-17T11:53:16-04:00', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'title': 'Federal Reserve issues FOMC statement', 'source_file': 'monetary20250917a1.pdf', 'page': 2, 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7536895393972114), (Document(metadata={'file_date': 202509170000, 'source': './unstructured_data/monetary20250917a1.pdf', 'moddate': '2025-09-17T11:53:16-04:00', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page': 0, 'creator': 'Acrobat PDFMaker 25 for Word', 'page_label': '1', 'producer': 'Adobe PDF Library 25.1.97', 'creationdate': '2025-09-17T11:48:45-04:00', 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7255547689219765)]\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", "\n", "[+] Local confidence is sufficient.\n" ] @@ -617,7 +659,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -691,7 +733,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "cellView": "form", "colab": { @@ -710,7 +752,7 @@ } ], "source": [ - "#This is a diagnostic code. No need to run if everything works\n", + "\n", "# @title\n", "from langchain_community.retrievers import BM25Retriever\n", "\n", @@ -745,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": { "id": "b75dd440" }, @@ -828,7 +870,7 @@ "output_type": "stream", "text": [ "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", - "Confidence Score: 0.7484\n", + "Confidence Score: 0.7586\n", "Action: LOCAL DATA SUFFICIENT\n", "[Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='execute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:'), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='announced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025.')]\n" ] From 0b904ae4288226aab54132c7ee863349033efae9 Mon Sep 17 00:00:00 2001 From: hongxu69 Date: Sat, 8 Aug 2026 22:25:54 -0400 Subject: [PATCH 22/24] introduce semantic boundaries --- .../BAA10Y_forecasting/00_RagPrototype.ipynb | 18831 ++++++++-------- 1 file changed, 9335 insertions(+), 9496 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb index 7f83dfde..400c6018 100644 --- a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb +++ b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb @@ -1,9520 +1,9359 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "3_AMgsqVfjJ2" - }, - "source": [ - "In order to run this NB (=notebook), one needs to have a HuggingFace account and Token (say: \"nimportequel\"). Put the following into file .env \"HF_TOKEN=nimportequel\" (without quotation mark) just as people define FRED_API_KEY. Then one also needs to set JupyterServer to our project .venv. Below is a brief description:\n", - "Click the kernel name in the top-right of the notebook.\n", - "It’s the only clickable item on the right side of the toolbar.\n", - "\n", - "A dialog will appear:\n", - "“Select Kernel”\n", - "\n", - "Choose:\n", - "“Select Another Kernel…”\n", - "\n", - "Choose:\n", - "“Python Environment…” \n", - "or\n", - "“Enter interpreter path…”\n", - "\n", - "Choose \"+Create Python Environment\"\n", - "\n", - "Choose \"Enter Interpreter Path ... Browse and Select a Python interpreter from anywhere\" \n", - "(in my coder workspace, this item is the last option being listed)\n", - "Paste your full interpreter path:\n", - "\n", - "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "636f09e2", - "outputId": "be85a077-11cf-4f16-f96d-bcbc0d44fcf7" - }, - "outputs": [], - "source": [ - "#%pip install langchain langchain-community langchain-huggingface sentence-transformers pypdf rank_bm25" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4yL7nbpAgEtK" - }, - "source": [ - "Run the following to see if access to HF is OK." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7aff7458", - "outputId": "7cb84e13-ac89-4019-a5c3-8e7db85a65cb" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully logged in to Hugging Face.\n" - ] - } - ], - "source": [ - "# @title\n", - "from dotenv import load_dotenv\n", - "import os\n", - "from huggingface_hub import notebook_login, login\n", - "#from google.colab import userdata\n", - "\n", - "try:\n", - " #hf_token = userdata.get('HF_TOKEN')\n", - " load_dotenv() # walks up from cwd to find the nearest .env\n", - " hf_token = os.environ.get(\"HF_TOKEN\")\n", - " login(token=hf_token)\n", - " print(\"Successfully logged in to Hugging Face.\")\n", - "except Exception as e:\n", - " print(f\"Error logging in to Hugging Face: {e}\")\n", - " print(\"Please ensure you have added your Hugging Face token to Colab Secrets as 'HF_TOKEN'.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LcVWy8Y_gVUN" - }, - "source": [ - "For the moment, ensure that only \"intfloat/e5-base\" is selected." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "f3fb7592", - "outputId": "1c15426c-b8c0-487f-d5f5-155b784e0de1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Embedding model selected: intfloat/e5-base\n", - "Model kwargs: {}\n" - ] - } - ], - "source": [ - "# @title Configure Embedding Model\n", - "EMBEDDING_MODEL_NAME = \"intfloat/e5-base\" # @param [\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\", \"all-MiniLM-L6-v2\"]\n", - "MODEL_KWARGS = {} # @param {type:\"raw\"}\n", - "E5_MODEL_NAMES = {\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\"}\n", - "\n", - "#Do NOT use in bootcamp because of potential license issue\n", - "# If using FinanceMTEB/fin-e5-base, it might require trust_remote_code=True\n", - "if EMBEDDING_MODEL_NAME == \"FinanceMTEB/fin-e5-base\":\n", - " MODEL_KWARGS = {'trust_remote_code': True}\n", - "\n", - "print(f\"Embedding model selected: {EMBEDDING_MODEL_NAME}\")\n", - "print(f\"Model kwargs: {MODEL_KWARGS}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "#v001\n", - "from langchain_huggingface import HuggingFaceEmbeddings\n", - "from typing import List\n", - "\n", - "class E5Embeddings(HuggingFaceEmbeddings):\n", - " \"\"\"HuggingFaceEmbeddings + E5's required 'query: '/'passage: ' prefixes.\n", - "\n", - " E5-family models (intfloat/e5-*, FinanceMTEB/fin-e5-*) are trained with\n", - " these prefixes; omitting them degrades retrieval. Only use this wrapper\n", - " for E5-family models — all-MiniLM-L6-v2 was not trained with this\n", - " convention and should be embedded unprefixed.\n", - " \"\"\"\n", - "\n", - " def embed_documents(self, texts: List[str]) -> List[List[float]]:\n", - " return super().embed_documents([f\"passage: {t}\" for t in texts])\n", - "\n", - " def embed_query(self, text: str) -> List[float]:\n", - " return super().embed_query(f\"query: {text}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "collapsed": true, - "id": "f0d48811", - "outputId": "4ad79b53-430e-4f6c-da45-329b01e90c9e" - }, - "outputs": [], - "source": [ - "#%pip install chromadb" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "3_AMgsqVfjJ2" + }, + "source": [ + "In order to run this NB (=notebook), one needs to have a HuggingFace account and Token (say: \"nimportequel\"). Put the following into file .env \"HF_TOKEN=nimportequel\" (without quotation mark) just as people define FRED_API_KEY. Then one also needs to set JupyterServer to our project .venv. Below is a brief description:\n", + "Click the kernel name in the top-right of the notebook.\n", + "It’s the only clickable item on the right side of the toolbar.\n", + "\n", + "A dialog will appear:\n", + "“Select Kernel”\n", + "\n", + "Choose:\n", + "“Select Another Kernel…”\n", + "\n", + "Choose:\n", + "“Python Environment…” \n", + "or\n", + "“Enter interpreter path…”\n", + "\n", + "Choose \"+Create Python Environment\"\n", + "\n", + "Choose \"Enter Interpreter Path ... Browse and Select a Python interpreter from anywhere\" \n", + "(in my coder workspace, this item is the last option being listed)\n", + "Paste your full interpreter path:\n", + "\n", + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "metadata": { - "id": "2APCwzjIhHri" - }, - "source": [ - "Use the immediately following RAG to create vector store because it uses an advanced embedding model pretrained for financial sector. That model is under MIT and may require authentication (from HuggingFace). Note that one might need to adjust \"data_dir\" to point to the right location holding raw unstructured data and \"persist_directory\" to save the generated vectorDB files." - ] + "collapsed": true, + "id": "636f09e2", + "outputId": "be85a077-11cf-4f16-f96d-bcbc0d44fcf7" + }, + "outputs": [], + "source": [ + "#%pip install langchain langchain-community langchain-huggingface sentence-transformers pypdf rank_bm25" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4yL7nbpAgEtK" + }, + "source": [ + "Run the following to see if access to HF is OK." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "7aff7458", + "outputId": "7cb84e13-ac89-4019-a5c3-8e7db85a65cb" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 907, - "referenced_widgets": [ - "32ee4014efb147e59d7306c034db6f3d", - 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"text": [ - "/tmp/ipykernel_4625/1185560718.py:11: DeprecationWarning: `langchain-community` is being sunset and is no longer actively maintained. See https://github.com/langchain-ai/langchain-community/issues/674 for details and migration guidance toward standalone integration packages.\n", - " from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Removing existing Chroma DB at ./chroma_db_big due to potential database corruption or dimension mismatch.\n", - "Processing 2 files...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Loading weights: 100%|██████████| 199/199 [00:00<00:00, 1015.66it/s]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ChromaDB PersistentClient successfully created at ./chroma_db_big.\n", - "Successfully indexed 50 chunks and saved to ./chroma_db_big.\n" - ] - } - ], - "source": [ - "# 1. Force upgrade to modern library structures\n", - "#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pycd ..pdf\n", - "\n", - "import os\n", - "import re\n", - "import shutil # Import shutil for directory removal\n", - "import whisper\n", - "import chromadb # Explicitly import chromadb for client creation\n", - "import datetime # Import datetime for date parsing\n", - "from langchain_core.documents import Document\n", - "from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n", - "from langchain_text_splitters import RecursiveCharacterTextSplitter\n", - "from langchain_huggingface import HuggingFaceEmbeddings\n", - "from langchain_community.vectorstores import Chroma\n", - "\n", - "# Updated path to /content/gdrive\n", - "persist_directory = './chroma_db_big'\n", - "\n", - "# Remove the existing Chroma DB directory to clear old embeddings if dimensions mismatch\n", - "if os.path.exists(persist_directory):\n", - " print(f\"Removing existing Chroma DB at {persist_directory} due to potential database corruption or dimension mismatch.\")\n", - " shutil.rmtree(persist_directory)\n", - "\n", - "os.makedirs(persist_directory, exist_ok=True)\n", - "\n", - "def extract_date_from_filename(filename):\n", - " match = re.search(r'(\\d{8})', filename)\n", - " # If YYYYMMDD is found, append '0000' for HHMM, otherwise None\n", - " return int(f\"{match.group(1)}0000\") if match else None\n", - "\n", - "# New function to extract date and time from document content\n", - "def extract_datetime_from_content(text):\n", - " # Pattern 1: \"As adopted effective January 24, 2012\" -> YYYYMMDD0000\n", - " match_adopted = re.search(r\"As adopted effective (January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text)\n", - " if match_adopted:\n", - " month_name, day, year = match_adopted.groups()\n", - " month_num = datetime.datetime.strptime(month_name, '%B').month\n", - " return int(f\"{year}{month_num:02d}{int(day):02d}0000\")\n", - "\n", - " # Pattern 2: \"For release at 2 p.m. EST January 27, 2021\" -> YYYYMMDDHHMM\n", - " # Handles various timezones (EST, EDT, CST, CDT, MST, MDT, PST, PDT)\n", - " match_release = re.search(r\"For release at (\\d{1,2})\\s*(a\\.m\\.|p\\.m\\.)\\s*(EST|EDT|CST|CDT|MST|MDT|PST|PDT)?\\s*(January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text, re.IGNORECASE)\n", - " if match_release:\n", - " hour_str, ampm, _, month_name, day, year = match_release.groups()\n", - " hour = int(hour_str)\n", - " if ampm.lower() == 'p.m.' and hour != 12:\n", - " hour += 12\n", - " elif ampm.lower() == 'a.m.' and hour == 12: # 12 a.m. is midnight\n", - " hour = 0\n", - " month_num = datetime.datetime.strptime(month_name, '%B').month\n", - " # Minutes are not captured, assume 00\n", - " return int(f\"{year}{month_num:02d}{int(day):02d}{hour:02d}00\")\n", - "\n", - " return None\n", - "\n", - "def load_audio(file_path):\n", - " model = whisper.load_model(\"base\")\n", - " result = model.transcribe(file_path)\n", - " return [Document(page_content=result[\"text\"], metadata={\"source\": file_path})]\n", - "\n", - "raw_docs = []\n", - "data_dir = './unstructured_data' #need to adjust/customize the directory for the raw files location\n", - "supported_exts = ('.pdf', '.docx', '.pptx', '.mp3', '.wav')\n", - "files = [f for f in os.listdir(data_dir) if f.lower().endswith(supported_exts)]\n", - "print(f\"Processing {len(files)} files...\")\n", - "\n", - "for file in files:\n", - " file_path = os.path.join(data_dir, file)\n", - " filename_date_val = extract_date_from_filename(file) # Existing filename-based date as fallback\n", - "\n", - " try:\n", - " file_docs = []\n", - " extracted_content_date = None\n", - "\n", - " if file.lower().endswith('.pdf'):\n", - " file_docs = PyPDFLoader(file_path).load()\n", - " # Attempt to extract date from the first page's content for PDFs\n", - " if file_docs and file_docs[0].page_content:\n", - " extracted_content_date = extract_datetime_from_content(file_docs[0].page_content)\n", - " elif file.lower().endswith('.docx'):\n", - " file_docs = Docx2txtLoader(file_path).load()\n", - " elif file.lower().endswith('.pptx'):\n", - " file_docs = UnstructuredPowerPointLoader(file_path).load()\n", - " elif file.lower().endswith(('.mp3', '.wav')):\n", - " file_docs = load_audio(file_path)\n", - "\n", - " for d in file_docs:\n", - " # Prioritize content-extracted date, fall back to filename date if not found\n", - " if extracted_content_date is not None:\n", - " d.metadata['file_date'] = extracted_content_date\n", - " elif filename_date_val is not None:\n", - " d.metadata['file_date'] = filename_date_val\n", - " else:\n", - " d.metadata['file_date'] = None # No date found from either source\n", - "\n", - " d.metadata['source_file'] = file\n", - " raw_docs.extend(file_docs)\n", - " except Exception as e:\n", - " print(f\"Error processing {file}: {e}\")\n", - "\n", - "if raw_docs:\n", - " text_splitter = RecursiveCharacterTextSplitter(chunk_size=250, chunk_overlap=50) # Using a reasonable chunk size for E5 models\n", - " splits = text_splitter.split_documents(raw_docs)\n", - " #embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n", - " #20260808\n", - " if EMBEDDING_MODEL_NAME in E5_MODEL_NAMES:\n", - " embeddings = E5Embeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", - " else:\n", - " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", - "\n", - " # Explicitly create the ChromaDB client\n", - " try:\n", - " client = chromadb.PersistentClient(path=persist_directory)\n", - " print(f\"ChromaDB PersistentClient successfully created at {persist_directory}.\")\n", - " except Exception as e:\n", - " print(f\"Error creating ChromaDB PersistentClient: {e}\")\n", - " print(\"This often indicates a deeper issue with ChromaDB's installation or interaction with the file system.\")\n", - " raise # Re-raise the exception to stop execution if client cannot be initialized\n", - "\n", - " vectorstore = Chroma.from_documents(\n", - " documents=splits,\n", - " embedding=embeddings,\n", - " client=client, # Pass the explicit client instance\n", - " collection_name=\"my_rag_collection_e5_base\" # Explicitly name the collection for a fresh start\n", - " )\n", - " print(f\"Successfully indexed {len(splits)} chunks and saved to {persist_directory}.\")\n", - "else:\n", - " print(\"No documents found to index.\")" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n", + "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" + ] }, { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n", - "Name: openai-whisper\n", - "Version: 20250625\n", - "Location: /home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages\n", - "Requires: more-itertools, numba, numpy, tiktoken, torch, tqdm, triton\n", - "Required-by:\n" - ] - } - ], - "source": [ - "#Diagnostic\n", - "import sys\n", - "print(sys.executable)\n", - "\n", - "!uv pip show openai-whisper\n" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully logged in to Hugging Face.\n" + ] + } + ], + "source": [ + "# @title\n", + "from dotenv import load_dotenv\n", + "import os\n", + "from huggingface_hub import notebook_login, login\n", + "#from google.colab import userdata\n", + "\n", + "try:\n", + " #hf_token = userdata.get('HF_TOKEN')\n", + " load_dotenv() # walks up from cwd to find the nearest .env\n", + " hf_token = os.environ.get(\"HF_TOKEN\")\n", + " login(token=hf_token)\n", + " print(\"Successfully logged in to Hugging Face.\")\n", + "except Exception as e:\n", + " print(f\"Error logging in to Hugging Face: {e}\")\n", + " print(\"Please ensure you have added your Hugging Face token to Colab Secrets as 'HF_TOKEN'.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "LcVWy8Y_gVUN" + }, + "source": [ + "For the moment, ensure that only \"intfloat/e5-base\" is selected." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "f3fb7592", + "outputId": "1c15426c-b8c0-487f-d5f5-155b784e0de1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "4970b54e" - }, - "outputs": [], - "source": [ - "def normalize_date_filter_value(date_val, is_before):\n", - " \"\"\"\n", - " Normalizes a date filter value to YYYYMMDDHHMM format.\n", - " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", - " If date_val is YYYYMMDDHHMM, it's used as is.\n", - " \"\"\"\n", - " if date_val is None:\n", - " return None\n", - " date_str = str(date_val)\n", - " if len(date_str) == 8: # YYYYMMDD format\n", - " if is_before:\n", - " return int(date_str + \"2359\") # End of day for upper bound\n", - " else:\n", - " return int(date_str + \"0000\") # Beginning of day for lower bound\n", - " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", - " return int(date_str)\n", - " return None # Invalid format\n", - "\n", - "def query_rag_with_date_filter(query, date_before=None, date_after=None, threshold=0.4):\n", - " \"\"\"\n", - " Query Chroma with optional date restrictions.\n", - " Example: date_before=20260312, date_after=20250101\n", - " \"\"\"\n", - " search_kwargs = {\"k\": 3}\n", - " filters = []\n", - "\n", - " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", - " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", - "\n", - " if normalized_date_after:\n", - " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", - " if normalized_date_before:\n", - " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", - "\n", - " if filters:\n", - " if len(filters) == 1:\n", - " search_kwargs[\"filter\"] = filters[0]\n", - " else:\n", - " search_kwargs[\"filter\"] = {\"$and\": filters}\n", - "\n", - " print(f\"[DEBUG] Query: '{query}'\")\n", - " print(f\"[DEBUG] Search arguments for ChromaDB: {search_kwargs}\")\n", - "\n", - " # Get results with relevance scores (0 to 1 range)\n", - " results = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", - "\n", - " print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", - "\n", - " if not results:\n", - " return [], 0.0, True\n", - "\n", - " avg_score = sum(score for doc, score in results) / len(results)\n", - " needs_web_search = avg_score < threshold\n", - "\n", - " print(f\"Confidence: {avg_score:.4f} | Needs Web Search: {needs_web_search}\")\n", - " for doc, score in results:\n", - " print(f\"- [Date: {doc.metadata.get('file_date')}] {doc.metadata.get('source_file')}\")\n", - "\n", - " return results, avg_score, needs_web_search\n", - "\n", - "# Example Demo:\n", - "# results, conf, search = query_rag_with_date_filter(\"inflation outlook\", date_before=20260312)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Embedding model selected: intfloat/e5-base\n", + "Model kwargs: {}\n" + ] + } + ], + "source": [ + "# @title Configure Embedding Model\n", + "EMBEDDING_MODEL_NAME = \"intfloat/e5-base\" # @param [\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\", \"all-MiniLM-L6-v2\"]\n", + "MODEL_KWARGS = {} # @param {type:\"raw\"}\n", + "E5_MODEL_NAMES = {\"intfloat/e5-base\", \"FinanceMTEB/fin-e5-base\"}\n", + "\n", + "#Do NOT use in bootcamp because of potential license issue\n", + "# If using FinanceMTEB/fin-e5-base, it might require trust_remote_code=True\n", + "if EMBEDDING_MODEL_NAME == \"FinanceMTEB/fin-e5-base\":\n", + " MODEL_KWARGS = {'trust_remote_code': True}\n", + "\n", + "print(f\"Embedding model selected: {EMBEDDING_MODEL_NAME}\")\n", + "print(f\"Model kwargs: {MODEL_KWARGS}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "#v001\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from typing import List\n", + "\n", + "class E5Embeddings(HuggingFaceEmbeddings):\n", + " \"\"\"HuggingFaceEmbeddings + E5's required 'query: '/'passage: ' prefixes.\n", + "\n", + " E5-family models (intfloat/e5-*, FinanceMTEB/fin-e5-*) are trained with\n", + " these prefixes; omitting them degrades retrieval. Only use this wrapper\n", + " for E5-family models — all-MiniLM-L6-v2 was not trained with this\n", + " convention and should be embedded unprefixed.\n", + " \"\"\"\n", + "\n", + " def embed_documents(self, texts: List[str]) -> List[List[float]]:\n", + " return super().embed_documents([f\"passage: {t}\" for t in texts])\n", + "\n", + " def embed_query(self, text: str) -> List[float]:\n", + " return super().embed_query(f\"query: {text}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ARwU4OoUyj3X", - "outputId": "691329e5-b884-427f-f0b4-c0839a724879" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BM25 Document DB initialized.\n", - "[DEBUG] Query: 'holdings of Treasury securities'\n", - "[DEBUG] Search arguments for ChromaDB: {'k': 3}\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", - "Confidence: 0.7548 | Needs Web Search: False\n", - "- [Date: 202509170000] monetary20250917a1.pdf\n", - "- [Date: 202509170000] monetary20250917a1.pdf\n", - "- [Date: 202509170000] monetary20250917a1.pdf\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", - "\n", - "[+] Local confidence is sufficient.\n" - ] - } - ], - "source": [ - "from langchain_community.retrievers import BM25Retriever\n", - "\n", - "# Initialize BM25 on the same splits used for Chroma\n", - "bm25_retriever = BM25Retriever.from_documents(splits)\n", - "bm25_retriever.k = 3\n", - "\n", - "print(\"BM25 Document DB initialized.\")\n", - "\n", - "# Example usage of the date-filtered search:\n", - "# This will find documents strictly before March 12, 2026\n", - "query_text = \"holdings of Treasury securities\"\n", - "#\"target range for the federal funds rate\"\n", - "target_date = None\n", - "\n", - "results, confidence, needs_search = query_rag_with_date_filter(query_text, date_before=target_date, date_after=None)\n", - "\n", - "print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", - "if needs_search:\n", - " print(\"\\n[!] Local confidence is low. Recommendation: Search Google/Web for more info.\")\n", - "else:\n", - " print(\"\\n[+] Local confidence is sufficient.