tradermonty/claude-trading-skills

strategy-pivot-designer

Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

소스 보기

사용 방법

  1. 설치 명령을 복사하고 자신의 에이전트 환경에 설치하기 전에 소스 패키지를 확인하세요.
  2. 필요한 도구, 인증 정보, 지원 환경은 원본 문서에서 확인하세요.
  3. 입력과 원하는 산출물을 설명하고 출처와 결과를 대조하세요.
npx skills add https://github.com/tradermonty/claude-trading-skills --skill strategy-pivot-designer

시작 전 확인

  • 설치에는 모델 구독, API 한도, 파일 접근 권한이 포함되지 않습니다.
  • 별점 수와 설치 수는 활동 지표이며 독립적인 품질·보안 검증이 아닙니다.
원본 Skill 문서

원본 저장소의 제목, 예시, 코드, 표, 링크, 이미지를 유지해 표시합니다.

Strategy Pivot Designer

Overview

Detect when a strategy's backtest iteration loop has stalled and propose structurally different strategy architectures. This skill acts as the feedback loop for the Edge pipeline (hint-extractor -> concept-synthesizer -> strategy-designer -> candidate-agent), breaking out of local optima by redesigning the strategy's skeleton rather than tweaking parameters.

When to Use

  • Backtest scores have plateaued despite multiple refinement iterations.
  • A strategy shows signs of overfitting (high in-sample, low robustness).
  • Transaction costs defeat the strategy's thin edge.
  • Tail risk or drawdown exceeds acceptable thresholds.
  • You want to explore fundamentally different strategy architectures for the same market hypothesis.

Prerequisites

  • Python 3.9+
  • PyYAML
  • Iteration history JSON (accumulated backtest-expert evaluations)
  • Source strategy draft YAML (from edge-strategy-designer)

Output

  • pivot_drafts/research_only/*.yaml — strategy_draft compatible YAML proposals
  • pivot_drafts/exportable/*.yaml — export-ready drafts + ticket YAML for candidate-agent
  • pivot_report_*.md — human-readable pivot analysis
  • pivot_manifest_*.json — metadata for all generated files
  • pivot_diagnosis_*.json — stagnation detection results

Workflow

  1. Accumulate backtest evaluation results into an iteration history file using --append-eval.
  2. Run stagnation detection on the history to identify triggers (plateau, overfitting, cost defeat, insufficient profit factor, tail risk). A no-loss profit factor is not cost defeat; an undefined or overflowed ratio requires review.
  3. If the diagnosis says review_required, repair or re-evaluate the underlying metrics first; do not generate pivots. If stagnation is confirmed, generate pivot proposals using three techniques: assumption inversion, archetype switch, objective reframe.
  4. Review ranked proposals (scored by quality potential + novelty).
  5. For exportable proposals, ticket YAML is ready for edge-candidate-agent pipeline.
  6. For research_only proposals, manual strategy design needed before pipeline integration.
  7. Feed the selected pivot draft back into backtest-expert for the next iteration cycle.

Quick Commands

Append a backtest evaluation to history (creates history if new):

bash
python3 skills/strategy-pivot-designer/scripts/detect_stagnation.py \
  --append-eval reports/backtest_eval_2026-02-10_120000.json \
  --history reports/iteration_history.json \
  --strategy-id draft_edge_concept_breakout_behavior_riskon_core \
  --changes "Widened stop_loss from 5% to 7%"

Detect stagnation:

bash
python3 skills/strategy-pivot-designer/scripts/detect_stagnation.py \
  --history reports/iteration_history.json \
  --output-dir reports/

Generate pivot proposals:

bash
python3 skills/strategy-pivot-designer/scripts/generate_pivots.py \
  --diagnosis reports/pivot_diagnosis_*.json \
  --strategy reports/edge_strategy_drafts/draft_*.yaml \
  --max-pivots 3 \
  --output-dir reports/

Resources

  • skills/strategy-pivot-designer/scripts/detect_stagnation.py
  • skills/strategy-pivot-designer/scripts/generate_pivots.py
  • references/stagnation_triggers.md
  • references/strategy_archetypes.md
  • references/pivot_techniques.md
  • references/pivot_proposal_schema.md
  • skills/backtest-expert/scripts/evaluate_backtest.py
  • skills/edge-strategy-designer/scripts/design_strategy_drafts.py

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