aengusmartindonaire/systematic-equity-alpha

This project is a classic 'Quant 101' machine learning pipeline. While the claimed cumulative alpha is high, the Sharpe ratio of 0.38 is extremely low by institutional standards, suggesting high volatility or drawdowns that would make it difficult to trade in a professional context. From a competitive standpoint, the repository has zero stars, forks, or community traction, indicating it is likely a personal research project or a portfolio piece. The techniques used (XGBoost, Bayesian Ridge, SHAP) are industry-standard commodity methods. There is no technical moat or proprietary data included in the repo (as Bloomberg data requires a paid terminal). It competes with established libraries like Hudson & Thames' MlFinLab or QuantConnect, which offer much more robust infrastructure. Frontier labs pose low risk because they generally avoid the regulatory and capital-intensive nature of proprietary trading, but the project itself is easily displaced by any modern transformer-based or GNN-based quantitative approach.