\")" - ] + "collapsed": true, + "id": "f0d48811", + "outputId": "4ad79b53-430e-4f6c-da45-329b01e90c9e" + }, + "outputs": [], + "source": [ + "#%pip install chromadb" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2APCwzjIhHri" + }, + "source": [ + "Use the immediately following RAG to create vector store because it uses an advanced embedding model pretrained for financial sector. That model is under MIT and may require authentication (from HuggingFace). Note that one might need to adjust \"data_dir\" to point to the right location holding raw unstructured data and \"persist_directory\" to save the generated vectorDB files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 907, + "referenced_widgets": [ + "32ee4014efb147e59d7306c034db6f3d", + "b4c38c159026419dba77661d6e0ee496", + "1a48f3acfad94d7ba04f6c6f8a74b111", + "dd6898d61bc146c0ab5904691244df82", + "f63e6ed7e5db404d8af13faa260afcf8", + "9b8fbd212d4847829fc635fa5aeac316", + "18cf57fe3470472cbd46b0019e0c20f0", + "92ed47aacd564569968a8898ebab9478", + "fe9a6c14229749369ac95dfa623544ab", + "4ab2027dcb744597bb4d966ddffc92d3", + "4bf48d4dd4074ecdad434820443a6638", + "b20b4eae8fee4b7ab735424daa4547e2", + "9d2339916b9c4965a57d30150000baa8", + "f9fabac9104f4a5187260b0b1c6787dc", + "2f9d6287218543a2bd73f02c8733a91b", + "2eea719d4a144c23816b44636dfba6b3", + "d210138bb9b549f7b878189b0847c56d", + 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"62f73c4e180c4922a7a3289bb86512b5", + "52e695bba4fc41ae88e935317eec7cc4", + "399ed8d2bdbb47caabdab766381f068e", + "7948a6eddb07433e8fb55a5259ce97bd", + "7ab99b6cc71040d9a7d4e3b6bd345a23", + "b7d53e5fb21142f497715d2ca5af45c2", + "09842ad2a0ef4f96868cbb3904132363", + "0b75d6ecd2bc48ad8ad8581830e16e41", + "7b8c6586036243d2a0a52346c4458b81", + "c9d4384744ec4cfa9e96226e8bd3942f", + "ca5a9fc86a214098b64d4e036aef78cf" + ] }, + "id": "a296cc99", + "outputId": "acaab241-0576-44dc-a078-9d5d3689a95b" + }, + "outputs": [], + "source": "# 1. Force upgrade to modern library structures\n#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pycd ..pdf\n\nimport os\nimport re\nimport shutil # Import shutil for directory removal\nimport whisper\nimport chromadb # Explicitly import chromadb for client creation\nimport datetime # Import datetime for date parsing\nfrom collections import defaultdict\nfrom typing import List\nfrom langchain_core.documents import Document\nfrom langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\nfrom langchain_huggingface import HuggingFaceEmbeddings\nfrom langchain_community.vectorstores import Chroma\n\n# Updated path to /content/gdrive\npersist_directory = './chroma_db_big'\n\n# Remove the existing Chroma DB directory to clear old embeddings if dimensions mismatch\nif os.path.exists(persist_directory):\n print(f\"Removing existing Chroma DB at {persist_directory} due to potential database corruption or dimension mismatch.\")\n shutil.rmtree(persist_directory)\n\nos.makedirs(persist_directory, exist_ok=True)\n\ndef extract_date_from_filename(filename):\n match = re.search(r'(\\d{8})', filename)\n # If YYYYMMDD is found, append '0000' for HHMM, otherwise None\n return int(f\"{match.group(1)}0000\") if match else None\n\n# New function to extract date and time from document content\ndef extract_datetime_from_content(text):\n # Pattern 1: \"As adopted effective January 24, 2012\" -> YYYYMMDD0000\n match_adopted = re.search(r\"As adopted effective (January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text)\n if match_adopted:\n month_name, day, year = match_adopted.groups()\n month_num = datetime.datetime.strptime(month_name, '%B').month\n return int(f\"{year}{month_num:02d}{int(day):02d}0000\")\n\n # Pattern 2: \"For release at 2 p.m. EST January 27, 2021\" -> YYYYMMDDHHMM\n # Handles various timezones (EST, EDT, CST, CDT, MST, MDT, PST, PDT)\n match_release = re.search(r\"For release at (\\d{1,2})\\s*(a\\.m\\.|p\\.m\\.)\\s*(EST|EDT|CST|CDT|MST|MDT|PST|PDT)?\\s*(January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text, re.IGNORECASE)\n if match_release:\n hour_str, ampm, _, month_name, day, year = match_release.groups()\n hour = int(hour_str)\n if ampm.lower() == 'p.m.' and hour != 12:\n hour += 12\n elif ampm.lower() == 'a.m.' and hour == 12: # 12 a.m. is midnight\n hour = 0\n month_num = datetime.datetime.strptime(month_name, '%B').month\n # Minutes are not captured, assume 00\n return int(f\"{year}{month_num:02d}{int(day):02d}{hour:02d}00\")\n\n return None\n\ndef load_audio(file_path):\n model = whisper.load_model(\"base\")\n result = model.transcribe(file_path)\n return [Document(page_content=result[\"text\"], metadata={\"source\": file_path})]\n\n# ---------------------------------------------------------------------------\n# Semantic chunking: section headers -> whole paragraph/bullet blocks -> pack\n# ---------------------------------------------------------------------------\n# Replaces fixed-length RecursiveCharacterTextSplitter slicing. Guarantees:\n# 1. A chunk never spans two different named sections.\n# 2. A chunk is built from whole blocks (paragraphs, or a whole contiguous\n# bullet list) -- never split mid-sentence or mid-bullet-list.\n# 3. Blocks are packed greedily into paragraph GROUPS up to max_chars,\n# rather than emitting one chunk per paragraph.\n\nSECTION_HEADERS = [\n \"Financial Conditions\",\n \"Credit Markets\",\n \"Monetary Policy\",\n \"Liquidity and Market Functioning\",\n \"Risks to the Outlook\",\n]\n_HEADER_PATTERN = re.compile(r\"(?m)^(\" + \"|\".join(re.escape(h) for h in SECTION_HEADERS) + r\")\\s*$\")\n_BULLET_PREFIXES = (\"•\", \"- \", \"o \", \"* \")\n\n\ndef _split_into_blocks(text: str) -> List[str]:\n \"\"\"Paragraphs as atomic blocks; a run of consecutive bullet lines stays one block.\"\"\"\n raw_blocks = re.split(r\"\\n\\s*\\n\", text.strip())\n blocks, buffer = [], \"\"\n for raw in raw_blocks:\n raw = raw.strip()\n if not raw:\n continue\n is_bullet = raw.lstrip().startswith(_BULLET_PREFIXES)\n if is_bullet and buffer.lstrip().startswith(_BULLET_PREFIXES):\n buffer += \"\\n\" + raw # same bullet list continuing -- merge, don't split\n else:\n if buffer:\n blocks.append(buffer)\n buffer = raw\n if buffer:\n blocks.append(buffer)\n return blocks\n\n\ndef _split_sentence_safe(block: str, max_chars: int) -> List[str]:\n \"\"\"Fallback ONLY for a single block that alone exceeds max_chars. Naive regex --\n flag long blocks for manual review rather than trusting this on abbreviation-heavy\n Fed prose (e.g. 'U.S.'); nltk/spacy sentence tokenizers are sturdier if this matters.\"\"\"\n sentences = re.split(r\"(?<=[.!?])\\s+(?=[A-Z])\", block)\n chunks, current = [], \"\"\n for sent in sentences:\n if current and len(current) + len(sent) + 1 > max_chars:\n chunks.append(current)\n current = sent\n else:\n current = f\"{current} {sent}\".strip()\n if current:\n chunks.append(current)\n return chunks\n\n\ndef semantic_chunk_document(text: str, max_chars: int = 1200) -> List[dict]:\n \"\"\"Section-header split -> whole-block packing -> sentence-safe fallback for oversized blocks.\"\"\"\n matches = list(_HEADER_PATTERN.finditer(text))\n if not matches:\n sections = [(\"unlabeled\", text)] # e.g. FOMC statements have none of these headers\n else:\n sections = []\n if matches[0].start() > 0:\n sections.append((\"preamble\", text[: matches[0].start()]))\n for i, m in enumerate(matches):\n end = matches[i + 1].start() if i + 1 < len(matches) else len(text)\n sections.append((m.group(1), text[m.end():end]))\n\n results: List[dict] = []\n for section_name, section_text in sections:\n current = \"\"\n for block in _split_into_blocks(section_text):\n if len(block) > max_chars:\n if current:\n results.append({\"section\": section_name, \"text\": current})\n current = \"\"\n results.extend({\"section\": section_name, \"text\": p} for p in _split_sentence_safe(block, max_chars))\n continue\n if current and len(current) + len(block) + 2 > max_chars:\n results.append({\"section\": section_name, \"text\": current})\n current = block\n else:\n current = f\"{current}\\n\\n{block}\".strip()\n if current:\n results.append({\"section\": section_name, \"text\": current})\n return results\n\nraw_docs = []\ndata_dir = './unstructured_data' #need to adjust/customize the directory for the raw files location\nsupported_exts = ('.pdf', '.docx', '.pptx', '.mp3', '.wav')\nfiles = [f for f in os.listdir(data_dir) if f.lower().endswith(supported_exts)]\nprint(f\"Processing {len(files)} files...\")\n\nfor file in files:\n file_path = os.path.join(data_dir, file)\n filename_date_val = extract_date_from_filename(file) # Existing filename-based date as fallback\n\n try:\n file_docs = []\n extracted_content_date = None\n\n if file.lower().endswith('.pdf'):\n file_docs = PyPDFLoader(file_path).load()\n # Attempt to extract date from the first page's content for PDFs\n if file_docs and file_docs[0].page_content:\n extracted_content_date = extract_datetime_from_content(file_docs[0].page_content)\n elif file.lower().endswith('.docx'):\n file_docs = Docx2txtLoader(file_path).load()\n elif file.lower().endswith('.pptx'):\n file_docs = UnstructuredPowerPointLoader(file_path).load()\n elif file.lower().endswith(('.mp3', '.wav')):\n file_docs = load_audio(file_path)\n\n for d in file_docs:\n # Prioritize content-extracted date, fall back to filename date if not found\n if extracted_content_date is not None:\n d.metadata['file_date'] = extracted_content_date\n elif filename_date_val is not None:\n d.metadata['file_date'] = filename_date_val\n else:\n d.metadata['file_date'] = None # No date found from either source\n\n d.metadata['source_file'] = file\n raw_docs.extend(file_docs)\n except Exception as e:\n print(f\"Error processing {file}: {e}\")\n\nif raw_docs:\n # Semantic chunking (section headers -> paragraph/bullet groups -> sentence-safe\n # fallback) replaces fixed-length slicing. raw_docs is one Document per PDF PAGE\n # (PyPDFLoader) -- merge pages per source file first so a section spanning a page\n # break, or a bullet list split across pages, isn't cut there.\n pages_by_file = defaultdict(list)\n for d in raw_docs:\n pages_by_file[d.metadata[\"source_file\"]].append(d)\n\n splits: List[Document] = []\n for source_file, pages in pages_by_file.items():\n full_text = \"\\n\\n\".join(p.page_content for p in pages)\n file_meta = {\"file_date\": pages[0].metadata.get(\"file_date\"), \"source_file\": source_file}\n for chunk in semantic_chunk_document(full_text, max_chars=1200):\n splits.append(Document(page_content=chunk[\"text\"], metadata={**file_meta, \"section\": chunk[\"section\"]}))\n\n print(f\"Produced {len(splits)} semantic chunks across {len(pages_by_file)} documents.\")\n\n #embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n #20260808\n if EMBEDDING_MODEL_NAME in E5_MODEL_NAMES:\n embeddings = E5Embeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n else:\n embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n\n # Explicitly create the ChromaDB client\n try:\n client = chromadb.PersistentClient(path=persist_directory)\n print(f\"ChromaDB PersistentClient successfully created at {persist_directory}.\")\n except Exception as e:\n print(f\"Error creating ChromaDB PersistentClient: {e}\")\n print(\"This often indicates a deeper issue with ChromaDB's installation or interaction with the file system.\")\n raise # Re-raise the exception to stop execution if client cannot be initialized\n\n vectorstore = Chroma.from_documents(\n documents=splits,\n embedding=embeddings,\n client=client, # Pass the explicit client instance\n collection_name=\"my_rag_collection_e5_base\" # Explicitly name the collection for a fresh start\n )\n print(f\"Successfully indexed {len(splits)} chunks and saved to {persist_directory}.\")\nelse:\n print(\"No documents found to index.\")" + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "77388dbf", - "outputId": "317ec7e6-b415-4b86-c542-4190fd7bd562" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Successfully connected to Chroma. Found 50 chunks.\n", - "Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\n" - ] - } - ], - "source": [ - "# @title\n", - "#%pip install -q -U langchain-chroma langchain-community langchain-core rank_bm25 langchain-huggingface\n", - "\n", - "import os\n", - "from langchain_chroma import Chroma\n", - "from langchain_huggingface import HuggingFaceEmbeddings\n", - "from langchain_community.retrievers import BM25Retriever\n", - "from langchain_core.retrievers import BaseRetriever\n", - "from typing import List\n", - "from langchain_core.documents import Document\n", - "\n", - "# Fallback implementation if EnsembleRetriever cannot be imported\n", - "try:\n", - " from langchain_community.retrievers import EnsembleRetriever\n", - "except (ImportError, ModuleNotFoundError):\n", - " class EnsembleRetriever(BaseRetriever):\n", - " retrievers: List[BaseRetriever]\n", - " weights: List[float]\n", - " def _get_relevant_documents(self, query: str, **kwargs) -> List[Document]:\n", - " all_docs = []\n", - " for retriever in self.retrievers:\n", - " all_docs.extend(retriever.get_relevant_documents(query))\n", - " return all_docs[:5]\n", - "\n", - "if 'embeddings' not in locals():\n", - " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", - "\n", - "persist_directory = './chroma_db_big'\n", - "\n", - "try:\n", - " vectorstore = Chroma(\n", - " persist_directory=persist_directory,\n", - " embedding_function=embeddings,\n", - " collection_name=\"my_rag_collection_e5_base\" # Added collection name\n", - " )\n", - " count = vectorstore._collection.count()\n", - " print(f\"Successfully connected to Chroma. Found {count} chunks.\")\n", - "except Exception as e:\n", - " print(f\"Error connecting to database: {e}\")\n", - "\n", - "if 'vectorstore' in locals():\n", - " chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", - "\n", - " if 'splits' in locals() and splits:\n", - " bm25_retriever = BM25Retriever.from_documents(splits)\n", - " bm25_retriever.k = 3\n", - " ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5])\n", - " print(\"Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\")\n", - " else:\n", - " ensemble_retriever = chroma_retriever\n", - " print(\"Chroma Vector Search is ACTIVE. (BM25 requires 'splits' in memory)\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/bin/python\n", + "Name: openai-whisper\n", + "Version: 20250625\n", + "Location: /home/coder/agentic-forecasting-c1-bmo-one/implementations/BAA10Y_forecasting/.venv/lib/python3.12/site-packages\n", + "Requires: more-itertools, numba, numpy, tiktoken, torch, tqdm, triton\n", + "Required-by:\n" + ] + } + ], + "source": [ + "#Diagnostic\n", + "import sys\n", + "print(sys.executable)\n", + "\n", + "!uv pip show openai-whisper\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "4970b54e" + }, + "outputs": [], + "source": [ + "def normalize_date_filter_value(date_val, is_before):\n", + " \"\"\"\n", + " Normalizes a date filter value to YYYYMMDDHHMM format.\n", + " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", + " If date_val is YYYYMMDDHHMM, it's used as is.\n", + " \"\"\"\n", + " if date_val is None:\n", + " return None\n", + " date_str = str(date_val)\n", + " if len(date_str) == 8: # YYYYMMDD format\n", + " if is_before:\n", + " return int(date_str + \"2359\") # End of day for upper bound\n", + " else:\n", + " return int(date_str + \"0000\") # Beginning of day for lower bound\n", + " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", + " return int(date_str)\n", + " return None # Invalid format\n", + "\n", + "def query_rag_with_date_filter(query, date_before=None, date_after=None, threshold=0.4):\n", + " \"\"\"\n", + " Query Chroma with optional date restrictions.\n", + " Example: date_before=20260312, date_after=20250101\n", + " \"\"\"\n", + " search_kwargs = {\"k\": 3}\n", + " filters = []\n", + "\n", + " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", + " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", + "\n", + " if normalized_date_after:\n", + " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", + " if normalized_date_before:\n", + " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", + "\n", + " if filters:\n", + " if len(filters) == 1:\n", + " search_kwargs[\"filter\"] = filters[0]\n", + " else:\n", + " search_kwargs[\"filter\"] = {\"$and\": filters}\n", + "\n", + " print(f\"[DEBUG] Query: '{query}'\")\n", + " print(f\"[DEBUG] Search arguments for ChromaDB: {search_kwargs}\")\n", + "\n", + " # Get results with relevance scores (0 to 1 range)\n", + " results = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", + "\n", + " print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", + "\n", + " if not results:\n", + " return [], 0.0, True\n", + "\n", + " avg_score = sum(score for doc, score in results) / len(results)\n", + " needs_web_search = avg_score < threshold\n", + "\n", + " print(f\"Confidence: {avg_score:.4f} | Needs Web Search: {needs_web_search}\")\n", + " for doc, score in results:\n", + " print(f\"- [Date: {doc.metadata.get('file_date')}] {doc.metadata.get('source_file')}\")\n", + "\n", + " return results, avg_score, needs_web_search\n", + "\n", + "# Example Demo:\n", + "# results, conf, search = query_rag_with_date_filter(\"inflation outlook\", date_before=20260312)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ARwU4OoUyj3X", + "outputId": "691329e5-b884-427f-f0b4-c0839a724879" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "cellView": "form", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0b63c19d", - "outputId": "8710cf00-6d2c-465e-a578-3d115e1e6750" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hybrid EnsembleRetriever initialized successfully.\n" - ] - } - ], - "source": [ - "\n", - "# @title\n", - "from langchain_community.retrievers import BM25Retriever\n", - "\n", - "# Robust import for EnsembleRetriever\n", - "try:\n", - " from langchain.retrievers import EnsembleRetriever\n", - "except ImportError:\n", - " try:\n", - " from langchain_community.retrievers.ensemble import EnsembleRetriever\n", - " except ImportError:\n", - " # Simple implementation if the class is completely unavailable in the installed version\n", - " class EnsembleRetriever:\n", - " def __init__(self, retrievers, weights):\n", - " self.retrievers = retrievers\n", - " self.weights = weights\n", - " def invoke(self, query):\n", - " return self.retrievers[0].invoke(query)\n", - "\n", - "# Initialize Retrievers\n", - "chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", - "bm25_retriever = BM25Retriever.from_documents(splits)\n", - "bm25_retriever.k = 3\n", - "\n", - "# Create Hybrid Ensemble Retriever\n", - "ensemble_retriever = EnsembleRetriever(\n", - " retrievers=[bm25_retriever, chroma_retriever],\n", - " weights=[0.5, 0.5]\n", - ")\n", - "\n", - "print(\"Hybrid EnsembleRetriever initialized successfully.\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "BM25 Document DB initialized.\n", + "[DEBUG] Query: 'holdings of Treasury securities'\n", + "[DEBUG] Search arguments for ChromaDB: {'k': 3}\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", + "Confidence: 0.7548 | Needs Web Search: False\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "- [Date: 202509170000] monetary20250917a1.pdf\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", + "\n", + "[+] Local confidence is sufficient.\n" + ] + } + ], + "source": [ + "from langchain_community.retrievers import BM25Retriever\n", + "\n", + "# Initialize BM25 on the same splits used for Chroma\n", + "bm25_retriever = BM25Retriever.from_documents(splits)\n", + "bm25_retriever.k = 3\n", + "\n", + "print(\"BM25 Document DB initialized.\")\n", + "\n", + "# Example usage of the date-filtered search:\n", + "# This will find documents strictly before March 12, 2026\n", + "query_text = \"holdings of Treasury securities\"\n", + "#\"target range for the federal funds rate\"\n", + "target_date = None\n", + "\n", + "results, confidence, needs_search = query_rag_with_date_filter(query_text, date_before=target_date, date_after=None)\n", + "\n", + "print(f\"[DEBUG] Raw results from vectorstore: {results}\")\n", + "if needs_search:\n", + " print(\"\\n[!] Local confidence is low. Recommendation: Search Google/Web for more info.\")\n", + "else:\n", + " print(\"\\n[+] Local confidence is sufficient.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "77388dbf", + "outputId": "317ec7e6-b415-4b86-c542-4190fd7bd562" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "b75dd440" - }, - "outputs": [], - "source": [ - "def normalize_date_filter_value(date_val, is_before):\n", - " \"\"\"\n", - " Normalizes a date filter value to YYYYMMDDHHMM format.\n", - " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", - " If date_val is YYYYMMDDHHMM, it's used as is.\n", - " \"\"\"\n", - " if date_val is None:\n", - " return None\n", - " date_str = str(date_val)\n", - " if len(date_str) == 8: # YYYYMMDD format\n", - " if is_before:\n", - " return int(date_str + \"2359\") # End of day for upper bound\n", - " else:\n", - " return int(date_str + \"0000\") # Beginning of day for lower bound\n", - " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", - " return int(date_str)\n", - " return None # Invalid format\n", - "\n", - "def query_hybrid_rag(query, date_before=None, date_after=None, threshold=0.4):\n", - " \"\"\"\n", - " Queries the Hybrid Ensemble Retriever and calculates confidence.\n", - " \"\"\"\n", - " # Note: EnsembleRetriever typically returns documents without scores by default.\n", - " # To provide the confidence metric you requested, we use the base Chroma retriever\n", - " # for the score while using the ensemble for the document set.\n", - " search_kwargs = {\"k\": 3}\n", - " filters = []\n", - "\n", - " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", - " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", - "\n", - " if normalized_date_after:\n", - " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", - " if normalized_date_before:\n", - " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", - "\n", - " if filters:\n", - " if len(filters) == 1:\n", - " search_kwargs[\"filter\"] = filters[0]\n", - " else:\n", - " search_kwargs[\"filter\"] = {\"$and\": filters}\n", - "\n", - " # Get ensemble results\n", - " docs = ensemble_retriever.invoke(query)\n", - "\n", - " # Get scores specifically from Chroma for the confidence metric\n", - " results_with_scores = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", - "\n", - " avg_score = sum(score for doc, score in results_with_scores) / len(results_with_scores) if results_with_scores else 0.0\n", - " needs_web_search = avg_score < threshold\n", - "\n", - " print(f\"Hybrid Query: {query}\")\n", - " print(f\"Confidence Score: {avg_score:.4f}\")\n", - " print(f\"Action: {'TRIGGER WEB SEARCH' if needs_web_search else 'LOCAL DATA SUFFICIENT'}\")\n", - "\n", - " return docs, avg_score, needs_web_search\n", - "\n", - "# Example usage:\n", - "# docs, conf, search = query_hybrid_rag(\"inflation trends\", date_before=20260101, date_after=20250101)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully connected to Chroma. Found 50 chunks.\n", + "Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\n" + ] + } + ], + "source": [ + "# @title\n", + "#%pip install -q -U langchain-chroma langchain-community langchain-core rank_bm25 langchain-huggingface\n", + "\n", + "import os\n", + "from langchain_chroma import Chroma\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain_community.retrievers import BM25Retriever\n", + "from langchain_core.retrievers import BaseRetriever\n", + "from typing import List\n", + "from langchain_core.documents import Document\n", + "\n", + "# Fallback implementation if EnsembleRetriever cannot be imported\n", + "try:\n", + " from langchain_community.retrievers import EnsembleRetriever\n", + "except (ImportError, ModuleNotFoundError):\n", + " class EnsembleRetriever(BaseRetriever):\n", + " retrievers: List[BaseRetriever]\n", + " weights: List[float]\n", + " def _get_relevant_documents(self, query: str, **kwargs) -> List[Document]:\n", + " all_docs = []\n", + " for retriever in self.retrievers:\n", + " all_docs.extend(retriever.get_relevant_documents(query))\n", + " return all_docs[:5]\n", + "\n", + "if 'embeddings' not in locals():\n", + " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", + "\n", + "persist_directory = './chroma_db_big'\n", + "\n", + "try:\n", + " vectorstore = Chroma(\n", + " persist_directory=persist_directory,\n", + " embedding_function=embeddings,\n", + " collection_name=\"my_rag_collection_e5_base\" # Added collection name\n", + " )\n", + " count = vectorstore._collection.count()\n", + " print(f\"Successfully connected to Chroma. Found {count} chunks.