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
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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 proposalspivot_drafts/exportable/*.yaml— export-ready drafts + ticket YAML for candidate-agentpivot_report_*.md— human-readable pivot analysispivot_manifest_*.json— metadata for all generated filespivot_diagnosis_*.json— stagnation detection results
Workflow
- Accumulate backtest evaluation results into an iteration history file using
--append-eval. - Run stagnation detection on the history to identify triggers (plateau, overfitting, cost defeat, tail risk).
- If stagnation detected, generate pivot proposals using three techniques: assumption inversion, archetype switch, objective reframe.
- Review ranked proposals (scored by quality potential + novelty).
- For exportable proposals, ticket YAML is ready for edge-candidate-agent pipeline.
- For research_only proposals, manual strategy design needed before pipeline integration.
- 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.pyskills/strategy-pivot-designer/scripts/generate_pivots.pyreferences/stagnation_triggers.mdreferences/strategy_archetypes.mdreferences/pivot_techniques.mdreferences/pivot_proposal_schema.mdskills/backtest-expert/scripts/evaluate_backtest.pyskills/edge-strategy-designer/scripts/design_strategy_drafts.py