ML Alpha

objective

Develop machine-learning alpha models with robust validation, calibration, and deployment controls.

workflow

1. define target construction and purged train-validation splits.
2. engineer predictive features with leakage and survivorship safeguards.
3. train and calibrate models with benchmark challengers.
4. evaluate alpha net of costs under realistic execution assumptions.
5. deploy only when live monitoring and drift controls are configured.

required diagnostics

• precision, calibration, and information-coefficient stability.
• feature drift and target drift diagnostics.
• out-of-sample decay and horizon-specific performance.
• cost-adjusted edge versus simple baseline models.
• live-versus-backtest discrepancy monitoring.