\")\n", + "except Exception as e:\n", + " print(f\"Error connecting to database: {e}\")\n", + "\n", + "if 'vectorstore' in locals():\n", + " chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", + "\n", + " if 'splits' in locals() and splits:\n", + " bm25_retriever = BM25Retriever.from_documents(splits)\n", + " bm25_retriever.k = 3\n", + " ensemble_retriever = EnsembleRetriever(retrievers=[bm25_retriever, chroma_retriever], weights=[0.5, 0.5])\n", + " print(\"Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\")\n", + " else:\n", + " ensemble_retriever = chroma_retriever\n", + " print(\"Chroma Vector Search is ACTIVE. (BM25 requires 'splits' in memory)\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "0b63c19d", + "outputId": "8710cf00-6d2c-465e-a578-3d115e1e6750" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iOeipKrUM9Zf", - "outputId": "cb768336-771a-49bd-c966-ead9b3328941" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", - "Confidence Score: 0.7586\n", - "Action: LOCAL DATA SUFFICIENT\n", - "[Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='execute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:'), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='announced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025.')]\n" - ] - } - ], - "source": [ - "docs, conf, search = query_hybrid_rag(\"Increase the System Open Market Account holdings of Treasury securities by how much\", date_before=202601010000)\n", - "print(docs)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Hybrid EnsembleRetriever initialized successfully.\n" + ] + } + ], + "source": [ + "\n", + "# @title\n", + "from langchain_community.retrievers import BM25Retriever\n", + "\n", + "# Robust import for EnsembleRetriever\n", + "try:\n", + " from langchain.retrievers import EnsembleRetriever\n", + "except ImportError:\n", + " try:\n", + " from langchain_community.retrievers.ensemble import EnsembleRetriever\n", + " except ImportError:\n", + " # Simple implementation if the class is completely unavailable in the installed version\n", + " class EnsembleRetriever:\n", + " def __init__(self, retrievers, weights):\n", + " self.retrievers = retrievers\n", + " self.weights = weights\n", + " def invoke(self, query):\n", + " return self.retrievers[0].invoke(query)\n", + "\n", + "# Initialize Retrievers\n", + "chroma_retriever = vectorstore.as_retriever(search_kwargs={'k': 3})\n", + "bm25_retriever = BM25Retriever.from_documents(splits)\n", + "bm25_retriever.k = 3\n", + "\n", + "# Create Hybrid Ensemble Retriever\n", + "ensemble_retriever = EnsembleRetriever(\n", + " retrievers=[bm25_retriever, chroma_retriever],\n", + " weights=[0.5, 0.5]\n", + ")\n", + "\n", + "print(\"Hybrid EnsembleRetriever initialized successfully.\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "b75dd440" + }, + "outputs": [], + "source": [ + "def normalize_date_filter_value(date_val, is_before):\n", + " \"\"\"\n", + " Normalizes a date filter value to YYYYMMDDHHMM format.\n", + " If date_val is YYYYMMDD, it becomes YYYYMMDD0000 (start of day) or YYYYMMDD2359 (end of day).\n", + " If date_val is YYYYMMDDHHMM, it's used as is.\n", + " \"\"\"\n", + " if date_val is None:\n", + " return None\n", + " date_str = str(date_val)\n", + " if len(date_str) == 8: # YYYYMMDD format\n", + " if is_before:\n", + " return int(date_str + \"2359\") # End of day for upper bound\n", + " else:\n", + " return int(date_str + \"0000\") # Beginning of day for lower bound\n", + " elif len(date_str) == 12: # YYYYMMDDHHMM format\n", + " return int(date_str)\n", + " return None # Invalid format\n", + "\n", + "def query_hybrid_rag(query, date_before=None, date_after=None, threshold=0.4):\n", + " \"\"\"\n", + " Queries the Hybrid Ensemble Retriever and calculates confidence.\n", + " \"\"\"\n", + " # Note: EnsembleRetriever typically returns documents without scores by default.\n", + " # To provide the confidence metric you requested, we use the base Chroma retriever\n", + " # for the score while using the ensemble for the document set.\n", + " search_kwargs = {\"k\": 3}\n", + " filters = []\n", + "\n", + " normalized_date_after = normalize_date_filter_value(date_after, is_before=False)\n", + " normalized_date_before = normalize_date_filter_value(date_before, is_before=True)\n", + "\n", + " if normalized_date_after:\n", + " filters.append({\"file_date\": {\"$gte\": normalized_date_after}})\n", + " if normalized_date_before:\n", + " filters.append({\"file_date\": {\"$lte\": normalized_date_before}})\n", + "\n", + " if filters:\n", + " if len(filters) == 1:\n", + " search_kwargs[\"filter\"] = filters[0]\n", + " else:\n", + " search_kwargs[\"filter\"] = {\"$and\": filters}\n", + "\n", + " # Get ensemble results\n", + " docs = ensemble_retriever.invoke(query)\n", + "\n", + " # Get scores specifically from Chroma for the confidence metric\n", + " results_with_scores = vectorstore.similarity_search_with_relevance_scores(query, **search_kwargs)\n", + "\n", + " avg_score = sum(score for doc, score in results_with_scores) / len(results_with_scores) if results_with_scores else 0.0\n", + " needs_web_search = avg_score < threshold\n", + "\n", + " print(f\"Hybrid Query: {query}\")\n", + " print(f\"Confidence Score: {avg_score:.4f}\")\n", + " print(f\"Action: {'TRIGGER WEB SEARCH' if needs_web_search else 'LOCAL DATA SUFFICIENT'}\")\n", + "\n", + " return docs, avg_score, needs_web_search\n", + "\n", + "# Example usage:\n", + "# docs, conf, search = query_hybrid_rag(\"inflation trends\", date_before=20260101, date_after=20250101)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "iOeipKrUM9Zf", + "outputId": "cb768336-771a-49bd-c966-ead9b3328941" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "92653fd7", - "outputId": "b433c217-2834-4a1d-f6ff-d73d72a2bbfe" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "--- Extracted Document Contents (2026-08-05T15:12:59) ---\n", - "Document 1 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", - "execute transactions in the System Open Market Account in accordance with the following \n", - "domestic policy directive: \n", - "\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:\n", - "\n", - "------------------------------\n", - "\n", - "Document 2 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", - "day. \n", - "o Roll over at auction the amount of principal payments from the Federal Reserve's \n", - "holdings of Treasury securities maturing in each calendar month that exceeds a \n", - "cap of $5 billion per month. Redeem Treasury coupon securities up to this\n", - "\n", - "------------------------------\n", - "\n", - "Document 3 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", - "announced by the Federal Open Market Committee in its statement on September 17, 2025: \n", - "• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \n", - "on reserve balances to 4.15 percent, effective September 18, 2025.\n", - "\n", - "------------------------------\n", - "\n" - ] - } - ], - "source": [ - "# Extract and print the content of each document\n", - "from datetime import datetime\n", - "\n", - "print(f\"\\n--- Extracted Document Contents ({datetime.now().isoformat(timespec='seconds')}) ---\")\n", - "for i, doc in enumerate(docs):\n", - " print(f\"Document {i+1} (Source: {doc.metadata.get('source_file')} | Date: {doc.metadata.get('file_date')}):\")\n", - " print(doc.page_content)\n", - " print(\"\\n\" + \"-\" * 30 + \"\\n\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", + "Confidence Score: 0.7586\n", + "Action: LOCAL DATA SUFFICIENT\n", + "[Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='execute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:'), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='announced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025.')]\n" + ] } - ], - "metadata": { + ], + "source": [ + "docs, conf, search = query_hybrid_rag(\"Increase the System Open Market Account holdings of Treasury securities by how much\", date_before=202601010000)\n", + "print(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { "colab": { - 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Redeem Treasury coupon securities up to this\n", + "\n", + "------------------------------\n", + "\n", + "Document 3 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", + "announced by the Federal Open Market Committee in its statement on September 17, 2025: \n", + "• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \n", + "on reserve balances to 4.15 percent, effective September 18, 2025.\n", + "\n", + "------------------------------\n", + "\n" + ] } + ], + "source": [ + "# Extract and print the content of each document\n", + "from datetime import datetime\n", + "\n", + "print(f\"\\n--- Extracted Document Contents ({datetime.now().isoformat(timespec='seconds')}) ---\")\n", + "for i, doc in enumerate(docs):\n", + " print(f\"Document {i+1} (Source: {doc.metadata.get('source_file')} | Date: {doc.metadata.get('file_date')}):\")\n", + " print(doc.page_content)\n", + " print(\"\\n\" + \"-\" * 30 + \"\\n\")" + ] + } + ], + "metadata": { + 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"version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file From 1fb3571199a973bde999a5f812ec38719819892b Mon Sep 17 00:00:00 2001 From: default Date: Tue, 8 Sep 2026 02:44:34 +0000 Subject: [PATCH 23/24] latest workable version --- .../BAA10Y_forecasting/00_RagPrototype.ipynb | 358 ++++++++++++++++-- 1 file changed, 331 insertions(+), 27 deletions(-) diff --git a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb index 400c6018..54ba1253 100644 --- a/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb +++ b/implementations/BAA10Y_forecasting/00_RagPrototype.ipynb @@ -199,7 +199,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -342,8 +342,276 @@ "id": "a296cc99", "outputId": "acaab241-0576-44dc-a078-9d5d3689a95b" }, - "outputs": [], - "source": "# 1. Force upgrade to modern library structures\n#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pycd ..pdf\n\nimport os\nimport re\nimport shutil # Import shutil for directory removal\nimport whisper\nimport chromadb # Explicitly import chromadb for client creation\nimport datetime # Import datetime for date parsing\nfrom collections import defaultdict\nfrom typing import List\nfrom langchain_core.documents import Document\nfrom langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\nfrom langchain_huggingface import HuggingFaceEmbeddings\nfrom langchain_community.vectorstores import Chroma\n\n# Updated path to /content/gdrive\npersist_directory = './chroma_db_big'\n\n# Remove the existing Chroma DB directory to clear old embeddings if dimensions mismatch\nif os.path.exists(persist_directory):\n print(f\"Removing existing Chroma DB at {persist_directory} due to potential database corruption or dimension mismatch.\")\n shutil.rmtree(persist_directory)\n\nos.makedirs(persist_directory, exist_ok=True)\n\ndef extract_date_from_filename(filename):\n match = re.search(r'(\\d{8})', filename)\n # If YYYYMMDD is found, append '0000' for HHMM, otherwise None\n return int(f\"{match.group(1)}0000\") if match else None\n\n# New function to extract date and time from document content\ndef extract_datetime_from_content(text):\n # Pattern 1: \"As adopted effective January 24, 2012\" -> YYYYMMDD0000\n match_adopted = re.search(r\"As adopted effective (January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text)\n if match_adopted:\n month_name, day, year = match_adopted.groups()\n month_num = datetime.datetime.strptime(month_name, '%B').month\n return int(f\"{year}{month_num:02d}{int(day):02d}0000\")\n\n # Pattern 2: \"For release at 2 p.m. EST January 27, 2021\" -> YYYYMMDDHHMM\n # Handles various timezones (EST, EDT, CST, CDT, MST, MDT, PST, PDT)\n match_release = re.search(r\"For release at (\\d{1,2})\\s*(a\\.m\\.|p\\.m\\.)\\s*(EST|EDT|CST|CDT|MST|MDT|PST|PDT)?\\s*(January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text, re.IGNORECASE)\n if match_release:\n hour_str, ampm, _, month_name, day, year = match_release.groups()\n hour = int(hour_str)\n if ampm.lower() == 'p.m.' and hour != 12:\n hour += 12\n elif ampm.lower() == 'a.m.' and hour == 12: # 12 a.m. is midnight\n hour = 0\n month_num = datetime.datetime.strptime(month_name, '%B').month\n # Minutes are not captured, assume 00\n return int(f\"{year}{month_num:02d}{int(day):02d}{hour:02d}00\")\n\n return None\n\ndef load_audio(file_path):\n model = whisper.load_model(\"base\")\n result = model.transcribe(file_path)\n return [Document(page_content=result[\"text\"], metadata={\"source\": file_path})]\n\n# ---------------------------------------------------------------------------\n# Semantic chunking: section headers -> whole paragraph/bullet blocks -> pack\n# ---------------------------------------------------------------------------\n# Replaces fixed-length RecursiveCharacterTextSplitter slicing. Guarantees:\n# 1. A chunk never spans two different named sections.\n# 2. A chunk is built from whole blocks (paragraphs, or a whole contiguous\n# bullet list) -- never split mid-sentence or mid-bullet-list.\n# 3. Blocks are packed greedily into paragraph GROUPS up to max_chars,\n# rather than emitting one chunk per paragraph.\n\nSECTION_HEADERS = [\n \"Financial Conditions\",\n \"Credit Markets\",\n \"Monetary Policy\",\n \"Liquidity and Market Functioning\",\n \"Risks to the Outlook\",\n]\n_HEADER_PATTERN = re.compile(r\"(?m)^(\" + \"|\".join(re.escape(h) for h in SECTION_HEADERS) + r\")\\s*$\")\n_BULLET_PREFIXES = (\"•\", \"- \", \"o \", \"* \")\n\n\ndef _split_into_blocks(text: str) -> List[str]:\n \"\"\"Paragraphs as atomic blocks; a run of consecutive bullet lines stays one block.\"\"\"\n raw_blocks = re.split(r\"\\n\\s*\\n\", text.strip())\n blocks, buffer = [], \"\"\n for raw in raw_blocks:\n raw = raw.strip()\n if not raw:\n continue\n is_bullet = raw.lstrip().startswith(_BULLET_PREFIXES)\n if is_bullet and buffer.lstrip().startswith(_BULLET_PREFIXES):\n buffer += \"\\n\" + raw # same bullet list continuing -- merge, don't split\n else:\n if buffer:\n blocks.append(buffer)\n buffer = raw\n if buffer:\n blocks.append(buffer)\n return blocks\n\n\ndef _split_sentence_safe(block: str, max_chars: int) -> List[str]:\n \"\"\"Fallback ONLY for a single block that alone exceeds max_chars. Naive regex --\n flag long blocks for manual review rather than trusting this on abbreviation-heavy\n Fed prose (e.g. 'U.S.'); nltk/spacy sentence tokenizers are sturdier if this matters.\"\"\"\n sentences = re.split(r\"(?<=[.!?])\\s+(?=[A-Z])\", block)\n chunks, current = [], \"\"\n for sent in sentences:\n if current and len(current) + len(sent) + 1 > max_chars:\n chunks.append(current)\n current = sent\n else:\n current = f\"{current} {sent}\".strip()\n if current:\n chunks.append(current)\n return chunks\n\n\ndef semantic_chunk_document(text: str, max_chars: int = 1200) -> List[dict]:\n \"\"\"Section-header split -> whole-block packing -> sentence-safe fallback for oversized blocks.\"\"\"\n matches = list(_HEADER_PATTERN.finditer(text))\n if not matches:\n sections = [(\"unlabeled\", text)] # e.g. FOMC statements have none of these headers\n else:\n sections = []\n if matches[0].start() > 0:\n sections.append((\"preamble\", text[: matches[0].start()]))\n for i, m in enumerate(matches):\n end = matches[i + 1].start() if i + 1 < len(matches) else len(text)\n sections.append((m.group(1), text[m.end():end]))\n\n results: List[dict] = []\n for section_name, section_text in sections:\n current = \"\"\n for block in _split_into_blocks(section_text):\n if len(block) > max_chars:\n if current:\n results.append({\"section\": section_name, \"text\": current})\n current = \"\"\n results.extend({\"section\": section_name, \"text\": p} for p in _split_sentence_safe(block, max_chars))\n continue\n if current and len(current) + len(block) + 2 > max_chars:\n results.append({\"section\": section_name, \"text\": current})\n current = block\n else:\n current = f\"{current}\\n\\n{block}\".strip()\n if current:\n results.append({\"section\": section_name, \"text\": current})\n return results\n\nraw_docs = []\ndata_dir = './unstructured_data' #need to adjust/customize the directory for the raw files location\nsupported_exts = ('.pdf', '.docx', '.pptx', '.mp3', '.wav')\nfiles = [f for f in os.listdir(data_dir) if f.lower().endswith(supported_exts)]\nprint(f\"Processing {len(files)} files...\")\n\nfor file in files:\n file_path = os.path.join(data_dir, file)\n filename_date_val = extract_date_from_filename(file) # Existing filename-based date as fallback\n\n try:\n file_docs = []\n extracted_content_date = None\n\n if file.lower().endswith('.pdf'):\n file_docs = PyPDFLoader(file_path).load()\n # Attempt to extract date from the first page's content for PDFs\n if file_docs and file_docs[0].page_content:\n extracted_content_date = extract_datetime_from_content(file_docs[0].page_content)\n elif file.lower().endswith('.docx'):\n file_docs = Docx2txtLoader(file_path).load()\n elif file.lower().endswith('.pptx'):\n file_docs = UnstructuredPowerPointLoader(file_path).load()\n elif file.lower().endswith(('.mp3', '.wav')):\n file_docs = load_audio(file_path)\n\n for d in file_docs:\n # Prioritize content-extracted date, fall back to filename date if not found\n if extracted_content_date is not None:\n d.metadata['file_date'] = extracted_content_date\n elif filename_date_val is not None:\n d.metadata['file_date'] = filename_date_val\n else:\n d.metadata['file_date'] = None # No date found from either source\n\n d.metadata['source_file'] = file\n raw_docs.extend(file_docs)\n except Exception as e:\n print(f\"Error processing {file}: {e}\")\n\nif raw_docs:\n # Semantic chunking (section headers -> paragraph/bullet groups -> sentence-safe\n # fallback) replaces fixed-length slicing. raw_docs is one Document per PDF PAGE\n # (PyPDFLoader) -- merge pages per source file first so a section spanning a page\n # break, or a bullet list split across pages, isn't cut there.\n pages_by_file = defaultdict(list)\n for d in raw_docs:\n pages_by_file[d.metadata[\"source_file\"]].append(d)\n\n splits: List[Document] = []\n for source_file, pages in pages_by_file.items():\n full_text = \"\\n\\n\".join(p.page_content for p in pages)\n file_meta = {\"file_date\": pages[0].metadata.get(\"file_date\"), \"source_file\": source_file}\n for chunk in semantic_chunk_document(full_text, max_chars=1200):\n splits.append(Document(page_content=chunk[\"text\"], metadata={**file_meta, \"section\": chunk[\"section\"]}))\n\n print(f\"Produced {len(splits)} semantic chunks across {len(pages_by_file)} documents.\")\n\n #embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n #20260808\n if EMBEDDING_MODEL_NAME in E5_MODEL_NAMES:\n embeddings = E5Embeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n else:\n embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n\n # Explicitly create the ChromaDB client\n try:\n client = chromadb.PersistentClient(path=persist_directory)\n print(f\"ChromaDB PersistentClient successfully created at {persist_directory}.\")\n except Exception as e:\n print(f\"Error creating ChromaDB PersistentClient: {e}\")\n print(\"This often indicates a deeper issue with ChromaDB's installation or interaction with the file system.\")\n raise # Re-raise the exception to stop execution if client cannot be initialized\n\n vectorstore = Chroma.from_documents(\n documents=splits,\n embedding=embeddings,\n client=client, # Pass the explicit client instance\n collection_name=\"my_rag_collection_e5_base\" # Explicitly name the collection for a fresh start\n )\n print(f\"Successfully indexed {len(splits)} chunks and saved to {persist_directory}.\")\nelse:\n print(\"No documents found to index.\")" + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_6928/1568176166.py:13: DeprecationWarning: `langchain-community` is being sunset and is no longer actively maintained. See https://github.com/langchain-ai/langchain-community/issues/674 for details and migration guidance toward standalone integration packages.\n", + " from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Removing existing Chroma DB at ./chroma_db_big due to potential database corruption or dimension mismatch.\n", + "Processing 2 files...\n", + "Produced 12 semantic chunks across 2 documents.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Loading weights: 100%|██████████| 199/199 [00:00<00:00, 3511.68it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "ChromaDB PersistentClient successfully created at ./chroma_db_big.\n", + "Successfully indexed 12 chunks and saved to ./chroma_db_big.\n" + ] + } + ], + "source": [ + "# 1. Force upgrade to modern library structures\n", + "#%pip install -q --upgrade langchain langchain-community langchain-huggingface langchain-text-splitters chromadb langchain-chroma openai-whisper unstructured python-pptx docx2txt pycd ..pdf\n", + "\n", + "import os\n", + "import re\n", + "import shutil # Import shutil for directory removal\n", + "import whisper\n", + "import chromadb # Explicitly import chromadb for client creation\n", + "import datetime # Import datetime for date parsing\n", + "from collections import defaultdict\n", + "from typing import List\n", + "from langchain_core.documents import Document\n", + "from langchain_community.document_loaders import PyPDFLoader, Docx2txtLoader, UnstructuredPowerPointLoader\n", + "from langchain_huggingface import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores import Chroma\n", + "\n", + "# Updated path to /content/gdrive\n", + "persist_directory = './chroma_db_big'\n", + "\n", + "# Remove the existing Chroma DB directory to clear old embeddings if dimensions mismatch\n", + "if os.path.exists(persist_directory):\n", + " print(f\"Removing existing Chroma DB at {persist_directory} due to potential database corruption or dimension mismatch.\")\n", + " shutil.rmtree(persist_directory)\n", + "\n", + "os.makedirs(persist_directory, exist_ok=True)\n", + "\n", + "def extract_date_from_filename(filename):\n", + " match = re.search(r'(\\d{8})', filename)\n", + " # If YYYYMMDD is found, append '0000' for HHMM, otherwise None\n", + " return int(f\"{match.group(1)}0000\") if match else None\n", + "\n", + "# New function to extract date and time from document content\n", + "def extract_datetime_from_content(text):\n", + " # Pattern 1: \"As adopted effective January 24, 2012\" -> YYYYMMDD0000\n", + " match_adopted = re.search(r\"As adopted effective (January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text)\n", + " if match_adopted:\n", + " month_name, day, year = match_adopted.groups()\n", + " month_num = datetime.datetime.strptime(month_name, '%B').month\n", + " return int(f\"{year}{month_num:02d}{int(day):02d}0000\")\n", + "\n", + " # Pattern 2: \"For release at 2 p.m. EST January 27, 2021\" -> YYYYMMDDHHMM\n", + " # Handles various timezones (EST, EDT, CST, CDT, MST, MDT, PST, PDT)\n", + " match_release = re.search(r\"For release at (\\d{1,2})\\s*(a\\.m\\.|p\\.m\\.)\\s*(EST|EDT|CST|CDT|MST|MDT|PST|PDT)?\\s*(January|February|March|April|May|June|July|August|September|October|November|December)\\s+(\\d{1,2}),\\s+(\\d{4})\", text, re.IGNORECASE)\n", + " if match_release:\n", + " hour_str, ampm, _, month_name, day, year = match_release.groups()\n", + " hour = int(hour_str)\n", + " if ampm.lower() == 'p.m.' and hour != 12:\n", + " hour += 12\n", + " elif ampm.lower() == 'a.m.' and hour == 12: # 12 a.m. is midnight\n", + " hour = 0\n", + " month_num = datetime.datetime.strptime(month_name, '%B').month\n", + " # Minutes are not captured, assume 00\n", + " return int(f\"{year}{month_num:02d}{int(day):02d}{hour:02d}00\")\n", + "\n", + " return None\n", + "\n", + "def load_audio(file_path):\n", + " model = whisper.load_model(\"base\")\n", + " result = model.transcribe(file_path)\n", + " return [Document(page_content=result[\"text\"], metadata={\"source\": file_path})]\n", + "\n", + "# ---------------------------------------------------------------------------\n", + "# Semantic chunking: section headers -> whole paragraph/bullet blocks -> pack\n", + "# ---------------------------------------------------------------------------\n", + "# Replaces fixed-length RecursiveCharacterTextSplitter slicing. Guarantees:\n", + "# 1. A chunk never spans two different named sections.\n", + "# 2. A chunk is built from whole blocks (paragraphs, or a whole contiguous\n", + "# bullet list) -- never split mid-sentence or mid-bullet-list.