ml-meta-labeler

What problem does it solve?

This skill automates the meta-labeling layer (Layer 3) of a multi-model trading cascade, enabling selective action on quant-engine signals to improve precision and reduce false positives.

Core Features & Use Cases

• Triple-barrier labeling to generate robust binary/meta labels for training.
• XGBoost calibration and Platt scaling to produce well-calibrated trade-probabilities.
• Purged K-fold CV with embargo to prevent data leakage and maintain realistic out-of-sample evaluation.
• Threshold-based decisioning and routine retraining to adapt to changing markets.

Quick Start

Provide a history of quant signals and features, then run the meta-model training workflow to produce a calibrated classifier.