tradermonty/claude-trading-skills

edge-strategy-reviewer

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.

All-time #6277 Trending #5493 Hot #288 First seen Mar 1, 2026
8-week activity · all time api

Installation

$ npx skills add tradermonty/claude-trading-skills --skill edge-strategy-reviewer

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Repository health

Stars 2.8K
License LICENSE
Default branch main
Open issues 31
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,553 B
  • docs SUMMARY.md 323 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2,000 installs

SKILL.md

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 + exportreadyv1 + 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

# Review all drafts in a directory
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
  --drafts-dir reports/edge_strategy_drafts/ \
  --output-dir reports/

# Single draft review
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
  --draft reports/edge_strategy_drafts/draft_xxx.yaml \
  --output-dir reports/

# JSON output with markdown summary
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
  --drafts-dir reports/edge_strategy_drafts/ \
  --output-dir reports/ \
  --format json \
  --markdown-summary

# Strict export mode: export-eligible drafts with any warn → REVISE
python3 skills/edge-strategy-reviewer/scripts/review_strategy_drafts.py \
  --drafts-dir reports/edge_strategy_drafts/ \
  --output-dir reports/ \
  --strict-export

Output Format

Primary output: review.yaml (or review.json)

generated_at_utc: "2026-02-28T12:00:00+00:00"
source:
  drafts_dir: "/path/to/strategy_drafts"
  draft_count: 4
summary:
  total: 4
  PASS: 1
  REVISE: 2
  REJECT: 1
  export_eligible: 1
reviews:
  - draft_id: "draft_xxx_core"
    verdict: "PASS"
    confidence_score: 80
    export_eligible: true
    findings: [...]
    revision_instructions: []

Resources

  • references/review_criteria.md — Detailed scoring rubric for C1-C8
  • references/overfitting_checklist.md — Overfitting detection heuristics