\n", + "# 3. Blocks are packed greedily into paragraph GROUPS up to max_chars,\n", + "# rather than emitting one chunk per paragraph.\n", + "\n", + "SECTION_HEADERS = [\n", + " \"Financial Conditions\",\n", + " \"Credit Markets\",\n", + " \"Monetary Policy\",\n", + " \"Liquidity and Market Functioning\",\n", + " \"Risks to the Outlook\",\n", + "]\n", + "_HEADER_PATTERN = re.compile(r\"(?m)^(\" + \"|\".join(re.escape(h) for h in SECTION_HEADERS) + r\")\\s*$\")\n", + "_BULLET_PREFIXES = (\"•\", \"- \", \"o \", \"* \")\n", + "\n", + "\n", + "def _split_into_blocks(text: str) -> List[str]:\n", + " \"\"\"Paragraphs as atomic blocks; a run of consecutive bullet lines stays one block.\"\"\"\n", + " raw_blocks = re.split(r\"\\n\\s*\\n\", text.strip())\n", + " blocks, buffer = [], \"\"\n", + " for raw in raw_blocks:\n", + " raw = raw.strip()\n", + " if not raw:\n", + " continue\n", + " is_bullet = raw.lstrip().startswith(_BULLET_PREFIXES)\n", + " if is_bullet and buffer.lstrip().startswith(_BULLET_PREFIXES):\n", + " buffer += \"\\n\" + raw # same bullet list continuing -- merge, don't split\n", + " else:\n", + " if buffer:\n", + " blocks.append(buffer)\n", + " buffer = raw\n", + " if buffer:\n", + " blocks.append(buffer)\n", + " return blocks\n", + "\n", + "\n", + "def _split_sentence_safe(block: str, max_chars: int) -> List[str]:\n", + " \"\"\"Fallback ONLY for a single block that alone exceeds max_chars. Naive regex --\n", + " flag long blocks for manual review rather than trusting this on abbreviation-heavy\n", + " Fed prose (e.g. 'U.S.'); nltk/spacy sentence tokenizers are sturdier if this matters.\"\"\"\n", + " sentences = re.split(r\"(?<=[.!?])\\s+(?=[A-Z])\", block)\n", + " chunks, current = [], \"\"\n", + " for sent in sentences:\n", + " if current and len(current) + len(sent) + 1 > max_chars:\n", + " chunks.append(current)\n", + " current = sent\n", + " else:\n", + " current = f\"{current} {sent}\".strip()\n", + " if current:\n", + " chunks.append(current)\n", + " return chunks\n", + "\n", + "\n", + "def semantic_chunk_document(text: str, max_chars: int = 1200) -> List[dict]:\n", + " \"\"\"Section-header split -> whole-block packing -> sentence-safe fallback for oversized blocks.\"\"\"\n", + " matches = list(_HEADER_PATTERN.finditer(text))\n", + " if not matches:\n", + " sections = [(\"unlabeled\", text)] # e.g. FOMC statements have none of these headers\n", + " else:\n", + " sections = []\n", + " if matches[0].start() > 0:\n", + " sections.append((\"preamble\", text[: matches[0].start()]))\n", + " for i, m in enumerate(matches):\n", + " end = matches[i + 1].start() if i + 1 < len(matches) else len(text)\n", + " sections.append((m.group(1), text[m.end():end]))\n", + "\n", + " results: List[dict] = []\n", + " for section_name, section_text in sections:\n", + " current = \"\"\n", + " for block in _split_into_blocks(section_text):\n", + " if len(block) > max_chars:\n", + " if current:\n", + " results.append({\"section\": section_name, \"text\": current})\n", + " current = \"\"\n", + " results.extend({\"section\": section_name, \"text\": p} for p in _split_sentence_safe(block, max_chars))\n", + " continue\n", + " if current and len(current) + len(block) + 2 > max_chars:\n", + " results.append({\"section\": section_name, \"text\": current})\n", + " current = block\n", + " else:\n", + " current = f\"{current}\\n\\n{block}\".strip()\n", + " if current:\n", + " results.append({\"section\": section_name, \"text\": current})\n", + " return results\n", + "\n", + "raw_docs = []\n", + "data_dir = './unstructured_data' #need to adjust/customize the directory for the raw files location\n", + "supported_exts = ('.pdf', '.docx', '.pptx', '.mp3', '.wav')\n", + "files = [f for f in os.listdir(data_dir) if f.lower().endswith(supported_exts)]\n", + "print(f\"Processing {len(files)} files...\")\n", + "\n", + "for file in files:\n", + " file_path = os.path.join(data_dir, file)\n", + " filename_date_val = extract_date_from_filename(file) # Existing filename-based date as fallback\n", + "\n", + " try:\n", + " file_docs = []\n", + " extracted_content_date = None\n", + "\n", + " if file.lower().endswith('.pdf'):\n", + " file_docs = PyPDFLoader(file_path).load()\n", + " # Attempt to extract date from the first page's content for PDFs\n", + " if file_docs and file_docs[0].page_content:\n", + " extracted_content_date = extract_datetime_from_content(file_docs[0].page_content)\n", + " elif file.lower().endswith('.docx'):\n", + " file_docs = Docx2txtLoader(file_path).load()\n", + " elif file.lower().endswith('.pptx'):\n", + " file_docs = UnstructuredPowerPointLoader(file_path).load()\n", + " elif file.lower().endswith(('.mp3', '.wav')):\n", + " file_docs = load_audio(file_path)\n", + "\n", + " for d in file_docs:\n", + " # Prioritize content-extracted date, fall back to filename date if not found\n", + " if extracted_content_date is not None:\n", + " d.metadata['file_date'] = extracted_content_date\n", + " elif filename_date_val is not None:\n", + " d.metadata['file_date'] = filename_date_val\n", + " else:\n", + " d.metadata['file_date'] = None # No date found from either source\n", + "\n", + " d.metadata['source_file'] = file\n", + " raw_docs.extend(file_docs)\n", + " except Exception as e:\n", + " print(f\"Error processing {file}: {e}\")\n", + "\n", + "if raw_docs:\n", + " # Semantic chunking (section headers -> paragraph/bullet groups -> sentence-safe\n", + " # fallback) replaces fixed-length slicing. raw_docs is one Document per PDF PAGE\n", + " # (PyPDFLoader) -- merge pages per source file first so a section spanning a page\n", + " # break, or a bullet list split across pages, isn't cut there.\n", + " pages_by_file = defaultdict(list)\n", + " for d in raw_docs:\n", + " pages_by_file[d.metadata[\"source_file\"]].append(d)\n", + "\n", + " splits: List[Document] = []\n", + " for source_file, pages in pages_by_file.items():\n", + " full_text = \"\\n\\n\".join(p.page_content for p in pages)\n", + " file_meta = {\"file_date\": pages[0].metadata.get(\"file_date\"), \"source_file\": source_file}\n", + " for chunk in semantic_chunk_document(full_text, max_chars=1200):\n", + " splits.append(Document(page_content=chunk[\"text\"], metadata={**file_meta, \"section\": chunk[\"section\"]}))\n", + "\n", + " print(f\"Produced {len(splits)} semantic chunks across {len(pages_by_file)} documents.\")\n", + "\n", + " #embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS) # Using configured model\n", + " #20260808\n", + " if EMBEDDING_MODEL_NAME in E5_MODEL_NAMES:\n", + " embeddings = E5Embeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", + " else:\n", + " embeddings = HuggingFaceEmbeddings(model_name=EMBEDDING_MODEL_NAME, model_kwargs=MODEL_KWARGS)\n", + "\n", + " # Explicitly create the ChromaDB client\n", + " try:\n", + " client = chromadb.PersistentClient(path=persist_directory)\n", + " print(f\"ChromaDB PersistentClient successfully created at {persist_directory}.\")\n", + " except Exception as e:\n", + " print(f\"Error creating ChromaDB PersistentClient: {e}\")\n", + " print(\"This often indicates a deeper issue with ChromaDB's installation or interaction with the file system.\")\n", + " raise # Re-raise the exception to stop execution if client cannot be initialized\n", + "\n", + " vectorstore = Chroma.from_documents(\n", + " documents=splits,\n", + " embedding=embeddings,\n", + " client=client, # Pass the explicit client instance\n", + " collection_name=\"my_rag_collection_e5_base\" # Explicitly name the collection for a fresh start\n", + " )\n", + " print(f\"Successfully indexed {len(splits)} chunks and saved to {persist_directory}.\")\n", + "else:\n", + " print(\"No documents found to index.\")" + ] }, { "cell_type": "code", @@ -460,13 +728,13 @@ "text": [ "BM25 Document DB initialized.\n", "[DEBUG] Query: 'holdings of Treasury securities'\n", - "[DEBUG] Search arguments for ChromaDB: {'k': 3}\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", - "Confidence: 0.7548 | Needs Web Search: False\n", + "[DEBUG] Search arguments for ChromaDB: {'k': 3, 'filter': {'file_date': {'$lte': 202509180001}}}\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'source_file': 'monetary20250917a1.pdf', 'file_date': 202509170000, 'section': 'unlabeled'}, page_content='Redeem Treasury coupon securities up to this \\nmonthly cap and Treasury bills to the extent that coupon principal payments are \\nless than the monthly cap. \\no Reinvest the amount of principal payments from the Federal Reserve\\'s holdings of \\nagency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding. \\no Allow modest deviations from stated amounts for reinvestments, if needed for \\noperational reasons.\"'), 0.7610264417125636), (Document(metadata={'section': 'unlabeled', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='Recent indicators suggest that growth of economic activity moderated in the first half of \\nthe year. Job gains have slowed, and the unemployment rate has edged up but remains low. Inflation has moved up and remains somewhat elevated. The Committee seeks to achieve maximum employment and inflation at the rate of \\n2 percent over the longer run. Uncertainty about the economic outlook remains elevated. The \\nCommittee is attentive to the risks to both sides of its dual mandate and judges that downside \\nrisks to employment have risen. In support of its goals and in light of the shift in the balance of risks, the Committee \\ndecided to lower the target range for the federal funds rate by 1/4 percentage point to 4 to \\n4-1/4 percent. In considering additional adjustments to the target range for the federal funds rate, \\nthe Committee will carefully assess incoming data, the evolving outlook, and the balance of \\nrisks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting \\nmaximum employment and returning inflation to its 2 percent objective.'), 0.6811431805797739), (Document(metadata={'source_file': 'monetary20250917a1.pdf', 'file_date': 202509170000, 'section': 'unlabeled'}, page_content='Decisions Regarding Monetary Policy Implementation \\nThe Federal Reserve has made the following decisions to implement the monetary policy stance \\nannounced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025. \\n• As part of its policy decision, the Federal Open Market Committee voted to direct the Open \\nMarket Desk at the Federal Reserve Bank of New York, until instructed otherwise, to \\nexecute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to: \\no Undertake open market operations as necessary to maintain the federal funds rate \\nin a target range of 4 to 4-1/4 percent. \\no Conduct standing overnight repurchase agreement operations with a minimum bid \\nrate of 4.25 percent and with an aggregate operation limit of $500 billion. \\no Conduct standing overnight reverse repurchase agreement operations at an \\noffering rate of 4 percent and with a per-counterparty limit of $160 billion per \\nday. \\no Roll over at auction the amount of principal payments from the Federal Reserve\\'s \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month.'), 0.6768074289063368)]\n", + "Confidence: 0.7063 | Needs Web Search: False\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", "- [Date: 202509170000] monetary20250917a1.pdf\n", - "[DEBUG] Raw results from vectorstore: [(Document(metadata={'author': 'Federal Reserve', 'total_pages': 4, 'page_label': '3', 'file_date': 202509170000, 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creationdate': '2025-09-17T11:48:45-04:00', 'creator': 'Acrobat PDFMaker 25 for Word', 'producer': 'Adobe PDF Library 25.1.97', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 2, 'title': 'Federal Reserve issues FOMC statement'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), 0.7632134838286424), (Document(metadata={'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'title': 'Federal Reserve issues FOMC statement', 'author': 'Federal Reserve', 'page_label': '3', 'page': 2, 'file_date': 202509170000, 'producer': 'Adobe PDF Library 25.1.97', 'moddate': '2025-09-17T11:53:16-04:00', 'source_file': 'monetary20250917a1.pdf', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00'}, page_content='agency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding.'), 0.7551729398774009), (Document(metadata={'creationdate': '2025-09-17T11:48:45-04:00', 'source': './unstructured_data/monetary20250917a1.pdf', 'page': 0, 'page_label': '1', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'producer': 'Adobe PDF Library 25.1.97', 'file_date': 202509170000, 'author': 'Federal Reserve', 'creator': 'Acrobat PDFMaker 25 for Word', 'total_pages': 4, 'source_file': 'monetary20250917a1.pdf'}, page_content='risks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting'), 0.7460156730280147)]\n", + "[DEBUG] Raw results from vectorstore: [(Document(metadata={'source_file': 'monetary20250917a1.pdf', 'file_date': 202509170000, 'section': 'unlabeled'}, page_content='Redeem Treasury coupon securities up to this \\nmonthly cap and Treasury bills to the extent that coupon principal payments are \\nless than the monthly cap. \\no Reinvest the amount of principal payments from the Federal Reserve\\'s holdings of \\nagency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding. \\no Allow modest deviations from stated amounts for reinvestments, if needed for \\noperational reasons.\"'), 0.7610264417125636), (Document(metadata={'section': 'unlabeled', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='Recent indicators suggest that growth of economic activity moderated in the first half of \\nthe year. Job gains have slowed, and the unemployment rate has edged up but remains low. Inflation has moved up and remains somewhat elevated. The Committee seeks to achieve maximum employment and inflation at the rate of \\n2 percent over the longer run. Uncertainty about the economic outlook remains elevated. The \\nCommittee is attentive to the risks to both sides of its dual mandate and judges that downside \\nrisks to employment have risen. In support of its goals and in light of the shift in the balance of risks, the Committee \\ndecided to lower the target range for the federal funds rate by 1/4 percentage point to 4 to \\n4-1/4 percent. In considering additional adjustments to the target range for the federal funds rate, \\nthe Committee will carefully assess incoming data, the evolving outlook, and the balance of \\nrisks. The Committee will continue reducing its holdings of Treasury securities and agency debt \\nand agency mortgage‑backed securities. The Committee is strongly committed to supporting \\nmaximum employment and returning inflation to its 2 percent objective.'), 0.6811431805797739), (Document(metadata={'source_file': 'monetary20250917a1.pdf', 'file_date': 202509170000, 'section': 'unlabeled'}, page_content='Decisions Regarding Monetary Policy Implementation \\nThe Federal Reserve has made the following decisions to implement the monetary policy stance \\nannounced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025. \\n• As part of its policy decision, the Federal Open Market Committee voted to direct the Open \\nMarket Desk at the Federal Reserve Bank of New York, until instructed otherwise, to \\nexecute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to: \\no Undertake open market operations as necessary to maintain the federal funds rate \\nin a target range of 4 to 4-1/4 percent. \\no Conduct standing overnight repurchase agreement operations with a minimum bid \\nrate of 4.25 percent and with an aggregate operation limit of $500 billion. \\no Conduct standing overnight reverse repurchase agreement operations at an \\noffering rate of 4 percent and with a per-counterparty limit of $160 billion per \\nday. \\no Roll over at auction the amount of principal payments from the Federal Reserve\\'s \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month.'), 0.6768074289063368)]\n", "\n", "[+] Local confidence is sufficient.\n" ] @@ -482,10 +750,10 @@ "print(\"BM25 Document DB initialized.\")\n", "\n", "# Example usage of the date-filtered search:\n", - "# This will find documents strictly before March 12, 2026\n", + "# This will find documents strictly before \n", "query_text = \"holdings of Treasury securities\"\n", "#\"target range for the federal funds rate\"\n", - "target_date = None\n", + "target_date = 202509180001\n", "\n", "results, confidence, needs_search = query_rag_with_date_filter(query_text, date_before=target_date, date_after=None)\n", "\n", @@ -511,7 +779,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Successfully connected to Chroma. Found 50 chunks.\n", + "Successfully connected to Chroma. Found 12 chunks.\n", "Hybrid Retrieval (BM25 + Chroma) is now fully ACTIVE.\n" ] } @@ -519,7 +787,7 @@ "source": [ "# @title\n", "#%pip install -q -U langchain-chroma langchain-community langchain-core rank_bm25 langchain-huggingface\n", - "\n", + "#build both VectorDB search and document search with BM25.\n", "import os\n", "from langchain_chroma import Chroma\n", "from langchain_huggingface import HuggingFaceEmbeddings\n", @@ -572,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "cellView": "form", "colab": { @@ -626,7 +894,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": { "id": "b75dd440" }, @@ -695,7 +963,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -709,9 +977,9 @@ "output_type": "stream", "text": [ "Hybrid Query: Increase the System Open Market Account holdings of Treasury securities by how much\n", - "Confidence Score: 0.7586\n", + "Confidence Score: 0.7628\n", "Action: LOCAL DATA SUFFICIENT\n", - "[Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='execute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:'), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content=\"day. \\no Roll over at auction the amount of principal payments from the Federal Reserve's \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month. Redeem Treasury coupon securities up to this\"), Document(metadata={'producer': 'Adobe PDF Library 25.1.97', 'creator': 'Acrobat PDFMaker 25 for Word', 'creationdate': '2025-09-17T11:48:45-04:00', 'author': 'Federal Reserve', 'moddate': '2025-09-17T11:53:16-04:00', 'title': 'Federal Reserve issues FOMC statement', 'source': './unstructured_data/monetary20250917a1.pdf', 'total_pages': 4, 'page': 2, 'page_label': '3', 'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf'}, page_content='announced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025.')]\n" + "[Document(metadata={'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf', 'section': 'unlabeled'}, page_content='Decisions Regarding Monetary Policy Implementation \\nThe Federal Reserve has made the following decisions to implement the monetary policy stance \\nannounced by the Federal Open Market Committee in its statement on September 17, 2025: \\n• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \\non reserve balances to 4.15 percent, effective September 18, 2025. \\n• As part of its policy decision, the Federal Open Market Committee voted to direct the Open \\nMarket Desk at the Federal Reserve Bank of New York, until instructed otherwise, to \\nexecute transactions in the System Open Market Account in accordance with the following \\ndomestic policy directive: \\n\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to: \\no Undertake open market operations as necessary to maintain the federal funds rate \\nin a target range of 4 to 4-1/4 percent. \\no Conduct standing overnight repurchase agreement operations with a minimum bid \\nrate of 4.25 percent and with an aggregate operation limit of $500 billion. \\no Conduct standing overnight reverse repurchase agreement operations at an \\noffering rate of 4 percent and with a per-counterparty limit of $160 billion per \\nday. \\no Roll over at auction the amount of principal payments from the Federal Reserve\\'s \\nholdings of Treasury securities maturing in each calendar month that exceeds a \\ncap of $5 billion per month.'), Document(metadata={'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf', 'section': 'unlabeled'}, page_content='Redeem Treasury coupon securities up to this \\nmonthly cap and Treasury bills to the extent that coupon principal payments are \\nless than the monthly cap. \\no Reinvest the amount of principal payments from the Federal Reserve\\'s holdings of \\nagency debt and agency mortgage-backed securities (MBS) received in each \\ncalendar month that exceeds a cap of $35 billion per month into Treasury \\nsecurities to roughly match the maturity composition of Treasury securities \\noutstanding. \\no Allow modest deviations from stated amounts for reinvestments, if needed for \\noperational reasons.\"'), Document(metadata={'file_date': 202509170000, 'source_file': 'monetary20250917a1.pdf', 'section': 'unlabeled'}, page_content=\"• In a related action, the Board of Governors of the Federal Reserve System voted to \\napprove a 1/4 percentage point decrease in the primary credit rate to 4.25 percent, \\neffective September 18, 2025. In taking this action, the Board approved requests to \\nestablish that rate submitted by the Boards of Directors of the Federal Reserve Banks of \\nBoston, New York, Philadelphia, Richmond, Atlanta, Chicago, Minneapolis, Kansas \\nCity, Dallas, and San Francisco.\\n\\n(more)\\n\\n-2-\\n\\nThis information will be updated as appropriate to reflect decisions of the Federal Open Market \\nCommittee or the Board of Governors regarding details of the Federal Reserve's operational tools \\nand approach used to implement monetary policy. \\nMore information regarding open market operations and reinvestments may be found on the \\nFederal Reserve Bank of New York's website.\")]\n" ] } ], @@ -722,7 +990,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -736,26 +1004,62 @@ "output_type": "stream", "text": [ "\n", - "--- Extracted Document Contents (2026-08-05T15:12:59) ---\n", + "--- Extracted Document Contents (2026-09-08T02:40:08) ---\n", "Document 1 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", + "Decisions Regarding Monetary Policy Implementation \n", + "The Federal Reserve has made the following decisions to implement the monetary policy stance \n", + "announced by the Federal Open Market Committee in its statement on September 17, 2025: \n", + "• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \n", + "on reserve balances to 4.15 percent, effective September 18, 2025. \n", + "• As part of its policy decision, the Federal Open Market Committee voted to direct the Open \n", + "Market Desk at the Federal Reserve Bank of New York, until instructed otherwise, to \n", "execute transactions in the System Open Market Account in accordance with the following \n", "domestic policy directive: \n", - "\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to:\n", + "\"Effective September 18, 2025, the Federal Open Market Committee directs the Desk to: \n", + "o Undertake open market operations as necessary to maintain the federal funds rate \n", + "in a target range of 4 to 4-1/4 percent. \n", + "o Conduct standing overnight repurchase agreement operations with a minimum bid \n", + "rate of 4.25 percent and with an aggregate operation limit of $500 billion. \n", + "o Conduct standing overnight reverse repurchase agreement operations at an \n", + "offering rate of 4 percent and with a per-counterparty limit of $160 billion per \n", + "day. \n", + "o Roll over at auction the amount of principal payments from the Federal Reserve's \n", + "holdings of Treasury securities maturing in each calendar month that exceeds a \n", + "cap of $5 billion per month.\n", "\n", "------------------------------\n", "\n", "Document 2 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", - "day. \n", - "o Roll over at auction the amount of principal payments from the Federal Reserve's \n", - "holdings of Treasury securities maturing in each calendar month that exceeds a \n", - "cap of $5 billion per month. Redeem Treasury coupon securities up to this\n", + "Redeem Treasury coupon securities up to this \n", + "monthly cap and Treasury bills to the extent that coupon principal payments are \n", + "less than the monthly cap. \n", + "o Reinvest the amount of principal payments from the Federal Reserve's holdings of \n", + "agency debt and agency mortgage-backed securities (MBS) received in each \n", + "calendar month that exceeds a cap of $35 billion per month into Treasury \n", + "securities to roughly match the maturity composition of Treasury securities \n", + "outstanding. \n", + "o Allow modest deviations from stated amounts for reinvestments, if needed for \n", + "operational reasons.