portfolio-optimizations

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.

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

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

Mean-variance portfolio optimization in Python, testing whether Ledoit-Wolf covariance shrinkage improves out-of-sample results

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

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

Modern portfolio optimization using constrained Kelly, HRP, K-Fold cross-validation, and Marčenko-Pastur denoising to improve risk-adjusted returns.

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.

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

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

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.

Multi-asset Value-at-Risk (VaR) engine for bonds, stocks, FX, and European derivatives. Three VaR methodologies (Parametric, Historical, Monte Carlo) with four optimization strategies and Kupiec backtesting. Use as Python package or interactive Streamlit dashboard for portfolio risk analysis.