Projects

Python Equity Research Pipeline

A self-built quantitative equity research pipeline in Python integrating LSTM-based return forecasting, mean-variance portfolio optimization, and systematic backtesting. The pipeline ingests historical price and fundamental data, generates forward return estimates via a trained LSTM model, and constructs an optimized portfolio allocation using mean-variance optimization with configurable constraints. Performance is evaluated through systematic backtesting against the Nasdaq-100 (QQQ) as a benchmark, with metrics including Sharpe ratio, maximum drawdown, and annualized alpha.

The project reflects the intersection of my CS background and finance interest, translating academic concepts from modern portfolio theory into a functional, end-to-end research tool. It was also the practical foundation for the quantitative framing used in the Alphabet equity research brief, specifically the beta and correlation analysis underpinning the Nasdaq-100 proxy thesis.