\"\n", "\n", "------------------------------\n", "\n", "Document 3 (Source: monetary20250917a1.pdf | Date: 202509170000):\n", - "announced by the Federal Open Market Committee in its statement on September 17, 2025: \n", - "• The Board of Governors of the Federal Reserve System voted to lower the interest rate paid \n", - "on reserve balances to 4.15 percent, effective September 18, 2025.\n", + "• In a related action, the Board of Governors of the Federal Reserve System voted to \n", + "approve a 1/4 percentage point decrease in the primary credit rate to 4.25 percent, \n", + "effective September 18, 2025. In taking this action, the Board approved requests to \n", + "establish that rate submitted by the Boards of Directors of the Federal Reserve Banks of \n", + "Boston, New York, Philadelphia, Richmond, Atlanta, Chicago, Minneapolis, Kansas \n", + "City, Dallas, and San Francisco.\n", + "\n", + "(more)\n", + "\n", + "-2-\n", + "\n", + "This information will be updated as appropriate to reflect decisions of the Federal Open Market \n", + "Committee or the Board of Governors regarding details of the Federal Reserve's operational tools \n", + "and approach used to implement monetary policy. \n", + "More information regarding open market operations and reinvestments may be found on the \n", + "Federal Reserve Bank of New York's website.\n", "\n", "------------------------------\n", "\n" @@ -9356,4 +9660,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} \ No newline at end of file +} From f20cc83acef7c82dd0e5338ced4cc6c7dda5ef60 Mon Sep 17 00:00:00 2001 From: hongxu69 Date: Mon, 14 Sep 2026 22:34:37 -0400 Subject: [PATCH 24/24] add files to use RAG system to deal with structured and unstructured data via PDF files extraction. The newly produced time series can assist calm/stress regime labelling --- .../RAGtools/chunking_design.md | 34 + .../RAGtools/config_design.md | 44 ++ .../CreditStressNarrativeScore.md | 91 +++ .../RAGtools/feature_docs/FRBNYLoanCPFF.md | 81 +++ .../RAGtools/feature_docs/FacilityStress.md | 152 +++++ .../InterventionNarrativeScore.md | 100 +++ .../feature_docs/LiquidityNarrativeScore.md | 58 ++ .../RAGtools/feature_docs/QTIntensity.md | 102 +++ .../feature_docs/ReserveBalancesLevel.md | 50 ++ .../RAGtools/running_the_pipeline.md | 147 +++++ .../BAA10Y_forecasting/__init__.py | 26 +- implementations/BAA10Y_forecasting/data.py | 158 ++++- .../BAA10Y_forecasting/pyproject.toml | 6 +- .../src/baa10y_forecasting/__init__.py | 2 + .../src/baa10y_forecasting/rag/__init__.py | 5 + .../rag/build_narrative_series.py | 143 ++++ .../rag/build_structured_series.py | 122 ++++ .../src/baa10y_forecasting/rag/lexicons.py | 78 +++ .../rag/narrative_extraction.py | 87 +++ .../rag/narrative_scoring.py | 78 +++ .../src/baa10y_forecasting/rag/paths.py | 28 + .../rag/structured_extraction.py | 292 +++++++++ implementations/BAA10Y_forecasting/uv.lock | 616 +++++++++++++++++- 23 files changed, 2492 insertions(+), 8 deletions(-) create mode 100644 implementations/BAA10Y_forecasting/RAGtools/chunking_design.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/config_design.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/CreditStressNarrativeScore.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/FRBNYLoanCPFF.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/FacilityStress.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/InterventionNarrativeScore.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/LiquidityNarrativeScore.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/QTIntensity.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/feature_docs/ReserveBalancesLevel.md create mode 100644 implementations/BAA10Y_forecasting/RAGtools/running_the_pipeline.md create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/__init__.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/__init__.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_narrative_series.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_structured_series.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/lexicons.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_extraction.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_scoring.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/paths.py create mode 100644 implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/structured_extraction.py diff --git a/implementations/BAA10Y_forecasting/RAGtools/chunking_design.md b/implementations/BAA10Y_forecasting/RAGtools/chunking_design.md new file mode 100644 index 00000000..822ac077 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/chunking_design.md @@ -0,0 +1,34 @@ +# Chunking design notes (from reviewing the `chunking.py` prototype) + +**Status:** `chunking.py` was a prototype, not code to build on — kept here only as the design +takeaway before the file itself is deleted. + +## The idea worth keeping + +Classify every extracted block *first* (`table` / `footnote` / `header` / `page_number` / +`narrative`), then only run token-count chunking *within* the narrative category. This directly +solves the "don't break a chunk across a table/figure boundary" concern: a table or footnote can +never get merged into an adjacent narrative chunk, because chunking only ever operates on blocks +already classified as narrative. This is the right shape and should carry over into the real +package's chunking module. + +## What needs to change from the prototype + +1. **Table detection should use pdfplumber's native `extract_tables()`/table-finder, not a + hand-rolled `is_table_candidate`.** The prototype stubs this out entirely + (`extract_blocks_with_layout`, `is_table_candidate`, `estimate_body_font_size`, etc. are all + marked "your own function"). H.4.1's tables are fairly regular numeric grids — pdfplumber's + built-in detector should handle them well, but needs testing against real filings across eras, + since we've confirmed the table layout itself changes shape multiple times over 2000–2025 (see + `FacilityStress.md`'s era findings). +2. **Reuse the existing oversized-block fallback from `00_RagPrototype.ipynb` + (`_split_sentence_safe`), don't reimplement it.** The prototype's `chunk_narrative_blocks` only + enforces a `max_tokens` ceiling and has no fallback for a single block that alone exceeds it. + `00_RagPrototype.ipynb`'s `semantic_chunk_document` already solved this (sentence-safe splitting + as a fallback, flagged as a naive regex approach worth manual review on abbreviation-heavy Fed + prose) — that logic should be reused as-is, not redesigned. +3. Table/footnote/header/page-number chunks were appended to the output in fixed type-order per + page (all tables, then all footnotes, then headers, then page numbers, then narrative) rather + than original document reading-order. Doesn't matter for retrieval (every chunk carries its own + page metadata), but worth being a deliberate choice in the real implementation, not an accident + of iteration order. diff --git a/implementations/BAA10Y_forecasting/RAGtools/config_design.md b/implementations/BAA10Y_forecasting/RAGtools/config_design.md new file mode 100644 index 00000000..572cf765 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/config_design.md @@ -0,0 +1,44 @@ +# Why the RAG configuration is split into three files, not one + +**Status:** Decision made — rationale for later reference. + +## The three kinds of "configuration" in this system + +1. **Tunable parameters** — `top_k` (vector search and BM25 separately), the combination weights + (`w1`/`w2`/`w3` for the narrative-score pipeline), the confidence/retry threshold, `max_attempts` + for the agentic retry loop. A human sets these by judgment and may reasonably change them. +2. **Computed calibration statistics** — the min/max normalization bounds per component + (`keyword_freq`, `bm25_score`, `embedding_sim`), frozen from a one-time pass across the corpus + (see `CreditStressNarrativeScore.md`). Nobody hand-picks these; they're *derived* from the data + and need periodic regeneration as `h41_pdfs/` grows every week. +3. **Domain rules learned from reading real filings** — e.g. `FacilityStress`'s exclusion set (the + stable routine/structural line items to exclude) documented in `FacilityStress.md`. This isn't + tuned or computed either — it's a fact about how the H.4.1 table is structured, arrived at by + actually reading PDFs across eras, with its own justification trail. + +## Why not put all three in one file + +- **Blast radius of a mistake.** A human editing `top_k` for an experiment shouldn't be able to + accidentally overwrite a frozen calibration statistic in the same edit, and a calibration + regeneration script shouldn't need write access to hand-tuned settings at all. Separate files + give each writer (person vs. script) its own narrow scope. +- **Git history stays legible.** A diff to the tunable-params file means "someone changed a + setting." A diff to the calibration file means "the corpus grew and stats were refreshed." A diff + to a domain-rule file means "we learned something new about the H.4.1 table structure and updated + the rule." Collapsing these into one file makes every diff ambiguous about which of the three + actually happened — directly working against the audit/reproducibility goal driving this whole + design. +- **Different regeneration cadences.** Tunable params change when a person decides to change them + (rare, deliberate). Calibration stats should be regenerated on a defined cadence tied to corpus + growth (e.g., whenever a new H.4.1 filing is ingested, or on a periodic schedule). Domain rules + change only when new PDF evidence contradicts the current rule (as happened when reading the + 2010/2020/2023 filings revealed `FacilityStress` needed an exclusion-based rule instead of an + inclusion list). Three different triggers argue for three different files. + +## Resulting split + +| File (indicative name) | Contents | Who/what writes it | +|---|---|---| +| `rag_config.yaml` | `top_k` (vector, BM25), combination weights, confidence threshold, `max_attempts` | Human, by hand | +| Calibration snapshot (e.g. `calibration_stats.json`) | Frozen min/max per normalized component, with the corpus snapshot version/date it was computed from | Regeneration script, not hand-edited | +| Per-feature docs (`feature_docs/*.md`) + any derived rule files | Domain rules like `FacilityStress`'s exclusion set, with the PDF-reading evidence behind them | Human, informed by reading real filings | diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/CreditStressNarrativeScore.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/CreditStressNarrativeScore.md new file mode 100644 index 00000000..49fe7949 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/CreditStressNarrativeScore.md @@ -0,0 +1,91 @@ +# CreditStressNarrativeScore + +**Status:** Final — pipeline and combination method settled. +**Feature type:** unstructured_text. +**Source:** H.4.1 narrative/footnote text (RAG-retrieved chunks, not tables). + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +This captures the Fed's own real-time qualitative characterization of credit-market conditions — +footnote and narrative language explaining why a facility exists or what conditions it addresses. +Two properties make it a plausible complement to the purely numeric features: (1) it is +**continuous, not facility-gated** — it can register elevated (if modest) stress language during +periods when conditions are deteriorating but haven't yet escalated to warrant a new emergency +facility, giving coverage `FacilityStress` structurally cannot; (2) it may carry information the +raw balance-sheet numbers don't, since it reflects Fed staff's own contemporaneous framing rather +than just a count of dollars drawn. Honest caveat: during weeks when a facility *is* active, this +score will likely correlate significantly with `FacilityStress` (they're often describing the same +events) — its main expected incremental value is in the non-crisis periods where the numeric +facility features are structurally silent. + +## Pipeline + +Per chunk of narrative/footnote text: + +1. `keyword_freq` = (# credit-stress keyword hits in chunk) / (total tokens in chunk), using the + lexicon from the original reference doc ("credit tightening", "credit availability", "funding + pressures", "credit deterioration", "credit strains", "credit markets", "credit support", + "credit disruptions", "credit facilities", "credit risk", "credit stress", "credit + contraction"). +2. `bm25_score` = BM25 relevance of the chunk against a fixed query ("credit stress, tightening + credit conditions, funding pressures"). +3. `embedding_sim` = cosine similarity of the chunk's E5 embedding against the query embedding + ("credit stress in financial markets"). + +## Normalization (why, and how) + +`keyword_freq` (~0.01), `bm25_score` (~7, unbounded), and `embedding_sim` (cosine similarity, +mathematically bounded [-1,1], though same-domain financial text against E5 tends to land solidly +positive in practice, ~0.8) live on incompatible raw scales — summing them raw lets `bm25_score` +dominate regardless of weighting. Fix: **min-max normalize each component to [0,1]**, using min/max +observed across the **full historical corpus, computed once and frozen at build time** — not +recomputed adaptively as new documents are processed, which would make a score computed today mean +something different than the same score computed a year from now (a reproducibility/leak-safety +issue, not just an accuracy one). This requires a one-time calibration pass across the full +`h41_pdfs/` corpus (or a representative sample) before any covariate value is finalized. + +`norm_sim = (embedding_sim - min_sim_observed) / (max_sim_observed - min_sim_observed)`, and +analogously for `norm_freq`/`norm_bm25`, using the frozen calibration min/max. + +**These normalized values can fall outside [0,1] once the corpus keeps growing past the +calibration snapshot** — `h41_pdfs/` gets a new filing every week, and a future document's raw +score can legitimately be more extreme than anything seen when the calibration bounds were frozen, +pushing the normalized value below 0 or above 1. This isn't a defect so much as an unhandled case: +**explicitly clip each normalized component to `[0, 1]`** (`max(0, min(1, x))`) after normalization, +so the combined score's bounded, magnitude-only property holds regardless of calibration drift. +This trades away the "more extreme than all of history" signal at the boundary — recoverable later +via periodic recalibration if that turns out to matter, not needed now. + +## Combination — magnitude-only default, signed mode behind a flag + +``` +CreditStressNarrativeScore_chunk = w1*norm_freq + w2*norm_bm25 + w3*norm_sim (all in [0,1]) +``` + +All three raw components are already "higher = more like the query" quantities, so this +normalized weighted sum is magnitude-only by construction — no separate design needed for that. +Equal weights (`w1=w2=w3=1/3`) as the starting default. + +A `SIGNED_MODE` flag (default `False`) is reserved but not built: when `True`, run a second pass +against an opposite-polarity ("credit relief/support") lexicon and return +`stress_magnitude - relief_magnitude` instead. Not implemented this round — no stated need for it +yet, but the seam is documented so it isn't a rework later. + +## Aggregation to one weekly value: median + max, not mean + +Both kept, mean dropped. Rationale: the expensive part (running BM25 + embedding queries against +every chunk in a release) happens once regardless of how many ways the results get summarized — +computing median and max from that same per-chunk array afterward is free. They answer genuinely +different questions: median is robust central tendency ("how concerning is this report overall"), +max catches a single alarming passage in an otherwise calm report. Mean was dropped because it's +the aggregation most likely to dilute a real signal across a long, mostly-boilerplate report. +LightGBM tolerates the resulting correlation between the two output columns without issue. + +## Coverage + +Never literally absent — every filing has some narrative/footnote text, even short early ones. +Will score near-baseline during calm periods, which is correct behavior (continuous signal), not a +coverage gap. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FRBNYLoanCPFF.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FRBNYLoanCPFF.md new file mode 100644 index 00000000..1dca605f --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FRBNYLoanCPFF.md @@ -0,0 +1,81 @@ +# FRBNYLoanCPFF + +**Status:** Final recommendation — narrow/literal definition, not generalized. +**Feature type:** unstructured_numeric. +**Source:** H.4.1 supplementary detail table ("Information on Principal Accounts of Commercial +Paper Funding Facility LLC") — narrative/footnote territory, not the main factors table, hence +RAG extraction rather than table_parser. + +## Definition + +``` +FRBNYLoanCPFF_t = "Outstanding principal amount of loan extended by the Federal Reserve Bank of + New York" to the CPFF LLC specifically, as reported in that facility's own + detail table. +``` + +Confirmed present, e.g. Nov 6, 2008 filing: `243,124` (distinct from CPFF LLC's own net portfolio +holdings, `243,305` — the loan principal and the LLC's owned-asset value are close but not +identical, consistent with "usage vs. funding" being genuinely two different numbers). + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +CPFF specifically targeted the commercial paper market — short-term (up to 3-month) corporate +funding. Commercial paper market functioning is closely linked to short-term corporate credit +conditions, and historically CP-market seizures (issuers unable to roll over paper) have preceded +or coincided with the sharpest episodes of broader corporate bond spread widening: a company that +can't fund itself at the short end faces acute near-term distress that eventually reprices its +longer-tenor bonds too. A rising `FRBNYLoanCPFF` balance is fairly unambiguous evidence that +private CP buyers have pulled back badly enough that issuers need the Fed backstop. Honest +limitation, consistent with the coverage note below: this is the narrowest-window feature of the +seven, so its practical contribution is necessarily confined to the 2008–2010 and 2020–2021 +windows where CPFF specifically existed — the economic rationale is sound, but the sample coverage +is the most limited of the group. + +## The scope question, explained + +The concern was whether to keep this feature narrowly tied to the CPFF LLC specifically, or +generalize it to "whichever special-purpose-vehicle facility is currently active" — since CPFF is +only one of several SPV-style facilities the Fed has stood up across different crises (Maiden Lane +in 2008; Maiden Lane II/III and AIG-related SPVs also in 2008-10; Main Street Lending Program, +Municipal Liquidity Facility, and TALF II in 2020-23 — all confirmed present in the filings checked +for `FacilityStress.md`). Not every crisis tool uses this SPV structure — direct-lending programs +like BTFP, the 2020 Primary Dealer Credit Facility, or the ordinary discount window report a +simple loan balance directly in Table 1, with no separate "FRBNY loan to the LLC" detail table. +Only the SPV-style facilities (Fed lends to a purpose-built LLC, which then buys assets) generate +this specific "loan principal + accrued interest" supplementary disclosure pattern. + +So the real choice was: keep `FRBNYLoanCPFF` as literally the CPFF number, or build a generalized +`FRBNYLoanSPV` that picks up whichever SPV-LLC's loan-and-accrued-interest detail table is active +that era (CPFF in 2008 and 2020; Maiden Lane/II/III in 2008-10; Main Street/Municipal Liquidity/ +TALF II in 2020-23). + +## Decision: keep it narrow (CPFF-specific) + +Two reasons, both grounded in what was found building `FacilityStress.md`'s alias table: + +1. **Generalizing would recreate the redundancy problem just found and fixed in `QTIntensity`.** + A generalized "sum of all active SPVs' loan principal" would track very closely with the + CPFF/Maiden Lane/MS-Facilities/Municipal-Liquidity-Facility/TALF-II "net portfolio holdings" + rows *already included* inside the corrected `FacilityStress` formula — the loan funds the SPV's + asset purchases, so the two figures move together. Building a second, broader SPV-loan feature + risks being the same kind of near-duplicate signal QT2 turned out to be relative to QT1+QT3. +2. **Multiple SPVs are routinely active simultaneously**, not one at a time (2010 alone had five: + CPFF, Maiden Lane, Maiden Lane II, Maiden Lane III, plus AIG-related preferred interests; 2023 + had three: Main Street, Municipal Liquidity Facility, TALF II). A "generalized" version isn't a + simple rename — it's a second full era-alias-table build, parallel to `FacilityStress`'s, for a + feature whose narrow, single-facility specificity is arguably a virtue: a human reading a spike + in this series can point to exactly one thing ("the CPFF loan balance"), rather than an + ambiguous, era-shifting blend. + +## Coverage + +Crisis-window-only by nature: nonzero only when the CPFF LLC specifically exists — 2008-2010 and +again 2020-2021 (CPFF II, confirmed launched via the April 16, 2020 filing's own announcement +text). Absent (not zero) outside those windows — no CPFF detail table exists in the report at all +when the facility isn't active. This narrowness is expected and correct, not a defect — see the +earlier discussion in this project's history of BAA10Y's own crisis-window behavior lining up with +exactly these periods. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FacilityStress.