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Edge strategy reviewer

Skill xonevn-ai/xone-trading-skills/skills/edge-strategy-reviewer

Bộ "kỹ năng" (Claude Skills) hỗ trợ workflow giao dịch cho nhà đầu tư cá nhân Việt Nam có quỹ thời gian hạn chế. Mục tiêu là chuẩn hoá quy trình review thị trường, quản trị rủi ro, lập kế hoạch giao dịch, ghi nhật ký và cải tiến liên tục — không thay thế phán đoán của bạn, không đưa ra tín hiệu mua/bán.

Install

npx -y skills add xonevn-ai/xone-trading-skills --skill edge-strategy-reviewer

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One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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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.

SKILL.md

3.5 KB, as published. Nobody here has run it

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

Load draft YAML files from --drafts-dir or a single --draft file
Evaluate each draft against 8 criteria (C1-C8) with weighted scoring
Compute confidence score (weighted average of all criteria)
Determine verdict: PASS / REVISE / REJECT
Assess export eligibility (PASS + export_ready_v1 + exportable family)
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

Keep looking

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