S&P 500 Portfolio Optimization via K-Means Clustering

Sat, 01 Mar 2025 00:00:00 +0000

A systematic, data-driven trading strategy that applies unsupervised machine learning to a decade of S&P 500 equities. Technical indicators (RSI, ATR, MACD, Bollinger Bands, Garman-Klass volatility) and rolling Fama-French 5-factor betas are engineered as features, then K-Means clustering with customized centroid initialization groups stocks each month. Portfolios are built from the selected cluster via maximum Sharpe ratio optimization, and cumulative returns are backtested against the SPY benchmark.

K-Means Clustering | Saeed Mohseni-Sehdeh

S&P 500 Portfolio Optimization via K-Means Clustering