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FacilityStress.md new file mode 100644 index 00000000..8349ca0f --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/FacilityStress.md @@ -0,0 +1,152 @@ +# FacilityStress + +**Status:** Final — era-alias table built from direct PDF inspection across 7 filings spanning +2000–2025. +**Feature type:** structured_numeric. +**Source:** H.4.1 Table 1, leaf rows under "Other loans"/"Loans", plus sibling SPV-LLC +"net portfolio holdings" rows. + +## Definition + +``` +FacilityStress_t = sum of all "emergency/temporary facility" leaf rows active this release, + EXCLUDING the routine Primary credit / Secondary credit / Seasonal credit rows, + EXCLUDING the "Other loans"/"Loans" parent row itself (it is a subtotal of its + own children — see "double-counting" below). +``` + +Implementation recommendation, **revised after reading 2010/2015/2020/2023 filings**: don't build +an alias table of facility *names* at all — define `FacilityStress` **by exclusion**, not +inclusion. Building an inclusion list is a losing game: every crisis invents new facility names +(see the era findings below — at least 9 distinct SPV entities appear across the corpus, plus +several differently-named direct-lending facilities under "Loans"), so an inclusion list needs +updating every time a new crisis happens, including ones not yet in this corpus. The set of +*routine/structural* line items, by contrast, is stable across the entire 2000–2025 span: + +**Excluded (always, every era) — the stable "machinery" of the balance sheet:** +Securities held outright (and its children), unamortized premiums/discounts on securities held +outright, repurchase agreements, `Primary credit` / `Secondary credit` / `Seasonal credit` +(routine discount window, present at low levels in every era), float, other Federal Reserve +assets, foreign currency denominated assets, gold stock, SDR certificate account, Treasury +currency outstanding. + +**Included: everything else** — every other child row under `Loans`/`Other loans` (whatever it's +named that era) plus every sibling "Net portfolio holdings of `___` LLC" / "Credit extended to +`___`" / "Preferred interests in `___`" row that appears in the table that week. This +automatically captures whichever ad hoc facility exists in a given era without needing to +recognize its name, and is automatically forward-compatible with facilities that don't exist yet. + +One deliberate carve-out to decide on: **central bank liquidity swaps**. These are standing Fed +machinery for providing dollars to foreign central banks, not a domestic-crisis-specific facility +— but the balance does spike hard during global stress (2020 shows $393 billion). I'd keep it as +its own separate diagnostic series rather than folding it into `FacilityStress` or excluding it +outright, since it's measuring a related-but-distinct thing (foreign-official dollar funding +stress, not domestic credit-facility usage) — flag if you'd rather fold it in. + +**Era findings, from filings actually read (not inferred):** + +| Filing | What's inside `Loans`/`Other loans` (leaf children) | Sibling SPV/LLC lines | +|---|---|---| +| 2000-04-27 | Adjustment credit, Seasonal credit, Extended credit (routine only — no crisis facilities exist yet) | none | +| 2008-11-06 | Primary/Secondary/Seasonal credit, **PDCF**, **ABCP-MMF facility**, Other credit extensions | CPFF LLC, Maiden Lane LLC | +| 2010-01-14 | Primary/Secondary/Seasonal credit, PDCF (0), ABCP (0), **Credit extended to AIG, Inc., net**, **TALF** (direct facility), Other credit extensions | CPFF LLC, Maiden Lane LLC, **Maiden Lane II LLC**, **Maiden Lane III LLC**, **TALF LLC**, **Preferred interests in AIA Aurora LLC and ALICO Holdings LLC** (AIG-related, distinct from Maiden Lane II/III) | +| 2015-06-11 | Primary/Secondary/Seasonal credit, TALF (0), Other credit extensions (0) — PDCF/ABCP/CPFF **fully removed from the table**, not just zeroed | Maiden Lane LLC, Maiden Lane II LLC (0), Maiden Lane III LLC (0), TALF LLC (0) — all present as legacy zero-rows years after economically inactive | +| 2020-04-16 | Primary/Secondary/Seasonal credit, **Primary Dealer Credit Facility** (PDCF revived, full name this time), **Money Market Mutual Fund Liquidity Facility (MMLF)**, Other credit extensions (0) | **Commercial Paper Funding Facility II LLC** — note the name changed from 2008's plain "Commercial Paper Funding Facility LLC" to "...II LLC"; a hardcoded exact-string match would miss this | +| 2023-03-16 | Primary/Secondary/Seasonal credit, **Paycheck Protection Program Liquidity Facility (PPPLF)** (2020-era, still nonzero 3 years later), **Bank Term Funding Program (BTFP)** (launched 3 days earlier), **Other credit extensions = $57.6B** — the filing's own release note explains this specific figure is FDIC-bridge-bank loans for the SVB/Signature Bank resolutions | MS Facilities LLC (Main Street), Municipal Liquidity Facility LLC, TALF II LLC — all 2020-era legacy residuals coexisting with the brand-new BTFP response | +| 2025-09-04 | Primary/Secondary/Seasonal credit, PPPLF (residual), Bank Term Funding Program (0, wound down), Other credit extensions (0) | MS Facilities 2020 LLC (residual) | + +Two concrete, load-bearing findings from this table: +1. **"Other credit extensions" is not a routine catch-all bucket** — in March 2023 it carried $57.6B of real, FDIC-guaranteed bridge-bank lending. It must be *included* in the sum, not excluded as "generic other." +2. **SPV entity names are not stable even for the same underlying program** — CPFF became "CPFF II LLC" in 2020. Matching must be substring/fuzzy ("Commercial Paper Funding Facility" as the stable fragment), never an exact full-string match. + +This also settles the open question from earlier about generalizing `FRBNYLoanCPFF` to "whichever SPV is active": the SPV universe turns out to be at least 9 distinct entities across history (CPFF/CPFF II, Maiden Lane I/II/III, TALF/TALF II, AIA Aurora, ALICO Holdings, Main Street, Municipal Liquidity Facility) — generalizing would mean chasing all of them, a materially bigger scope than first estimated. Reinforces keeping `FRBNYLoanCPFF` narrow (see `FRBNYLoanCPFF.md`). + +## Corpus coverage limitation (implementation finding) + +28 filings, clustered in one contiguous ~7-month window (Feb–Aug 2001), use a font +with a broken character-to-glyph mapping (`(cid:N)` codes instead of real digit +characters in the extracted text layer) — pdfplumber cannot recover the numbers +from these specific source PDFs; this is a defect in the source files themselves, +not a parsing bug. All three structured series (`ReserveBalancesLevel`, +`SecuritiesHeldOutrightLevel`/`QTIntensity`, `FacilityStress`) consistently treat +these 28 dates as missing (`NaN`, dropped from the parquet output) rather than a +false `0` — extraction returning "no lines parsed at all" is an unambiguous +failure signal, since a real filing always has dozens of matched lines. Not +pursued further: this window is calm/pre-crisis and economically unremarkable, +so the forecasting value of recovering it (e.g. by reverse-engineering the font's +CID map) is low relative to the effort; revisit only if it turns out to matter. + +**Full-corpus validation, 1,376 filings (2000-04 to 2026-08), 0 hard failures:** +`FacilityStress` is exactly 0 through 2000–2006, a small footprint in 2007 (TAF's +December launch), large through 2008–2012 (peaking in 2009), a small persistent +legacy residual 2013–2018 (matches the lingering Maiden Lane/TALF LLC rows found +in the 2015 filing), exactly 0 in 2019 (fully wound down), then reactivates for +2020–2022 (COVID) and 2023 (BTFP/regional-bank crisis) before tapering again — +matching documented Fed history at yearly resolution, not just the 7 filings +individually hand-verified during design. + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +`FacilityStress` is, almost by definition, a contemporaneous flag for acute financial-sector +stress — non-zero only during episodes (2008 GFC, 2020 COVID, 2023 regional-bank stress) when +private funding markets have broken down badly enough that institutions turn to Fed emergency +backstops. Its expected value here is less as a smooth, continuously-informative predictor and more +as a **regime indicator**: BAA10Y's own behavior (volatility, tail risk, autocorrelation) is +materially different inside vs. outside these episodes — exactly why `baa10y_stress_2020.yaml` +exists as a dedicated spec rather than folding 2020 into the regular backtest window. A model that +can condition on "are we in a facility-stress regime" should be able to adjust its predictive +distribution (wider intervals, different quantile shape) rather than fitting one stationary model +across calm and crisis periods alike — conceptually the same role the price-history-based +vol-regime detection plays elsewhere in this project, applied here to the Fed's own balance-sheet +response instead of realized price volatility. + +## Why "Other loans"/"Loans" can never be added alongside its children + +Confirmed by direct arithmetic in two filings 15 years apart: + +- **Nov 6, 2008** (`h41_20081106.pdf`): `Other loans` (346,531) = `Primary credit (108,567) + + Secondary credit (0) + Seasonal credit (9) + PDCF (71,642) + ABCP (85,097) + Other credit + extensions (81,215)` = 346,531. Exact match. +- **Sept 4, 2025** (`h41_20250904.pdf`): `Loans` (6,156) = `Primary credit (4,690) + Secondary + credit (0) + Seasonal credit (60) + Paycheck Protection Program Liquidity Facility (1,406) + + Bank Term Funding Program (0) + Other credit extensions (0)` = 6,156. Exact match. +- **Jan 14, 2010** (`h41_20100114.pdf`): same pattern, with two additional children (`Credit + extended to AIG, net`, `Term Asset-Backed Securities Loan Facility, net`) folded into the same + parent subtotal. + +The parent/child relationship is structural and has held across every era checked. Adding the +parent row as a separate term while also adding its own children double- (or triple-, for +PDCF/ABCP-style rows) counts them. + +## Era-by-era alias table (confirmed by direct PDF inspection) + +| Filing checked | "Loans"/"Other loans" children beyond Primary/Secondary/Seasonal | Sibling SPV rows (net portfolio holdings, not nested) | +|---|---|---| +| **2000-04-27** | *(none — table doesn't have this section yet; only "Adjustment credit / Seasonal credit / Extended credit," pre-2003 discount-window naming)* | *(none)* | +| **2008-11-06** (GFC peak) | PDCF ("Primary dealer and other broker-dealer credit"), ABCP ("Asset-backed commercial paper money market mutual fund liquidity facility"), Other credit extensions | CPFF LLC, Maiden Lane LLC | +| **2010-01-14** (GFC wind-down, roster still expanding) | PDCF, ABCP, **Credit extended to AIG, Inc., net**, **Term Asset-Backed Securities Loan Facility, net**, Other credit extensions | CPFF LLC, Maiden Lane LLC, **Maiden Lane II LLC**, **Maiden Lane III LLC**, **TALF LLC** (a second, separate TALF-related line from the "net" one above), **Preferred interests in AIA Aurora LLC and ALICO Holdings LLC** | +| **2015-06-11** (post-crisis baseline) | Term Asset-Backed Securities Loan Facility (near-zero residual), Other credit extensions (0) | Maiden Lane LLC / II / III / TALF LLC — all near-de-minimis residuals; Central bank liquidity swaps = 0 | +| **2020-04-16** (COVID peak) | **"Primary Dealer Credit Facility"** (PDCF reactivated, relabeled), **"Money Market Mutual Fund Liquidity Facility"** (MMLF — ABCP's COVID-era analog, broader scope, relabeled), Other credit extensions | **CPFF II LLC** (relaunched, same "Net portfolio holdings of Commercial Paper Funding Facility ... LLC" label pattern). **Maiden Lane LLC row explicitly removed from Table 1 this release** (announced in the filing itself: balances reduced to de minimis, moved into "Other Federal Reserve assets"). Central bank liquidity swaps spiked to $393B. | +| **2023-03-16** (regional-bank crisis) | Primary credit itself spiked ~10x (discount window stress, not a "facility" row but a real signal), **Paycheck Protection Program Liquidity Facility** (COVID-era, still winding down), **Bank Term Funding Program** (BTFP, brand new — launched March 13, 2023), Other credit extensions (⚠️ see caveat below) | **MS Facilities LLC (Main Street Lending Program)**, **Municipal Liquidity Facility LLC**, **TALF II LLC** | +| **2025-09-04** (current, wound down) | Paycheck Protection Program Liquidity Facility (residual), Bank Term Funding Program (0, fully wound down by ~March 2024 per public record — not independently reverified this session), Other credit extensions (0) | MS Facilities 2020 LLC (Main Street) — small residual | + +## Caveat: a row's label doesn't guarantee a stable real-world meaning + +"Other credit extensions" is **not** the same thing in every era despite the identical label. In +the March 2023 filing, the Fed's own release notes state this row specifically reports "loans that +were extended to depository institutions established by the FDIC" (the SVB/Signature bridge +banks) — a different real-world referent than whatever "Other credit extensions" captured in 2008. +Label-matching alone is not sufficient to guarantee semantic consistency; where it matters, check +the filing's own footnotes/announcement text, not just the row label. + +## Not verified this session (flagging, not asserting) + +- Exact BTFP wind-down date/mechanics (public record: ~March 2024) — not independently re-checked + against a 2024 filing. +- Whether any *other* SPV facilities existed between the ones explicitly found here (e.g., a + Primary Market/Secondary Market Corporate Credit Facility LLC, Municipal Liquidity Facility + predecessor forms) — the alias table above is what was directly confirmed in 7 checked filings, + not a claim of completeness across all 1,376. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/InterventionNarrativeScore.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/InterventionNarrativeScore.md new file mode 100644 index 00000000..3f5227d9 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/InterventionNarrativeScore.md @@ -0,0 +1,100 @@ +# InterventionNarrativeScore + +**Status:** DRAFT — pipeline confirmed, keyword lexicon is a first-pass proposal pending your review. +**Feature type:** unstructured_text. +**Source:** H.4.1 narrative/footnote text. + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +Fed intervention announcements have historically produced sharp, fast reactions in credit spreads — +often at the moment of announcement, sometimes before the facility is meaningfully drawn on at all +(the "announcement effect": a credible backstop reduces perceived tail risk even at low +utilization). The CPFF announcement in Oct 2008 and the March 2020 / March 2023 facility launches +are all examples where the surrounding period saw notable stabilization in credit-market pricing. +Because this score specifically isolates announcement-style language — distinct from the +ongoing-condition language the other two narrative scores capture — it's expected to behave as a +sparse, spiky, event-study-like signal, most informative in the narrow window right around a +facility's launch, which is also plausibly the point of maximum BAA10Y sensitivity to Fed action. +This is a different kind of signal from `FacilityStress` (which reflects usage continuously after +the fact): this one is trying to isolate the announcement moment itself, often the more +market-moving event of the two. + +## Pipeline + +Identical to `CreditStressNarrativeScore.md`: per-chunk `keyword_freq` + `bm25_score` + +`embedding_sim`, min-max normalized against corpus-wide frozen calibration stats, combined via +equal-weighted sum (magnitude-only, `SIGNED_MODE` flag reserved), aggregated to weekly median + +max. Only the lexicon/query changes. + +## Proposed lexicon (draft — not yet validated against real chunks) + +Distinguishing target: language describing the **act of the Fed intervening** — a new facility +being announced or launched — as distinct from language describing market *conditions* +(`CreditStressNarrativeScore`, `LiquidityNarrativeScore`). This axis should spike sharply and +briefly right at a facility's launch (a genuinely different temporal signature than the other two, +which should track ongoing conditions more smoothly), based on the actual announcement language +seen directly in this session's PDF reads (e.g. the April 2020 CPFF II and March 2023 BTFP +announcement pages). + +Keywords / phrases: +- "the Federal Reserve announced" +- "extended credit to" +- "under the authority of section 13(3)" +- "the Committee directs the Desk" +- "was formed to" +- "began purchasing" / "began extending loans" +- "facility established" +- "unusual and exigent circumstances" +- "with approval of the Treasury Secretary" +- "emergency lending" +- "in response to" + +Query for BM25/embedding: "Federal Reserve announces a new emergency lending facility" + +## Open before finalizing + +Same caveat as `LiquidityNarrativeScore`: this lexicon hasn't been checked against real chunks. +The announcement-page language actually seen this session ("On March 17, 2020, the Federal Reserve +announced the CPFF...", "The Federal Reserve announced the BTFP on March 12, 2023...") matches +several of these phrases closely, which is a reasonable sanity check but not a substitute for +running the pipeline against real chunks before treating this as final. + +## One design question worth flagging + +Because this axis is about *announcements* specifically, it may be structurally sparser and more +spike-like than the other two narrative scores (most weekly releases contain zero intervention +language at all, since most weeks aren't a facility-launch week) — closer in shape to +`FacilityStress`/`FRBNYLoanCPFF` than to a smoothly-varying sentiment score. Worth deciding whether +`median` (which will be near-zero almost always) is actually useful for this one, or whether `max` +alone carries the real signal here. Not resolved — flagging for your input. + +## Confirmed limitation, from the full-corpus run (implementation finding) + +The concern above was optimistic about what `max` actually captures. Full-corpus yearly means show +`max` spiking to 0.76 in 2009 (correct — genuine GFC intervention language) but then **staying +elevated at 0.72–0.78 through 2010–2014**, and still at 0.52–0.59 through 2015–2019 — years with no +new intervention at all. Root cause, confirmed directly: the "Notes on consolidation" boilerplate +describing the *2008* interventions ("On June 26, 2008, the Federal Reserve Bank of New York... +extended credit... under the authority of section 13(3)...") gets reprinted **verbatim in every +weekly filing for years** after the events themselves, since it isn't removed until the underlying +LLC is fully wound up. A keyword/semantic-similarity scorer has no way to distinguish "this +describes an intervention happening now" from "this is standing boilerplate describing an old one" +— both use identical language. So in practice, `InterventionNarrativeScore_max` behaves less like +the hoped-for "sparse, spiky, announcement-moment" signal and more like a noisy, lagging proxy for +"an SPV facility from a past intervention is still on the books" — informative, but not what the +feature was designed to isolate. Fixing this properly needs date-aware reasoning (e.g., flagging +only *new* intervention language vs. text repeated from a prior week) — exactly the kind of thing +the long-term agentic-RAG phase could do (compare this week's chunk against last week's to detect +"is this new"), not something the current vanilla pipeline attempts. Documented here rather than +silently patched — a design decision for you, not mine to make unilaterally given this pipeline's +lexicon/lexicon-comparison mechanics are otherwise settled. + +Separately: `max` also drifts upward across all three narrative scores after 2020 even in calm +years (e.g. `CreditStressNarrativeScore_max` averages ~0.44 in 2012-2019 vs. ~0.53-0.63 in +2022-2026) — filings have gotten longer and more complex (more facility line items, more +footnotes) since 2020, so there are simply more chunks per filing and thus more chances to hit a +high-scoring outlier chunk by chance, independent of genuine stress. Worth keeping in mind when +interpreting `max` trends across the pre-/post-2020 boundary specifically. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/LiquidityNarrativeScore.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/LiquidityNarrativeScore.md new file mode 100644 index 00000000..b504c623 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/LiquidityNarrativeScore.md @@ -0,0 +1,58 @@ +# LiquidityNarrativeScore + +**Status:** DRAFT — pipeline confirmed, keyword lexicon is a first-pass proposal pending your review. +**Feature type:** unstructured_text. +**Source:** H.4.1 narrative/footnote text. + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +Corporate bond spreads are conventionally decomposed into a default-risk component and a +liquidity/technical component — well established in fixed-income research, especially post-2008 +work distinguishing "true" credit risk from illiquidity-driven spread widening. `CreditStress +NarrativeScore` is oriented toward the former (lending/default-risk language); this score targets +the latter (funding-market/money-market functioning language) specifically so the two axes aren't +conflated into one blended signal. The hypothesis: a liquidity-driven BAA10Y move (e.g., dealers +pulling back from market-making with no change in issuer credit quality) should show up +disproportionately here relative to `CreditStressNarrativeScore`, giving the forecasting model a +way to distinguish the two regimes even though both eventually move the same headline spread. + +## Pipeline + +Identical to `CreditStressNarrativeScore.md`: per-chunk `keyword_freq` + `bm25_score` + +`embedding_sim`, min-max normalized against corpus-wide frozen calibration stats, combined via +equal-weighted sum (magnitude-only, `SIGNED_MODE` flag reserved), aggregated to weekly median + +max. Only the lexicon/query changes. + +## Proposed lexicon (draft — not yet validated against real chunks) + +Distinguishing target: **money-market / funding-liquidity conditions**, as distinct from +`CreditStressNarrativeScore`'s lending/default-risk framing. There will be real overlap in +practice (a stressed sentence often touches both), which is expected, not a flaw — the point is +which axis's language dominates a given chunk, not a clean partition. + +Keywords: +- "liquidity in short-term funding markets" +- "money market pressures" +- "funding markets" +- "repo market" +- "reserve scarcity" +- "market functioning" +- "liquidity facility" +- "liquidity support" +- "liquidity injections" +- "term funding" +- "commercial paper market" +- "short-term funding" + +Query for BM25/embedding: "liquidity conditions and funding market stress" + +## Open before finalizing + +This lexicon hasn't been checked against real chunks the way `CreditStressNarrativeScore`'s was +(that one came with a worked example in the original reference doc; this one doesn't). Recommend a +quick sanity pass — pull a handful of narrative chunks from a known-stressed filing (e.g. the +2008-11-06 or 2020-04-16 releases already read this session) and confirm the keywords actually +fire on real Fed language — before treating this as final. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/QTIntensity.