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strategy-pivot-designer

tradermonty/claude-trading-skills

Break strategy backtest plateaus by detecting stagnation and generating structurally different pivot proposals.

What is strategy-pivot-designer?

Detects when a trading strategy's backtest iteration has stalled despite parameter tuning, then generates fundamentally different strategy architectures to escape local optima. Use this when backtest scores plateau, overfitting emerges, or tail risk exceeds thresholds.

  • Identifies stagnation triggers: score plateaus, overfitting, transaction cost defeat, and excessive drawdown
  • Generates three types of pivot proposals: assumption inversion, archetype switch, and objective reframe
  • Ranks pivot candidates by quality potential and novelty score
  • Outputs strategy-draft compatible YAML for research and export-ready YAML with ticket metadata
  • Integrates with the Edge pipeline (hint-extractor → concept-synthesizer → strategy-designer → candidate-agent)
  • Produces diagnostic reports and manifests documenting stagnation analysis and generated pivots

How to install strategy-pivot-designer

npx skills add https://github.com/tradermonty/claude-trading-skills --skill strategy-pivot-designer
Prerequisites
  • Python 3.9 or later
  • PyYAML library
  • Iteration history JSON file (accumulated backtest-expert evaluations)
  • Source strategy draft YAML (from edge-strategy-designer)
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How to use strategy-pivot-designer

  1. 1.Accumulate backtest evaluation results into an iteration history file using the --append-eval flag with detect_stagnation.py
  2. 2.Run stagnation detection on the history to identify triggers (plateau, overfitting, cost defeat, tail risk)
  3. 3.If stagnation is detected, generate pivot proposals using generate_pivots.py with --max-pivots to control output count
  4. 4.Review ranked proposals scored by quality potential and novelty
  5. 5.For exportable proposals, use the generated ticket YAML with edge-candidate-agent; for research_only proposals, refine manually before pipeline integration
  6. 6.Feed the selected pivot draft back into backtest-expert to begin the next iteration cycle

Use cases

Good for
  • A momentum strategy's backtest plateaus at 12% annual return after 20 parameter iterations; use pivot-designer to explore mean-reversion or volatility-regime architectures instead
  • An options strategy shows 95% in-sample accuracy but 40% out-of-sample; detect overfitting and pivot to a simpler, more robust structure
  • Transaction costs consume 60% of a high-frequency strategy's edge; generate lower-frequency or batch-execution alternatives
  • A strategy's maximum drawdown exceeds 25% despite optimization; pivot to a multi-timeframe or risk-parity redesign
Who it's for
  • Quantitative traders and strategy researchers optimizing algorithmic trading systems
  • Backtesting engineers managing iterative strategy refinement pipelines
  • Trading teams seeking to escape local optima and explore new strategy architectures

strategy-pivot-designer FAQ

What triggers stagnation detection?

Score plateaus (no improvement over N iterations), overfitting (high in-sample vs. low out-of-sample), transaction costs exceeding edge, and tail risk or drawdown exceeding thresholds.

What are the three pivot techniques?

Assumption inversion (flip core market hypothesis), archetype switch (change strategy type: momentum to mean-reversion, etc.), and objective reframe (optimize for different metric: Sharpe to Sortino, etc.).

Can I use pivot proposals directly in backtesting?

Export-ready proposals include ticket YAML ready for the candidate-agent pipeline. Research-only proposals require manual strategy design refinement before pipeline integration.

How does this integrate with the Edge pipeline?

It closes the feedback loop: when backtest-expert and strategy-designer reach local optima, pivot-designer generates structurally different candidates to restart the cycle.

What output files are generated?

Strategy YAML drafts (research_only and exportable), pivot_report markdown, pivot_manifest JSON (metadata), and pivot_diagnosis JSON (stagnation detection results).

Full instructions (SKILL.md)

Source of truth, from tradermonty/claude-trading-skills.


name: strategy-pivot-designer description: Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.

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