Edge Strategy Designer

by tradermontyeab8d5cb97b9No license2.9K starsListed Oct 8, 2026Updated Oct 8, 2026Repository updated 3 days ago

Convert abstract edge concepts into strategy draft variants and optional exportable ticket YAMLs for edge-candidate-agent export/validation.

Includes scriptsBusiness & Finance
AI-generated overview

Turns edge concept hypotheses into strategy draft YAML variants with calibrated exits and optional exportable tickets.

What it does
Reads an edge_concepts.yaml file and generates per-concept strategy draft specs as YAML, producing multiple variants such as core, conservative and research-probe. It applies hypothesis-type exit overrides for stop-loss, reward-to-risk, time-stop and trailing-stop, clamps reward-to-risk at a floor of 1.5, and writes a run manifest. It can also emit exportable ticket YAMLs for downstream export validation.
When to use it
Use it after concept synthesis when you already have edge_concepts.yaml and need concrete strategy candidates. It fits workflows that want several risk-profile variants per concept or exportable tickets for interface v1 families.
Requirements
Python 3.9+ and PyYAML, plus an edge_concepts.yaml produced by concept synthesis. It ships executable scripts and writes output to a chosen directory; downstream export uses a separate edge-candidate-agent script.

Edge Strategy Designer

Overview

Translate concept-level hypotheses into concrete strategy draft specs. This skill sits after concept synthesis and before pipeline export validation.

When to Use

  • You have edge_concepts.yaml and need strategy candidates.
  • You want multiple variants (core/conservative/research-probe) per concept.
  • You want optional exportable ticket files for interface v1 families.

Prerequisites

  • Python 3.9+
  • PyYAML
  • edge_concepts.yaml produced by concept synthesis

Output

  • strategy_drafts/*.yaml
  • strategy_drafts/run_manifest.json
  • Optional exportable_tickets/*.yaml for downstream export_candidate.py

Workflow

  1. Load edge_concepts.yaml.
  2. Choose risk profile (conservative, balanced, aggressive).
  3. Generate per-concept variants with hypothesis-type exit calibration.
  4. Apply HYPOTHESIS_EXIT_OVERRIDES to adjust stop-loss, reward-to-risk, time-stop, and trailing-stop per hypothesis type (breakout, earnings_drift, panic_reversal, etc.).
  5. Clamp reward-to-risk at RR_FLOOR=1.5 to prevent C5 review failures.
  6. Export v1-ready ticket YAML when applicable.
  7. Hand off exportable tickets to skills/edge-candidate-agent/scripts/export_candidate.py.

Quick Commands

Generate drafts only:

bash
python3 skills/edge-strategy-designer/scripts/design_strategy_drafts.py \  --concepts /tmp/edge-concepts/edge_concepts.yaml \  --output-dir /tmp/strategy-drafts \  --risk-profile balanced

Generate drafts + exportable tickets:

bash
python3 skills/edge-strategy-designer/scripts/design_strategy_drafts.py \  --concepts /tmp/edge-concepts/edge_concepts.yaml \  --output-dir /tmp/strategy-drafts \  --exportable-tickets-dir /tmp/exportable-tickets \  --risk-profile conservative

Resources

  • skills/edge-strategy-designer/scripts/design_strategy_drafts.py
  • references/strategy_draft_schema.md
  • skills/edge-candidate-agent/scripts/export_candidate.py

Source and attribution

Source:tradermonty/claude-trading-skillsinskills/edge-strategy-designerat commiteab8d5c

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

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