Quant Portfolio Optimizer

This project is a quantitative portfolio analysis tool built around Modern Portfolio Theory to determine optimal asset allocation from historical market data. It combines constrained optimization, live financial data, and interactive visualization to help users explore the tradeoff between risk and return, with a particular focus on maximizing Sharpe Ratio and comparing results against market benchmarks. The final product turns textbook portfolio theory into an accessible, usable web application.

A quantitative finance dashboard that computes optimal portfolio weights, visualizes the efficient frontier, and backtests performance against the S&P 500.

Highlights

• —Implemented a Modern Portfolio Theory optimization pipeline using constrained SLSQP optimization to maximize Sharpe Ratio
• —Built an interactive Streamlit app for real-time portfolio analysis using user-defined ticker sets and historical market data
• —Visualized the efficient frontier through Monte Carlo portfolio simulations to make risk-return tradeoffs interpretable
• —Backtested the optimized allocation against the S&P 500 and reported benchmark outperformance over the evaluation period
• —Produced a portfolio configuration with 39.23% expected annual return, 33.08% annual volatility, and a Sharpe Ratio of 1.13 in the documented example