edge-strategy-designer
tradermonty/claude-trading-skills
Convert trading edge concepts into strategy draft variants with optional exportable ticket YAMLs.
What is edge-strategy-designer?
Translates concept-level trading hypotheses into concrete strategy draft specifications with multiple risk-profile variants. Use this after concept synthesis to generate strategy candidates before pipeline export validation.
- Load edge concepts from YAML and generate multiple strategy variants per concept
- Apply hypothesis-type-specific exit calibration (breakout, earnings drift, panic reversal, etc.)
- Generate variants across risk profiles: conservative, balanced, and aggressive
- Clamp reward-to-risk ratios at floor threshold to prevent review failures
- Export v1-ready ticket YAML files for downstream validation
- Produce run manifest JSON tracking all generated drafts
How to install edge-strategy-designer
npx skills add https://github.com/tradermonty/claude-trading-skills --skill edge-strategy-designer- Python 3.9 or later
- PyYAML library
- edge_concepts.yaml file from concept synthesis step
How to use edge-strategy-designer
- 1.Load your edge_concepts.yaml file produced by concept synthesis
- 2.Choose a risk profile: conservative, balanced, or aggressive
- 3.Run design_strategy_drafts.py with your concepts file and desired output directory
- 4.Optionally add --exportable-tickets-dir flag to generate v1-ready ticket YAMLs
- 5.Review generated strategy_drafts/*.yaml files for hypothesis calibration and reward-to-risk ratios
- 6.Pass exportable tickets to edge-candidate-agent export_candidate.py for validation
Use cases
- Convert abstract trading edge hypotheses into testable strategy specifications
- Generate conservative and aggressive variants of the same edge concept for A/B comparison
- Prepare strategy drafts for validation by edge-candidate-agent before live export
- Create exportable ticket files that integrate with downstream pipeline tools
- Calibrate stop-loss and trailing-stop parameters by hypothesis type
- Quantitative traders building systematic edge strategies
- Trading strategy researchers prototyping multiple concept variants
- Pipeline engineers integrating concept synthesis with strategy validation
- Risk managers reviewing strategy specs before export
edge-strategy-designer FAQ
Three profiles: conservative (lower reward-to-risk, tighter stops), balanced (moderate parameters), and aggressive (higher reward-to-risk, wider stops).
It applies hypothesis-type-specific calibration for stop-loss, reward-to-risk, time-stop, and trailing-stop parameters based on the edge type (breakout, earnings_drift, panic_reversal, etc.).
Reward-to-risk ratios are clamped at a minimum of 1.5 to prevent C5 review failures and ensure adequate risk-adjusted returns.
Yes, use the --exportable-tickets-dir flag alongside --output-dir to generate both strategy drafts and v1-ready ticket YAMLs in a single run.
Hand off exportable tickets to edge-candidate-agent's export_candidate.py for validation and pipeline integration.
Full instructions (SKILL.md)
Source of truth, from tradermonty/claude-trading-skills.
name: edge-strategy-designer description: Convert abstract edge concepts into strategy draft variants and optional exportable ticket YAMLs for edge-candidate-agent export/validation.
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.yamland 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+
PyYAMLedge_concepts.yamlproduced by concept synthesis
Output
strategy_drafts/*.yamlstrategy_drafts/run_manifest.json- Optional
exportable_tickets/*.yamlfor downstreamexport_candidate.py
Workflow
- Load
edge_concepts.yaml. - Choose risk profile (
conservative,balanced,aggressive). - Generate per-concept variants with hypothesis-type exit calibration.
- Apply
HYPOTHESIS_EXIT_OVERRIDESto adjust stop-loss, reward-to-risk, time-stop, and trailing-stop per hypothesis type (breakout, earnings_drift, panic_reversal, etc.). - Clamp reward-to-risk at
RR_FLOOR=1.5to prevent C5 review failures. - Export v1-ready ticket YAML when applicable.
- Hand off exportable tickets to
skills/edge-candidate-agent/scripts/export_candidate.py.
Quick Commands
Generate drafts only:
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:
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.pyreferences/strategy_draft_schema.mdskills/edge-candidate-agent/scripts/export_candidate.py
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