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Strategy pivot designer

Skill BaggaT236/AI-Trading-Skills/skills/strategy-pivot-designer

Ai agent claude trading skills for disiplined, repeatable trading workflows with a modern typescript platform

Install
npx -y skills add BaggaT236/AI-Trading-Skills --skill strategy-pivot-designer

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What its author says it does

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

SKILL.md

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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, tail risk).
  3. If stagnation detected, 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):

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:

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

Generate pivot proposals:

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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