Strategy Pivot Designer
SkillCommerce & financeDetect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Strategy Pivot Designer skill
What this skill tells your AI
The instructions your AI receives, as published by baggat236/ai-trading-skills in skills/strategy-pivot-designer/SKILL.md and read by ahel’s review.
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
Signals
- GitHub stars
- 122
- Forks
- 960
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
strategy-pivot-designer- Source
- github.com/baggat236/ai-trading-skills