sachahuberty / Portfolio-Optimization-Machine-LearningView on GitHub
Cross-sectional stock selection on the S&P 500 using fundamental, technical, and macro factors. XGBoost classifies direction while meta-labeling sizes positions via triple-barrier labels. Purged cross-validation prevents lookahead bias, sector/beta neutralization controls risk, and cost-aware backtesting evaluates the resulting long-short portfo…
16Apr 28, 2026Updated 2 months ago

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