edge-strategy-reviewer

Evaluating trading strategy overfitting risk requires assessing entry condition complexity relative to sample sizes. This skill checks overfitting risk, edge plausibility, and sample adequacy using YAML inputs to output PASS, REVISE, or REJECT verdicts with confidence scores.

Validating trading strategy edge plausibility involves checking hypothesis coherence, stop-loss calibration, and volume filters for execution realism. This review process scores strategy robustness and outputs reliability verdicts based on structured YAML definitions.

Checking market regime dependency involves evaluating whether your strategy logic adapts to different market conditions. This skill reviews YAML strategy drafts to assess regime dependency, risk concentration, and invalidation quality, returning a confidence score.

Yes, you can use YAML files to automate trading strategy evaluation. This skill parses YAML strategy definitions using PyYAML to validate execution realism, sample adequacy, and edge plausibility, generating scored review reports.

Automated trading strategy review limitations include relying on YAML input accuracy and estimated annual trading opportunities. The evaluation flags restrictive strategies with inadequate sample sizes but requires well-defined strategy parameters to generate reliable confidence scores.

Yes, edge-strategy-reviewer works directly with strategies designed by edge-strategy-designer. It validates the designer's output drafts by checking risk concentration, exit calibration, and overfitting risk to determine production deployment readiness.