Critically review strategy drafts from edge-strategy-designer for edge plausibility, overfitting risk, sample size adequacy, and execution realism. Use when strategy_drafts/*.yaml exists and needs quality gate before pipeline export. Outputs PASS/REVISE/REJECT verdicts with confidence scores.

Edge Strategy Reviewer Deterministic quality gate for strategy drafts produced by edge strategy designer . When to Use After edge strategy designer generates strategy drafts/ .yaml Before exporting drafts to edge candidate agent via the pipeline When manually validating a draft strategy for edge plausibility Prerequisites Strategy draft YAML files (output of edge strategy designer ) Python 3.10+ with PyYAML Workflow 1. Load draft YAML files from drafts dir or a single draft file 2. Evaluate each draft against 8 criteria (C1 C8) with weighted scoring 3. Compute confidence score (weighted average of all criteria) 4. Determine verdict: PASS / REVISE / REJECT 5. Assess export eligibility (PASS + export ready v1 + exportable family) 6. Write review output (YAML or JSON) and optional markdown summary Review Criteria Criterion Weight Key Checks C1 Edge Plausibility 20 Thesis quality, domain terms, mechanism keywords (continuous 50 95) C2 Overfitting Risk 20 5 tier filter count scoring (90/80/60/40/10), precise threshold penalty C3 Sample Adequacy 15 Continuous scoring from estimated annual opportunities (10 95) C4 Regime Dependency 10 Cross regime validation C5 Exit Calibration 10 Stop loss, reward to risk C6 Risk Concentration 10 Position sizing limits C7 Execution Realism 10 Volume filter, export consistency C8 Invalidation Quality 5 Signal count and specificity Verdict Logic C1 or C2 severity=fail → immediate REJECT confidence = 70, no fail findings → PASS confidence < 35 → REJECT Otherwise → REVISE (with revision instructions) Running the Script Output Format Primary output: review.yaml (or review.json ) Resources references/review criteria.md — Detailed scoring rubric for C1 C8 references/overfitting checklist.md — Overfitting detection heuristics

edge-strategy-reviewer