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/QTIntensity.md new file mode 100644 index 00000000..859afeb4 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/QTIntensity.md @@ -0,0 +1,102 @@ +# QTIntensity + +**Status:** Final — resolved this session. +**Feature type:** structured_numeric. +**Source:** H.4.1 Table 1, "Securities held outright" (2008+ label; "U.S. government securities... bought outright" pre-2008). + +## Definition + +``` +QTIntensity_t = -Δ(Securities Held Outright)_t +``` + +Positive = tightening (balance-sheet runoff). Negative = expansion (QE). This is a single-term, +non-derived-from-anything-else quantity: the week-over-week change in the one line item that +"QT"/"QE" actually refers to in Fed communications and financial media. + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +QE/QT operate on corporate credit spreads through the "portfolio balance channel": when the Fed +buys securities (QE, `QTIntensity` negative), it removes duration and safe collateral from the +market, pushing yield-seeking investors further out the risk curve — including into corporate +credit — which compresses corporate-Treasury spreads. This is a well-documented effect from QE1– +QE3 (published Fed and academic research found measurable Baa/Treasury spread compression +attributable to the purchase programs specifically, not just to the broader macro environment). +`QTIntensity` positive (runoff) works in reverse: it withdraws that support and, especially at an +accelerated or poorly-telegraphed pace, has coincided with tighter financial conditions and +episodic funding-market stress (the Sept 2019 repo spike occurred during a QT episode). The +expectation is that sustained positive `QTIntensity` correlates with gradual BAA10Y widening, and +sharp or accelerating readings may coincide with episodic widening events — distinct from and +complementary to `FacilityStress`, which only fires during acute crises, not gradual QT-driven +tightening. + +## What was proposed and rejected + +An earlier draft (`QTIntensity.pdf`, the collaborator-AI doc) proposed a 3-term weighted combination: + +``` +QTIntensity_t = w1*QT1_t + w2*QT2_t + w3*QT3_t +QT1_t = -Δ(Securities Held Outright)_t +QT2_t = -Δ(Reserve Bank credit)_t +QT3_t = -Δ(Facility Usage)_t where Facility Usage = Term Auction Credit + Other Loans + PDCF + ABCP + CPFF + Maiden Lane + Other credit extensions +``` + +This was rejected for three reasons, in order of severity: + +### 1. QT2 is not independent of QT1 and QT3 — it's (almost exactly) their sum + +Verified against the real Nov 6, 2008 filing (`h41_20081106.pdf`), using the weekly-average +column and correcting `Facility Usage` for the double-counting bug below first: + +``` +ΔSecurities (-355) + ΔFacilityUsage_corrected (Term auction 0 + Other loans -29,800 + CPFF +185,189 + Maiden Lane +41 = 155,430) + + ΔFloat (-86) + ΔOtherFRAssets (+27,909) += -355 + 155,430 - 86 + 27,909 = 182,898 +``` + +`ΔReserve Bank credit` that same week was **exactly +182,898**. This is not approximate — `Reserve +Bank credit` is defined in the table as the sum of Securities held outright, Term auction credit, +Other loans, CPFF, Maiden Lane, Float, and Other F.R. assets. So `QT2` already contains essentially +all of `QT1` and `QT3`, modulo two small residual lines (Float, Other F.R. assets). Summing all +three with any positive weights counts the same balance-sheet movement two to three times over. +Letting PCA or LightGBM "learn the weights" doesn't fix near-perfect-by-construction collinearity — +it makes the learned weights unstable and uninterpretable, not correct. + +### 2. QT3's "Facility Usage" double-counts, the same way FacilityStress did + +`Other Loans` is a parent/subtotal row of `Primary credit + Secondary credit + Seasonal credit + +PDCF + ABCP + Other credit extensions` (confirmed exactly, see `FacilityStress.md`). Summing +`Other Loans` and `PDCF` and `ABCP` as separate terms double-counts PDCF and ABCP. + +### 3. Internal basis inconsistency in the original doc + +The H.4.1 table reports two parallel figures for every line: a weekly-average basis and a +Wednesday point-in-time-level basis. The original doc's `QT1` example (`489,691`, change `-398`) +pulled from the Wednesday-level column; its `QT2` and `QT3` examples both correctly used the +weekly-average column (`490,027`, change `-355` is the actual weekly-average figure for the same +`Securities held outright` line). Mixing bases within one formula means the terms aren't measuring +the same thing before you even get to summing them. + +## Decision + +Use `QT1` alone, computed consistently on the weekly-average basis (matching every other feature +in this project). No combination, no weights, no `Reserve Bank credit` term. + +If a future need arises to capture "facilities offsetting QT" as a *separate* idea, it should be +its own two-term feature (`QT1 - FacilityUsageChange_corrected`, no `Reserve Bank credit`), not +folded back into `QTIntensity`. Not building this now — no stated requirement for it. + +## Implementation notes + +- `QTIntensity` is a **derived** series: extract the raw level (`SecuritiesHeldOutright`) per + release as the atomic stored value, then compute the week-over-week diff in a downstream step + over consecutive stored levels — not by looking up "last week's value" inside the per-document + extraction step. +- Label drift across eras: pre-2008 filings use "U.S. government securities... bought outright — + system account" (confirmed in `h41_20000427.pdf`); 2008 onward uses "Securities held outright" + consistently through the 2025 filing checked. One alias pair to handle, not several. +- Full coverage 2000–2025 — this line has existed in every filing checked (2000, 2008, 2010, 2015, + 2020, 2023, 2025). No absent-period handling needed. diff --git a/implementations/BAA10Y_forecasting/RAGtools/feature_docs/ReserveBalancesLevel.md b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/ReserveBalancesLevel.md new file mode 100644 index 00000000..afd70e81 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/feature_docs/ReserveBalancesLevel.md @@ -0,0 +1,50 @@ +# ReserveBalancesLevel + +**Status:** Final. +**Feature type:** structured_numeric. +**Source:** H.4.1 Table 1, "Reserve balances with Federal Reserve Banks" (bottom line of the +factors table — the report's namesake figure). + +## Definition + +``` +ReserveBalancesLevel_t = value of "Reserve balances with Federal Reserve Banks" (weekly-average basis) +``` + +A direct level, not a derived quantity — no diffing, no combination. + +## Why this is expected to help forecast BAA10Y + +*Hypothesis, to be validated once this covariate is actually backtested — not a claim of proven +predictive power.* + +The level (and trend) of reserve balances is the primary real-time gauge market participants and +the Fed itself use to judge whether the banking system sits in an "ample" or "scarce" reserves +regime. Scarce or rapidly-declining reserves have historically preceded episodes of money-market +dysfunction (the Sept 2019 repo spike is the clearest example) that can spill over into broader +credit-market liquidity and, from there, into corporate bond spreads — banks and dealers with +tighter balance-sheet capacity intermediate credit less freely, widening the liquidity-driven +component of BAA10Y. As a *level* rather than a flow, this is expected to complement `QTIntensity` +(a rate of change): two systems shrinking reserves at the same pace can be in very different stress +postures depending on how much buffer is left, which only the level captures. + +## Coverage — confirmed across the full corpus span + +Checked directly in filings from 2000, 2008, 2010, 2015, 2020, 2023, and 2025 — present in every +one, with only minor label drift: + +| Era | Label seen | +|---|---| +| 2000 | "Reserve balances with F.R. Banks" | +| 2008–2025 | "Reserve balances with Federal Reserve Banks" | + +No absent-period handling needed — this is the one line item guaranteed to exist in every H.4.1 +release ever published, since it's what the report exists to track. + +## Implementation notes + +- Extraction: table_parser, single row, weekly-average column, consistent with every other + structured feature in this project. +- No known double-counting or redundancy risk — it does not sum other lines, and while it is + itself a component of the balance sheet's liability side, none of the other 6 features in scope + this round are derived from it, so no collinearity concern analogous to `QTIntensity`'s. diff --git a/implementations/BAA10Y_forecasting/RAGtools/running_the_pipeline.md b/implementations/BAA10Y_forecasting/RAGtools/running_the_pipeline.md new file mode 100644 index 00000000..57e95344 --- /dev/null +++ b/implementations/BAA10Y_forecasting/RAGtools/running_the_pipeline.md @@ -0,0 +1,147 @@ +# How to run the H.4.1 RAG pipeline and produce the `.parquet` outputs + +This is an operational runbook — how to actually execute the pipeline described +in `feature_docs/*.md`, `config_design.md`, and `chunking_design.md`. Read those +first for *why* the features are defined the way they are; this doc is only +about *running* the code that implements them. + +## What this produces + +Nine covariate series, as ten parquet files, under `data/h41_rag/` (repo root): + +| File | Feature | Built by | +|---|---|---| +| `ReserveBalancesLevel.parquet` | `reserve_balances_level_l1b` | Phase A | +| `SecuritiesHeldOutrightLevel.parquet` | (audit copy — `QTIntensity`'s raw input, not itself registered as a covariate) | Phase A | +| `QTIntensity.parquet` | `qt_intensity_l1b` | Phase A | +| `FacilityStress.parquet` | `facility_stress_l1b` | Phase A | +| `CreditStressNarrativeScore_median.parquet` / `_max.parquet` | `credit_stress_narrative_median_l1b` / `_max_l1b` | Phase B | +| `LiquidityNarrativeScore_median.parquet` / `_max.parquet` | `liquidity_narrative_median_l1b` / `_max_l1b` | Phase B | +| `InterventionNarrativeScore_median.parquet` / `_max.parquet` | `intervention_narrative_median_l1b` / `_max_l1b` | Phase B | +| `_narrative_chunks_cache.parquet` | (intermediate cache, not a covariate) | Phase B | + +`FRBNYLoanCPFF` (`unstructured_numeric`) is not yet built — see `FRBNYLoanCPFF.md`. + +## The centerpiece files — where the actual logic lives + +There isn't one single "core" file — the pipeline has two genuinely separate +engines, sharing nothing but the corpus and the output convention: + +- **`src/baa10y_forecasting/rag/structured_extraction.py`** — the deterministic + engine (Phase A). Table-line regex parsing of H.4.1's "Factors Affecting + Reserve Balances" table, plus `FacilityStress`'s exclusion-based leaf-sum + rule. No embeddings, no LLM calls, no RAG in the retrieval sense — this is + the file to read to understand *how a number gets pulled off the page*. +- **`src/baa10y_forecasting/rag/narrative_scoring.py`** — the RAG engine's + scoring core (Phase B). `keyword_freq` + BM25 + E5 embedding similarity, + calibrated normalization with clipping, magnitude-only combination, + median/max aggregation. This is the file to read to understand *how a + chunk of prose becomes a 0-1 score*. +- **`src/baa10y_forecasting/rag/narrative_extraction.py`** — feeds Phase B: + splits each filing's text into narrative/footnote chunks (by excluding + table-row-shaped lines, not by positively classifying block types). +- **`src/baa10y_forecasting/rag/lexicons.py`** — not logic, but the single + place all three narrative features' keyword lists and BM25/embedding + queries are defined. Change the scoring *target* here, not in + `narrative_scoring.py`. + +The two `build_*.py` files below are orchestration/entry-points — thin glue +that loops over the corpus and calls into the two engines above. They are +what you actually run, but not where the interesting logic lives. + +## Prerequisites + +1. **Use the nested venv, not the root one.** This pipeline's dependencies + (`pdfplumber`, `chromadb`, `sentence-transformers`, `pandas`, `pyarrow`, + `rank-bm25`) live in `implementations/BAA10Y_forecasting/.venv` — a + **separate** environment from the repo-root `.venv` that + `01_BAA10Y_multivariate_backtest.ipynb`/`data.py` use. Running these + scripts with the wrong interpreter fails on missing imports. + - Windows: `implementations\BAA10Y_forecasting\.venv\Scripts\python.exe` + - Linux/Coder: `implementations/BAA10Y_forecasting/.venv/bin/python` +2. **`h41_pdfs/` must exist at the repo root** with the weekly H.4.1 PDFs + (`h41_YYYYMMDD.pdf`). This pipeline only reads from it, never writes to it. +3. **The E5 embedding model should already be cached locally** (from + `00_RagPrototype.ipynb`'s earlier work), under + `~/.cache/huggingface/hub/models--intfloat--e5-base`. `build_narrative_series.py` + forces `HF_HUB_OFFLINE=1` itself, so it never attempts a network call — it + will fail fast if that cache doesn't exist yet. If you're setting this up + on a machine that's never run the E5 model before, you'll need one + successful *online* load first, which on this network requires either a + working proxy cert or the corporate-proxy workaround below. +4. **Corporate-proxy TLS note**: if any dependency needs installing fresh + (`uv add ...`), this network's proxy intercepts TLS and breaks the default + cert check. Add `--system-certs` to the `uv` command (or set + `UV_NATIVE_TLS=1` once, persistently, per-user). + +## Running it + +Both phases are independent — run in either order, or in parallel. Each is a +plain script, not a notebook; run from any working directory, using the +nested venv's interpreter. Each processes all 1,376 filings and takes real +wall-clock time — Phase A is fast (pure text parsing, ~a few minutes), Phase +B is slow (embeds ~96,000 chunks, expect considerably longer). Both print +progress every 200 filings, so a long silence isn't necessarily a hang. + +**Phase A — structured features:** +``` +/implementations/BAA10Y_forecasting/.venv/Scripts/python.exe ^ + /implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_structured_series.py +``` + +**Phase B — narrative-score features:** +``` +/implementations/BAA10Y_forecasting/.venv/Scripts/python.exe ^ + /implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_narrative_series.py +``` + +Both scripts resolve the repo root and the `data/h41_rag/` output directory +themselves (see `paths.py`) — nothing to configure, no arguments to pass. + +## What success looks like + +Phase A, printed at the end: +``` +Parsed 1376 filings successfully, 0 failed. +Wrote parquet files to /data/h41_rag: + ReserveBalancesLevel.parquet: 1348 rows, 2000-04-19 to 2026-08-26 + SecuritiesHeldOutrightLevel.parquet: 1348 rows, ... + QTIntensity.parquet: 1347 rows, ... + FacilityStress.parquet: 1348 rows, ... +``` +The 28-filing gap (1376 → 1348) is expected — see "Known limitations" below, +not a bug to chase. + +Phase B, printed at the end (one block per feature): +``` +Extracted 96188 narrative chunks from 1376 filings. +=== CreditStressNarrativeScore === + wrote CreditStressNarrativeScore_median.parquet / _max.parquet: 1376 rows, ... +=== LiquidityNarrativeScore === + ... +=== InterventionNarrativeScore === + ... +``` + +## Known limitations (expected, not bugs) + +- **28 filings (Feb-Aug 2001) are silently dropped from all four Phase-A + series.** Those specific source PDFs use a broken font encoding pdfplumber + can't recover text from — a defect in the source files, not the parser. + See `FacilityStress.md`'s "Corpus coverage limitation" section. +- **`InterventionNarrativeScore_max` runs elevated for years after a real + intervention**, not just during it — stale boilerplate describing old + facilities gets reprinted verbatim for years. See + `InterventionNarrativeScore.md`'s "Confirmed limitation" section. +- **`CreditStressNarrativeScore`'s validation surfaced a false-positive risk** + (its top-scoring chunk in a small sample was generic MBS boilerplate, not + real credit-stress content) — usable, but trust it less than + `LiquidityNarrativeScore`, which validated cleanly. + +## After running + +Output lands in `data/h41_rag/`, already gitignored (not committed). To make +these series usable by the forecasting notebooks, see `data.py`'s +`H41_RAG_SERIES_SPECS`/`H41_RAG_OPTIONAL_COVARIATE_SERIES_IDS` — already wired +in on this branch; if copying the `.parquet` files to another checkout, that +wiring needs porting too (see that branch's own notes, not covered here). diff --git a/implementations/BAA10Y_forecasting/__init__.py b/implementations/BAA10Y_forecasting/__init__.py index 43b29404..78030ce2 100644 --- a/implementations/BAA10Y_forecasting/__init__.py +++ b/implementations/BAA10Y_forecasting/__init__.py @@ -37,7 +37,17 @@ HYOAS_OPTIONAL_COVARIATE_SERIES_IDS, SERIES_ID_HYOAS_OBSERVED_CHANGE, SERIES_ID_HYOAS_PROXY_CHANGE, - + H41_RAG_OPTIONAL_COVARIATE_SERIES_IDS, + H41_RAG_SERIES_SPECS, + SERIES_ID_RESERVE_BALANCES_LEVEL, + SERIES_ID_QT_INTENSITY, + SERIES_ID_FACILITY_STRESS, + SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN, + SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX, + SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN, + SERIES_ID_LIQUIDITY_NARRATIVE_MAX, + SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN, + SERIES_ID_INTERVENTION_NARRATIVE_MAX, ) @@ -66,4 +76,16 @@ "build_baa10y_multivariate_service", "HYOAS_OPTIONAL_COVARIATE_SERIES_IDS", "SERIES_ID_HYOAS_OBSERVED_CHANGE", - "SERIES_ID_HYOAS_PROXY_CHANGE",] \ No newline at end of file + "SERIES_ID_HYOAS_PROXY_CHANGE", + "H41_RAG_OPTIONAL_COVARIATE_SERIES_IDS", + "H41_RAG_SERIES_SPECS", + "SERIES_ID_RESERVE_BALANCES_LEVEL", + "SERIES_ID_QT_INTENSITY", + "SERIES_ID_FACILITY_STRESS", + "SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN", + "SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX", + "SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN", + "SERIES_ID_LIQUIDITY_NARRATIVE_MAX", + "SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN", + "SERIES_ID_INTERVENTION_NARRATIVE_MAX", +] \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/data.py b/implementations/BAA10Y_forecasting/data.py index 4a69de9a..33cc7e8b 100644 --- a/implementations/BAA10Y_forecasting/data.py +++ b/implementations/BAA10Y_forecasting/data.py @@ -255,6 +255,10 @@ def _default_cache_dir() -> Path: # resolved against repo ``data/`` (stem filenames like ``gspc_adj_close_1d.parquet`` per ticker). DEFAULT_YAHOO_CACHE_DIR = DEFAULT_CACHE_DIR / "yfinance" DEFAULT_FRED_CACHE_DIR = DEFAULT_CACHE_DIR / "fred" +# H.4.1-derived series (RAGtools/, src/baa10y_forecasting/rag/) -- precomputed +# offline from h41_pdfs/, no live fetch. See build_structured_series.py and +# build_narrative_series.py. +DEFAULT_H41_RAG_CACHE_DIR = DEFAULT_CACHE_DIR / "h41_rag" # Keys are FRED series ids; values are (description, units, pandas frequency hint) # for ``scripts/fetch_fred.py`` registration — keep in sync with _fred_frame call sites below. @@ -329,6 +333,87 @@ def _default_cache_dir() -> Path: SERIES_ID_HYOAS_PROXY_CHANGE, ] +# H.4.1-derived covariates -- see RAGtools/feature_docs/*.md for the design +# rationale behind each one, and FRBNYLoanCPFF.md for the one planned feature +# NOT yet built (needs RAG retrieval over footnote text, not table parsing; +# only the 9 below -- 3 structured + 3 narrative scores x {median, max} -- +# exist as parquet so far). +SERIES_ID_RESERVE_BALANCES_LEVEL = "reserve_balances_level_l1b" +SERIES_ID_QT_INTENSITY = "qt_intensity_l1b" +SERIES_ID_FACILITY_STRESS = "facility_stress_l1b" +SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN = "credit_stress_narrative_median_l1b" +SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX = "credit_stress_narrative_max_l1b" +SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN = "liquidity_narrative_median_l1b" +SERIES_ID_LIQUIDITY_NARRATIVE_MAX = "liquidity_narrative_max_l1b" +SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN = "intervention_narrative_median_l1b" +SERIES_ID_INTERVENTION_NARRATIVE_MAX = "intervention_narrative_max_l1b" + +H41_RAG_OPTIONAL_COVARIATE_SERIES_IDS: list[str] = [ + SERIES_ID_RESERVE_BALANCES_LEVEL, + SERIES_ID_QT_INTENSITY, + SERIES_ID_FACILITY_STRESS, + SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN, + SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX, + SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN, + SERIES_ID_LIQUIDITY_NARRATIVE_MAX, + SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN, + SERIES_ID_INTERVENTION_NARRATIVE_MAX, +] + +# series_id -> (parquet filename stem under DEFAULT_H41_RAG_CACHE_DIR, description, units). +# All 9 are structurally identical (load parquet, expand weekly->daily, lag) unlike +# the bespoke per-covariate blocks above -- a small loop over this table is the +# right amount of abstraction here, not the "it's all just code" bespoke-block +# pattern, since these genuinely are one shape. Add a row here plus the parquet +# file to add a new H.4.1-derived covariate. +H41_RAG_SERIES_SPECS: dict[str, tuple[str, str, str]] = { + SERIES_ID_RESERVE_BALANCES_LEVEL: ( + "ReserveBalancesLevel", + "Reserve balances with Federal Reserve Banks (H.4.1 Table 1), lagged 1 business day", + "millions-of-dollars", + ), + SERIES_ID_QT_INTENSITY: ( + "QTIntensity", + "-1 x week-over-week change in Securities Held Outright (H.4.1), lagged 1 business day", + "millions-of-dollars", + ), + SERIES_ID_FACILITY_STRESS: ( + "FacilityStress", + "Sum of non-routine Fed credit-facility usage, exclusion-based (H.4.1), lagged 1 business day", + "millions-of-dollars", + ), + SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN: ( + "CreditStressNarrativeScore_median", + "Median per-chunk credit-stress narrative score across a release's H.4.1 text, lagged 1 business day", + "score-0-1", + ), + SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX: ( + "CreditStressNarrativeScore_max", + "Max per-chunk credit-stress narrative score across a release's H.4.1 text, lagged 1 business day", + "score-0-1", + ), + SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN: ( + "LiquidityNarrativeScore_median", + "Median per-chunk liquidity-stress narrative score across a release's H.4.1 text, lagged 1 business day", + "score-0-1", + ), + SERIES_ID_LIQUIDITY_NARRATIVE_MAX: ( + "LiquidityNarrativeScore_max", + "Max per-chunk liquidity-stress narrative score across a release's H.4.1 text, lagged 1 business day", + "score-0-1", + ), + SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN: ( + "InterventionNarrativeScore_median", + "Median per-chunk Fed-intervention-announcement narrative score, lagged 1 business day", + "score-0-1", + ), + SERIES_ID_INTERVENTION_NARRATIVE_MAX: ( + "InterventionNarrativeScore_max", + "Max per-chunk Fed-intervention-announcement narrative score, lagged 1 business day", + "score-0-1", + ), +} + DEFAULT_COVARIATE_SERIES_IDS: list[str] = [ SERIES_ID_VIX_LEVEL, SERIES_ID_VIX_CHANGE, @@ -431,6 +516,38 @@ def _build_monthly_unemployment_feature( return _apply_one_business_day_feature_lag(daily) +def _build_h41_rag_feature( + parquet_stem: str, + *, + cache_dir: Path, + start: str, + end: str | None, +) -> pd.DataFrame: + """Load a precomputed H.4.1-derived series and apply the standard leak-safety treatment. + + Built entirely offline from the local ``h41_pdfs/`` corpus (see + ``src/baa10y_forecasting/rag/`` and ``RAGtools/feature_docs/``) -- no live + fetch, no ``refresh`` flag, just a parquet read. ``timestamp`` in the + source parquet is the H.4.1 "week ended" (Wednesday) statement date; the + filing's actual release is the next business day (Thursday, ~4:30pm ET, + per the release date printed on the filing itself). Unlike CPI/ + unemployment's *estimated* publication lag, this is the filing's own + stated release day, not a conservative proxy. + """ + path = cache_dir / f"{parquet_stem}.parquet" + if not path.exists(): + raise RuntimeError( + f"H.4.1-derived series file not found: {path}. Run " + "src/baa10y_forecasting/rag/build_structured_series.py and/or " + "build_narrative_series.py first." + ) + frame = pd.read_parquet(path)[["timestamp", "value"]].copy() + frame["timestamp"] = pd.to_datetime(frame["timestamp"]) + frame["released_at"] = frame["timestamp"] + pd.offsets.BDay(1) + daily = _business_daily_expand_from_releases(frame, start=start, end=end) + return _apply_one_business_day_feature_lag(daily) + + def _build_daily_fred_level_feature( fred_id: str, *, @@ -496,6 +613,7 @@ def build_baa10y_multivariate_service( # noqa: PLR0912, PLR0915 end: str | None = None, yahoo_cache_dir: Path | None = None, fred_cache_dir: Path | None = None, + h41_rag_cache_dir: Path | None = None, ) -> DataService: """Build DataService with BAA10Y target plus optional leak-safe covariates. @@ -536,6 +654,10 @@ def build_baa10y_multivariate_service( # noqa: PLR0912, PLR0915 raise RuntimeError("Could not resolve yahoo/fred cache directories.") yahoo_dir.mkdir(parents=True, exist_ok=True) + h41_rag_dir = _as_absolute_cache(h41_rag_cache_dir or DEFAULT_H41_RAG_CACHE_DIR) + if h41_rag_dir is None: + raise RuntimeError("Could not resolve H.4.1-derived-series cache directory.") + def _handle_covariate_error(series_id: str, exc: Exception) -> None: if strict_covariates: raise RuntimeError(f"Failed to build required covariate {series_id!r}.") from exc @@ -859,7 +981,27 @@ def _handle_covariate_error(series_id: str, exc: Exception) -> None: SERIES_ID_HYOAS_PROXY_CHANGE, exc, ) - + + ## H.4.1-derived covariates (RAG-built, offline, no refresh) -- see H41_RAG_SERIES_SPECS + for series_id, (parquet_stem, description, units) in H41_RAG_SERIES_SPECS.items(): + if series_id not in desired_set: + continue + try: + frame = _build_h41_rag_feature(parquet_stem, cache_dir=h41_rag_dir, start=start, end=end) + svc.register( + series_id, + StaticFrameAdapter(frame), + SeriesMetadata( + series_id=series_id, + description=description, + source="H.4.1 (Federal Reserve weekly release), RAG-derived", + units=units, + frequency="B", + table_id=f"h41_rag:{parquet_stem}:l1b", + ), + ) + except (RuntimeError, ValueError) as exc: + _handle_covariate_error(series_id, exc) return svc @@ -890,4 +1032,16 @@ def _handle_covariate_error(series_id: str, exc: Exception) -> None: "build_baa10y_multivariate_service", "HYOAS_OPTIONAL_COVARIATE_SERIES_IDS", "SERIES_ID_HYOAS_OBSERVED_CHANGE", - "SERIES_ID_HYOAS_PROXY_CHANGE",] \ No newline at end of file + "SERIES_ID_HYOAS_PROXY_CHANGE", + "H41_RAG_OPTIONAL_COVARIATE_SERIES_IDS", + "H41_RAG_SERIES_SPECS", + "SERIES_ID_RESERVE_BALANCES_LEVEL", + "SERIES_ID_QT_INTENSITY", + "SERIES_ID_FACILITY_STRESS", + "SERIES_ID_CREDIT_STRESS_NARRATIVE_MEDIAN", + "SERIES_ID_CREDIT_STRESS_NARRATIVE_MAX", + "SERIES_ID_LIQUIDITY_NARRATIVE_MEDIAN", + "SERIES_ID_LIQUIDITY_NARRATIVE_MAX", + "SERIES_ID_INTERVENTION_NARRATIVE_MEDIAN", + "SERIES_ID_INTERVENTION_NARRATIVE_MAX", +] \ No newline at end of file diff --git a/implementations/BAA10Y_forecasting/pyproject.toml b/implementations/BAA10Y_forecasting/pyproject.toml index 94acb3b4..cdb7cfcd 100644 --- a/implementations/BAA10Y_forecasting/pyproject.toml +++ b/implementations/BAA10Y_forecasting/pyproject.toml @@ -7,6 +7,7 @@ requires-python = ">=3.12" dependencies = [ "chromadb>=1.5.9", "docx2txt>=0.9", + "ipykernel>=7.3.0", "langchain>=1.3.14", "langchain-chroma>=1.1.0", "langchain-community>=0.4.2", @@ -14,6 +15,9 @@ dependencies = [ "langchain-huggingface>=1.2.2", "langchain-text-splitters>=1.1.2", "openai-whisper>=20250625", + "pandas>=3.0.5", + "pdfplumber>=0.11.10", + "pyarrow>=25.0.1", "pypdf>=6.14.2", "python-pptx>=1.0.2", "rank-bm25>=0.2.2", @@ -29,4 +33,4 @@ requires = ["uv_build>=0.12.1,<0.13.0"] build-backend = "uv_build" [tool.uv] -package = false \ No newline at end of file +package = false diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/__init__.