Trading Model Post-Training Workflow

About

This skill provides a comprehensive, verified workflow for managing the lifecycle of trading models after GPU-based training. It guides users through extracting training archives, applying specific fitness thresholds like Profit Factor and Max Drawdown, and performing rigorous walk-forward validation to ensure model consistency across different market regimes. It is specifically designed to bridge the gap between raw model output and production-ready deployment on platforms like Alpaca, providing clear decision matrices for live trading, paper trading, or retraining.

Key Features

• Automated gating assessment using predefined fitness and profit thresholds
• Clear distinction between training proxy metrics and real equity drawdown
• Standardized deployment decision matrix for risk management
• Multi-fold walk-forward validation to detect overfitting and regime sensitivity
• Seamless integration with Alpaca API for high-fidelity backtesting
• 0 GitHub stars

Use Cases

• Evaluating RL model fitness before moving from Colab training to deployment
• Standardizing the validation pipeline for crypto and stock trading algorithms
• Identifying inconsistent models that perform well only in specific market conditions