Systematic multi-factor equity strategy using momentum, liquidity and volatility signals with reproducible backtesting and Fama–French validation.

This is a project which uses Data Science, Machine learning to predict the stock movements, minimize the risk and maximise gains of portfolio using fama-french factors and many other models.Also the sentiment towards stocks are also monitored using sentiment analysis. Garch Model is used to predict the volatility and movements for intraday trading.

MSc thesis on hedge fund portfolio optimization using semi-parametric risk modeling, EVT tails, and copula dependence.

This project was carried out as the final assignment for the Mathematical Optimization for Data Science course. The goal of the analysis was to compare two variants of the Frank-Wolfe Method with the Projected Gradient Method on the Markowitz portfolio optimization problem.

This is the repository of Machine Learning Internship I have done with CareerLauncher endorsed by AICTE and Intel Partnership

Transaction-cost-aware portfolio optimization using the Gârleanu–Pedersen framework, VAR-based signal dynamics, and PPO reinforcement learning on factor-driven portfolios.

A quantitative approach to equity portfolio optimization. Features fundamental screening, OLS regression (SIM) with robust standard errors, and historical VaR/ES assessment

📊Machine-learning decision system for Indian IPOs: probability model + backtested allocation strategy + FastAPI service + Streamlit dashboard.

Quantitative investment framework combining Fama-French 5-factor analysis with XGBoost Learning-to-Rank. Implements a rolling-window strategy (Sharpe 0.95 vs SPY 0.85) to forecast excess returns and optimize portfolios. Includes Production FastAPI inference.

🚀 A dual-optimization system that automates personal budgeting and investment allocation using linear and quadratic programming to maximize long-term wealth accumulation.