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/__init__.py new file mode 100644 index 00000000..cc1a537e --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/__init__.py @@ -0,0 +1,2 @@ +def main() -> None: + print("Hello from baa10y-forecasting!") diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/__init__.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/__init__.py new file mode 100644 index 00000000..3e899438 --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/__init__.py @@ -0,0 +1,5 @@ +"""H.4.1-derived RAG/feature-engineering package for BAA10Y forecasting. + +See ``RAGtools/feature_docs/*.md`` for the design rationale and era findings +behind every feature built here. +""" diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_narrative_series.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_narrative_series.py new file mode 100644 index 00000000..3e33865b --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_narrative_series.py @@ -0,0 +1,143 @@ +"""Run the narrative-score pipeline across the full h41_pdfs/ corpus. + +Produces 6 parquet files under data/h41_rag/ (median + max per feature): + CreditStressNarrativeScore_median.parquet / _max.parquet + LiquidityNarrativeScore_median.parquet / _max.parquet + InterventionNarrativeScore_median.parquet / _max.parquet + +Pipeline (see narrative_scoring.py and the feature_docs/*.md files): + 1. Extract narrative chunks from every filing (narrative_extraction.py). + 2. Build ONE BM25 index and ONE set of E5 chunk embeddings, shared across all + three features -- only the query differs per feature. + 3. Per feature: raw keyword_freq + bm25_score + embedding_sim per chunk. + 4. Freeze calibration min/max per component across the whole corpus. + 5. Normalize, clip to [0,1], combine (equal weights, magnitude-only). + 6. Aggregate to one row per release: median and max across that release's chunks. +""" + +from __future__ import annotations + +import os + +# The E5 model is already cached locally (from 00_RagPrototype.ipynb's earlier +# work) -- force offline mode so sentence-transformers never attempts a network +# HEAD request, which fails behind this machine's corporate TLS-intercepting +# proxy (same root cause as the uv/--native-tls issue documented elsewhere). +os.environ.setdefault("HF_HUB_OFFLINE", "1") + +import sys +from datetime import timedelta +from pathlib import Path + +import numpy as np +import pandas as pd + +sys.path.insert(0, str(Path(__file__).resolve().parents[2])) + +from baa10y_forecasting.rag.lexicons import NARRATIVE_FEATURES # noqa: E402 +from baa10y_forecasting.rag.narrative_extraction import extract_narrative_chunks # noqa: E402 +from baa10y_forecasting.rag.narrative_scoring import ( # noqa: E402 + aggregate_median_max, + bm25_scores_for_query, + build_bm25_index, + combine_magnitude_only, + cosine_similarity_matrix, + keyword_freq, + normalize_and_clip, + tokenize, +) +from baa10y_forecasting.rag.paths import h41_pdfs_dir, h41_rag_output_dir # noqa: E402 + +EMBEDDING_MODEL_NAME = "intfloat/e5-base" + + +def _extract_all_chunks() -> pd.DataFrame: + pdf_paths = sorted(h41_pdfs_dir().glob("h41_*.pdf")) + print(f"Extracting narrative chunks from {len(pdf_paths)} filings...") + rows = [] + for i, pdf_path in enumerate(pdf_paths): + try: + for chunk in extract_narrative_chunks(pdf_path): + rows.append({"release_date": chunk.release_date, "page": chunk.page, "text": chunk.text}) + except Exception as exc: # noqa: BLE001 + print(f" WARNING: {pdf_path.name} failed: {type(exc).__name__}: {exc}") + if (i + 1) % 200 == 0: + print(f" processed {i + 1}/{len(pdf_paths)}...") + df = pd.DataFrame(rows).reset_index(drop=True) + print(f"Extracted {len(df)} narrative chunks from {df['release_date'].nunique()} filings.") + return df + + +def _embed_texts(texts: list[str], is_query: bool) -> np.ndarray: + from sentence_transformers import SentenceTransformer # noqa: PLC0415 -- heavy import, load once here + + model = _embed_texts._model # type: ignore[attr-defined] + prefix = "query: " if is_query else "passage: " + return model.encode([prefix + t for t in texts], show_progress_bar=True, batch_size=64) + + +def build_all() -> None: + chunks_df = _extract_all_chunks() + chunk_cache_path = h41_rag_output_dir() / "_narrative_chunks_cache.parquet" + chunks_df.to_parquet(chunk_cache_path, index=False) + print(f"Cached raw chunks to {chunk_cache_path}") + + chunk_texts = chunks_df["text"].tolist() + + print("Building shared BM25 index...") + bm25_index = build_bm25_index(chunk_texts) + + print(f"Loading embedding model ({EMBEDDING_MODEL_NAME}) and embedding all chunks (shared, once)...") + from sentence_transformers import SentenceTransformer # noqa: PLC0415 + + model = SentenceTransformer(EMBEDDING_MODEL_NAME) + _embed_texts._model = model # type: ignore[attr-defined] + chunk_embeddings = np.asarray( + model.encode(["passage: " + t for t in chunk_texts], show_progress_bar=True, batch_size=64) + ) + print(f"Chunk embeddings shape: {chunk_embeddings.shape}") + + out_dir = h41_rag_output_dir() + + for feature_name, cfg in NARRATIVE_FEATURES.items(): + print(f"\n=== {feature_name} ===") + keywords = cfg["keywords"] + bm25_query = cfg["bm25_query"] + embed_query = cfg["embed_query"] + + raw_freq = np.asarray([keyword_freq(t, keywords) for t in chunk_texts]) + raw_bm25 = bm25_scores_for_query(bm25_index, bm25_query) + query_embedding = np.asarray(model.encode(["query: " + embed_query]))[0] + raw_sim = cosine_similarity_matrix(chunk_embeddings, query_embedding) + + print( + f" raw ranges: freq [{raw_freq.min():.4f}, {raw_freq.max():.4f}] " + f"bm25 [{raw_bm25.min():.4f}, {raw_bm25.max():.4f}] " + f"sim [{raw_sim.min():.4f}, {raw_sim.max():.4f}]" + ) + + norm_freq = normalize_and_clip(raw_freq, raw_freq.min(), raw_freq.max()) + norm_bm25 = normalize_and_clip(raw_bm25, raw_bm25.min(), raw_bm25.max()) + norm_sim = normalize_and_clip(raw_sim, raw_sim.min(), raw_sim.max()) + + score = combine_magnitude_only(norm_freq, norm_bm25, norm_sim) + + chunk_scores = pd.DataFrame({"release_date": chunks_df["release_date"], "score": score}) + aggregated = aggregate_median_max(chunk_scores) + aggregated["timestamp"] = pd.to_datetime(aggregated["release_date"]) - timedelta(days=1) + + median_out = aggregated[["timestamp", "median"]].rename(columns={"median": "value"}) + max_out = aggregated[["timestamp", "max"]].rename(columns={"max": "value"}) + + median_out.sort_values("timestamp").to_parquet(out_dir / f"{feature_name}_median.parquet", index=False) + max_out.sort_values("timestamp").to_parquet(out_dir / f"{feature_name}_max.parquet", index=False) + + print( + f" wrote {feature_name}_median.parquet / _max.parquet: {len(aggregated)} rows, " + f"median range [{aggregated['median'].min():.4f}, {aggregated['median'].max():.4f}], " + f"max range [{aggregated['max'].min():.4f}, {aggregated['max'].max():.4f}]" + ) + + +if __name__ == "__main__": + build_all() diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_structured_series.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_structured_series.py new file mode 100644 index 00000000..05057cfb --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/build_structured_series.py @@ -0,0 +1,122 @@ +"""Run structured_extraction.py across the full h41_pdfs/ corpus and write parquet. + +Produces, under data/h41_rag/: + - ReserveBalancesLevel.parquet + - SecuritiesHeldOutrightLevel.parquet (QTIntensity's raw input, kept for audit) + - QTIntensity.parquet (-diff of the above, per QTIntensity.md) + - FacilityStress.parquet + +Each parquet has exactly two columns: timestamp, value. Leak-safety (released_at, +the 1-business-day feature lag, weekly-to-daily expansion) is applied downstream +in data.py, exactly like every other covariate in this project -- this script's +only job is "PDF -> raw (date, value) pairs," per the agreed division of labor. + +Timestamp convention: the "week ended" statement date, derived as (release +date - 1 day). H.4.1 filenames (h41_YYYYMMDD.pdf) record the Thursday release +date; the statement always covers the immediately preceding Wednesday -- this +arithmetic is more robust across eras than parsing "Week ended " from the +text, whose position in the header varies (confirmed: the 2000-era header +layout splits "Week ended" and the date itself across separate lines). +""" + +from __future__ import annotations + +import sys +import warnings +from datetime import timedelta + +import pandas as pd + +sys.path.insert(0, str(__import__("pathlib").Path(__file__).resolve().parents[2])) + +from baa10y_forecasting.rag.paths import h41_pdfs_dir, h41_rag_output_dir # noqa: E402 +from baa10y_forecasting.rag.structured_extraction import ( # noqa: E402 + compute_facility_stress, + get_reserve_balances_level, + get_securities_held_outright_level, + parse_h41_filing, +) + + +def build_all() -> None: + pdf_paths = sorted(h41_pdfs_dir().glob("h41_*.pdf")) + print(f"Found {len(pdf_paths)} filings.") + + rows: list[dict] = [] + failures: list[tuple[str, str]] = [] + + for i, pdf_path in enumerate(pdf_paths): + try: + filing = parse_h41_filing(pdf_path) + timestamp = pd.Timestamp(filing.release_date_from_filename) - timedelta(days=1) + rows.append( + { + "timestamp": timestamp, + "release_date": filing.release_date_from_filename, + "reserve_balances": get_reserve_balances_level(filing), + "securities_held_outright": get_securities_held_outright_level(filing), + "facility_stress": compute_facility_stress(filing), + } + ) + except Exception as exc: # noqa: BLE001 -- one bad filing must not kill the run + failures.append((pdf_path.name, f"{type(exc).__name__}: {exc}")) + if (i + 1) % 200 == 0: + print(f" processed {i + 1}/{len(pdf_paths)}...") + + print(f"Parsed {len(rows)} filings successfully, {len(failures)} failed.") + if failures: + print("Failures:") + for name, msg in failures: + print(f" {name}: {msg}") + + df = pd.DataFrame(rows).sort_values("timestamp").reset_index(drop=True) + + missing_rb = df["reserve_balances"].isna().sum() + missing_shr = df["securities_held_outright"].isna().sum() + missing_fs = df["facility_stress"].isna().sum() + if missing_rb or missing_shr or missing_fs: + warnings.warn( + f"{missing_rb} filings missing reserve_balances, {missing_shr} missing " + f"securities_held_outright, {missing_fs} missing facility_stress " + "-- inspect before trusting the parquet output.", + stacklevel=2, + ) + + out_dir = h41_rag_output_dir() + + reserve_balances = df.dropna(subset=["reserve_balances"])[["timestamp", "reserve_balances"]].rename( + columns={"reserve_balances": "value"} + ) + reserve_balances.to_parquet(out_dir / "ReserveBalancesLevel.parquet", index=False) + + shr = df.dropna(subset=["securities_held_outright"])[["timestamp", "securities_held_outright"]].rename( + columns={"securities_held_outright": "value"} + ) + shr.to_parquet(out_dir / "SecuritiesHeldOutrightLevel.parquet", index=False) + + # QTIntensity = -diff(SecuritiesHeldOutright), per QTIntensity.md. + qt = shr.sort_values("timestamp").reset_index(drop=True).copy() + qt["value"] = -qt["value"].diff() + qt = qt.dropna(subset=["value"]).reset_index(drop=True) + qt.to_parquet(out_dir / "QTIntensity.parquet", index=False) + + facility_stress = df.dropna(subset=["facility_stress"])[["timestamp", "facility_stress"]].rename( + columns={"facility_stress": "value"} + ) + facility_stress.to_parquet(out_dir / "FacilityStress.parquet", index=False) + + print(f"\nWrote parquet files to {out_dir}:") + for name, frame in [ + ("ReserveBalancesLevel", reserve_balances), + ("SecuritiesHeldOutrightLevel", shr), + ("QTIntensity", qt), + ("FacilityStress", facility_stress), + ]: + print( + f" {name}.parquet: {len(frame)} rows, " + f"{frame['timestamp'].min().date()} to {frame['timestamp'].max().date()}" + ) + + +if __name__ == "__main__": + build_all() diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/lexicons.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/lexicons.py new file mode 100644 index 00000000..860717c7 --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/lexicons.py @@ -0,0 +1,78 @@ +"""Keyword lexicons and queries for the three narrative-score features. + +See RAGtools/feature_docs/{CreditStressNarrativeScore,LiquidityNarrativeScore, +InterventionNarrativeScore}.md for the design rationale. Values here must match +those docs exactly -- this module is the implementation of what's written there, +not an independent design. +""" + +from __future__ import annotations + +CREDIT_STRESS_KEYWORDS: list[str] = [ + "credit tightening", + "tightening credit conditions", + "credit availability", + "funding pressures", + "credit deterioration", + "credit strains", + "credit markets", + "credit support", + "credit disruptions", + "credit facilities", + "credit risk", + "credit stress", + "credit contraction", +] +CREDIT_STRESS_BM25_QUERY = "credit stress, tightening credit conditions, funding pressures" +CREDIT_STRESS_EMBED_QUERY = "credit stress in financial markets" + +LIQUIDITY_KEYWORDS: list[str] = [ + "liquidity in short-term funding markets", + "money market pressures", + "funding markets", + "repo market", + "reserve scarcity", + "market functioning", + "liquidity facility", + "liquidity support", + "liquidity injections", + "term funding", + "commercial paper market", + "short-term funding", +] +LIQUIDITY_QUERY = "liquidity conditions and funding market stress" + +INTERVENTION_KEYWORDS: list[str] = [ + "the federal reserve announced", + "extended credit to", + "under the authority of section 13(3)", + "the committee directs the desk", + "was formed to", + "began purchasing", + "began extending loans", + "facility established", + "unusual and exigent circumstances", + "with approval of the treasury secretary", + "emergency lending", + "in response to", +] +INTERVENTION_QUERY = "Federal Reserve announces a new emergency lending facility" + + +NARRATIVE_FEATURES: dict[str, dict] = { + "CreditStressNarrativeScore": { + "keywords": CREDIT_STRESS_KEYWORDS, + "bm25_query": CREDIT_STRESS_BM25_QUERY, + "embed_query": CREDIT_STRESS_EMBED_QUERY, + }, + "LiquidityNarrativeScore": { + "keywords": LIQUIDITY_KEYWORDS, + "bm25_query": LIQUIDITY_QUERY, + "embed_query": LIQUIDITY_QUERY, + }, + "InterventionNarrativeScore": { + "keywords": INTERVENTION_KEYWORDS, + "bm25_query": INTERVENTION_QUERY, + "embed_query": INTERVENTION_QUERY, + }, +} diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_extraction.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_extraction.py new file mode 100644 index 00000000..d00db1f7 --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_extraction.py @@ -0,0 +1,87 @@ +"""Narrative/footnote text extraction from H.4.1 filings -- the complement of +structured_extraction.py's table-line parsing. + +Approach: a real Table 1 data row always contains at least one comma-grouped +figure (e.g. "490,027") -- confirmed across every era read (2000-2025), no +narrative sentence in these filings writes a number with a thousands-separator +comma (dates like "March 17, 2020" have a comma, but not *within* a number). +So rather than positively classifying block types (headers/tables/footnotes, +per chunking_design.md's original idea -- more robust in principle but needs +per-era layout tuning we don't have time to do exhaustively), narrative text +is extracted by *exclusion*: keep every line that does NOT contain a +comma-grouped number, drop short boilerplate/header lines, and group what's +left into paragraph-like chunks. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from pathlib import Path + +import pdfplumber + +from baa10y_forecasting.rag.structured_extraction import _release_date_from_filename + +_COMMA_NUMBER = re.compile(r"\d{1,3}(?:,\d{3})+") +# Small-value table rows (e.g. "Repurchase agreements6 32 + 30 + 32 0") have no +# comma-grouped figure but use the signed change-column notation ("+ 30", "- 174") +# -- confirmed leaking into "narrative" chunks without this check. Real prose in +# these filings never writes a bare sign directly against a number this way. +_SIGNED_NUMBER = re.compile(r"[+\-]\s*\d+") +_MIN_LINE_LEN = 30 # drops page headers, "H.4.1", standalone page numbers, etc. + + +@dataclass +class NarrativeChunk: + release_date: str # YYYY-MM-DD, from filename + page: int + text: str + + +def _is_narrative_line(line: str) -> bool: + line = line.strip() + if len(line) < _MIN_LINE_LEN: + return False + if _COMMA_NUMBER.search(line) or _SIGNED_NUMBER.search(line): + return False + return True + + +def extract_narrative_chunks(pdf_path: Path, max_chars: int = 800) -> list[NarrativeChunk]: + """Group consecutive narrative-candidate lines per page into chunks. + + A run of narrative lines ends when a table-row line (or a too-short/ + boilerplate line) breaks it, or when the accumulated text would exceed + ``max_chars`` -- these filings' narrative sections are short (footnotes, + a paragraph or two of announcement text), so a simple length cap without + the oversized-block fallback logic ``00_RagPrototype.ipynb`` needed for + longer documents is sufficient here. + """ + release_date = _release_date_from_filename(pdf_path) + chunks: list[NarrativeChunk] = [] + + with pdfplumber.open(pdf_path) as pdf: + for page_num, page in enumerate(pdf.pages, start=1): + text = page.extract_text() or "" + buffer: list[str] = [] + buffer_len = 0 + + def flush() -> None: + nonlocal buffer, buffer_len + if buffer: + chunks.append(NarrativeChunk(release_date, page_num, " ".join(buffer))) + buffer, buffer_len = [], 0 + + for raw_line in text.splitlines(): + line = raw_line.strip() + if _is_narrative_line(line): + if buffer_len + len(line) > max_chars: + flush() + buffer.append(line) + buffer_len += len(line) + 1 + else: + flush() + flush() + + return chunks diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_scoring.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_scoring.py new file mode 100644 index 00000000..97cbb39a --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/narrative_scoring.py @@ -0,0 +1,78 @@ +"""Per-chunk scoring pipeline for the narrative-score features. + +Implements RAGtools/feature_docs/CreditStressNarrativeScore.md's pipeline: +keyword_freq + bm25_score + embedding_sim per chunk, min-max normalized against +frozen corpus-wide calibration stats, clipped to [0,1], combined via equal +weights (magnitude-only), aggregated per release to median + max. +""" + +from __future__ import annotations + +import re + +import numpy as np +import pandas as pd +from rank_bm25 import BM25Okapi + +_TOKEN_RE = re.compile(r"[a-z0-9]+") + + +def tokenize(text: str) -> list[str]: + return _TOKEN_RE.findall(text.lower()) + + +def keyword_freq(text: str, keywords: list[str]) -> float: + """(# keyword-phrase hits) / (total tokens), case-insensitive substring count.""" + tokens = tokenize(text) + if not tokens: + return 0.0 + text_lower = text.lower() + hits = sum(text_lower.count(kw.lower()) for kw in keywords) + return hits / len(tokens) + + +def build_bm25_index(chunk_texts: list[str]) -> BM25Okapi: + return BM25Okapi([tokenize(t) for t in chunk_texts]) + + +def bm25_scores_for_query(index: BM25Okapi, query: str) -> np.ndarray: + return np.asarray(index.get_scores(tokenize(query))) + + +def cosine_similarity_matrix(chunk_embeddings: np.ndarray, query_embedding: np.ndarray) -> np.ndarray: + chunk_norms = np.linalg.norm(chunk_embeddings, axis=1) + query_norm = np.linalg.norm(query_embedding) + dot = chunk_embeddings @ query_embedding + denom = chunk_norms * query_norm + denom[denom == 0] = 1e-10 + return dot / denom + + +def normalize_and_clip(raw: np.ndarray, calib_min: float, calib_max: float) -> np.ndarray: + """Min-max normalize against frozen calibration bounds, then clip to [0,1]. + + See CreditStressNarrativeScore.md's Normalization section: values outside + the calibration snapshot's observed range are expected as the corpus grows + and must be clipped, not left to blow the combined score's [0,1] bound. + """ + span = calib_max - calib_min + if span == 0: + return np.zeros_like(raw) + norm = (raw - calib_min) / span + return np.clip(norm, 0.0, 1.0) + + +def combine_magnitude_only( + norm_freq: np.ndarray, + norm_bm25: np.ndarray, + norm_sim: np.ndarray, + weights: tuple[float, float, float] = (1 / 3, 1 / 3, 1 / 3), +) -> np.ndarray: + w1, w2, w3 = weights + return w1 * norm_freq + w2 * norm_bm25 + w3 * norm_sim + + +def aggregate_median_max(chunk_scores: pd.DataFrame) -> pd.DataFrame: + """chunk_scores: columns [release_date, score]. Returns one row per release_date.""" + grouped = chunk_scores.groupby("release_date")["score"] + return grouped.agg(median="median", max="max").reset_index() diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/paths.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/paths.py new file mode 100644 index 00000000..3cde8d6e --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/paths.py @@ -0,0 +1,28 @@ +"""Repo-root-relative path resolution, mirroring ``data.py``'s ``_repo_root`` convention.""" + +from __future__ import annotations + +from pathlib import Path + + +def repo_root() -> Path: + """Walk up from this file looking for the sibling ``aieng-forecasting`` dir. + + Same approach as ``BAA10Y_forecasting.data._repo_root`` -- kept independent + (not imported from there) so this package has no dependency on ``data.py``. + """ + here = Path(__file__).resolve() + for p in (here, *here.parents): + if (p / "aieng-forecasting").is_dir(): + return p + raise RuntimeError("Could not locate repo root (no ancestor with an 'aieng-forecasting' dir).") + + +def h41_pdfs_dir() -> Path: + return repo_root() / "h41_pdfs" + + +def h41_rag_output_dir() -> Path: + out = repo_root() / "data" / "h41_rag" + out.mkdir(parents=True, exist_ok=True) + return out diff --git a/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/structured_extraction.py b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/structured_extraction.py new file mode 100644 index 00000000..4464e2c1 --- /dev/null +++ b/implementations/BAA10Y_forecasting/src/baa10y_forecasting/rag/structured_extraction.py @@ -0,0 +1,292 @@ +"""Deterministic table-line extraction from H.4.1 filings -- no RAG/embeddings involved. + +Implements the definitions and era findings documented in ``RAGtools/feature_docs/``: +``ReserveBalancesLevel.md``, ``QTIntensity.md`` (the underlying "Securities held +outright" level; the week-over-week diff is applied downstream, not here), and +``FacilityStress.md`` (the exclusion-based leaf sum). + +Table 1 ("Factors Affecting Reserve Balances") is plain, digitally-generated text +in every filing read so far (2000-2025) -- one table row per text line, label +followed by the "week ended" / current-level column as the *first* number, with +change columns and the Wednesday level following it. No OCR, no ruling-line-based +table detection needed; a line-oriented regex is more robust here than pdfplumber's +table-finder given how much the table's visual layout has changed across eras. +""" + +from __future__ import annotations + +import re +from dataclasses import dataclass +from pathlib import Path + +import pdfplumber + + +_NUMBER = r"-?[\d,]+(?:\.\d+)?" +# Footnote markers appear two ways across eras: bare trailing digits +# ("outright1"), or parenthesized, sometimes multi-digit ("Banks (6)", +# "account (2,3)" -- confirmed in the 2000-era filings). Both must be +# consumed before the value, or the match fails outright on 2000-era text. +_LINE_RE = re.compile( + r"^(?P