ml-for-finance

Use time-series cross-validation with purging and embargo to prevent look-ahead bias when training supervised ML models for predicting returns in financial markets. This disciplined, time-aware evaluation ensures robust trading signals.

Purged cross-validation removes overlapping data points between training and validation sets to prevent information leakage. You need it when applying supervised ML to financial time-series problems like returns prediction and regime classification.

Use SHAP and permutation methods to analyze feature importance for model interpretability in finance ML. These techniques identify which variables drive predictions for returns and regime detection, enabling transparent and robust trading insights.

Yes, scikit-learn and XGBoost are suitable for regime classification and returns prediction. Combine them with time-aware evaluation using purged cross-validation and embargo to maintain robust, backtesting-ready performance monitoring.

Backtest ML trading signals using disciplined, time-aware evaluation with purged CV and embargo to avoid look-ahead bias. Monitor model decay and performance metrics post-deployment to ensure predictions remain robust in live financial markets.

Training success with backtesting failure usually indicates look-ahead bias from standard cross-validation. Apply purged cross-validation with embargoing to your financial time-series data to prevent leakage and achieve robust, backtesting-ready evaluation.