GitHub - ratemyguy/ml-alpha-strategy: ML-driven alpha signal generation for U.S. equities. Ridge, Random Forest, and XGBoost trained with walk-forward validation. XGBoost achieves Sharpe 1.07 out-of-sample. Full feature importance and IC stability analysis. · GitHub

Name		Name	Last commit message	Last commit date
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0.1_README.md		0.1_README.md
0.2_prices_daily.csv		0.2_prices_daily.csv
0.3_prices_monthly.csv		0.3_prices_monthly.csv
1.1_ml_layer1_features.py		1.1_ml_layer1_features.py
1.2_ml_features.csv		1.2_ml_features.csv
1.3_ml_features.png		1.3_ml_features.png
2.1_ml_layer2_fix.py		2.1_ml_layer2_fix.py
2.2_predictions_random_forest.csv		2.2_predictions_random_forest.csv
2.3_predictions_ridge_regression.csv		2.3_predictions_ridge_regression.csv
2.4_predictions_xgboost.csv		2.4_predictions_xgboost.csv
2.5_ml_model_results.png		2.5_ml_model_results.png
3.1_ml_layer3_importance.py		3.1_ml_layer3_importance.py
3.2_ml_feature_importance.png		3.2_ml_feature_importance.png

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ML-driven alpha signal generation for U.S. equities. Ridge, Random Forest, and XGBoost trained with walk-forward validation. XGBoost achieves Sharpe 1.07 out-of-sample. Full feature importance and IC stability analysis.

python machine-learning scikit-learn xgboost quantitative-finance backtesting alpha-signals

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