strategy-pivot-designer

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