paper-learning-validation

What problem does it solve?

This Skill solves the critical gap of unproven causality in paper trading learning systems, where parameter updates may not drive intended behavioral changes, and global performance metrics hide segment-specific weaknesses that can lead to failed live trading deployments.

Core Features & Use Cases

• Causality Validation: Confirms that learning parameter updates directly cause expected changes in trade admission and exit patterns.
• Segment Performance Analysis: Breaks down key metrics like win rate and profit factor by trading bucket, market regime, and symbol to avoid misleading global averages.
• Clear Pass/Fail Gates: Provides explicit criteria to label learning updates as successful, cautionary, or failed based on pre/post comparison of trade behavior and performance.
• Use Case: A crypto trading bot operator can use this Skill to verify that tightening the economic threshold parameter actually reduces unwanted starvation bypasses in bear market regimes, rather than seeing no change in the overall win rate.

Quick Start

Use the paper-learning-validation skill to confirm that your latest learning parameter update produced the expected reduction in trade admissions for the strict take bucket during bear trends.