Turn trading hypotheses into concrete strategy drafts with multiple variants and a chosen risk profile. Adjust exit settings by hypothesis type, then optionally export YAML tickets, structured files that a downstream tool can validate and process.
Convert abstract edge concepts into strategy draft variants and optional exportable ticket YAMLs for edge-candidate-agent export/validation.
Run score Not tested
See how to installReview the instructions the agent will receive before you install.
Translate concept-level hypotheses into concrete strategy draft specs. This skill sits after concept synthesis and before pipeline export validation.
edge_concepts.yaml and need strategy candidates.PyYAMLedge_concepts.yaml produced by concept synthesisstrategy_drafts/*.yamlstrategy_drafts/run_manifest.jsonexportable_tickets/*.yaml for downstream export_candidate.pyedge_concepts.yaml.conservative, balanced, aggressive).HYPOTHESIS_EXIT_OVERRIDES to adjust stop-loss, reward-to-risk, time-stop, and trailing-stop per hypothesis type (breakout, earnings_drift, panic_reversal, etc.).RR_FLOOR=1.5 to prevent C5 review failures.skills/edge-candidate-agent/scripts/export_candidate.py.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
skills/edge-strategy-designer/scripts/design_strategy_drafts.pyreferences/strategy_draft_schema.mdskills/edge-candidate-agent/scripts/export_candidate.pyNo reviews yet. Tell others what you thought.
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Plan and assess backtests, which test trading rules against historical data. Examine sensitivity to rule changes, trading costs, and data bias, then use supplied performance metrics to generate a structured evaluation with reasons that block adoption.
Assess how broadly US stock market gains are shared across stocks using public CSV data and optional charts. Produce English reports covering market conditions, trading signals, scenarios, and positioning for different investment timeframes.
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Screen US growth stocks with William O'Neil's CANSLIM method, which evaluates seven factors including earnings, price strength, trading volume, and market direction. Receive ranked candidates with composite scores, factor breakdowns, market warnings, and data quality notes.
Combine three reports on crowded positions, failed reactions to news, and weekly price reversals into one trading setup status. Identify missing or invalid evidence and determine whether the setup is ready for planning.