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trade-hypothesis-ideator

Generate falsifiable trade strategy hypotheses from market data, trade logs, and journal snippets. Use when you have a structured input bundle and want ranked hypothesis cards with experiment designs, kill criteria, and optional strategy.yaml export compatible with edge-finder-candidate/v1.

tradermonty/claude-trading-skills
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含まれるファイル（28）

  • SKILL.md2.7 KB
  • examples/example_input.json2.5 KB
  • examples/example_output.json3.3 KB
  • examples/sample_input.json1.8 KB
  • prompts/critique_prompt_template.md309 B
  • prompts/developer_prompt_template.md405 B
  • prompts/system_prompt.md406 B
  • references/evidence_quality_guide.md654 B
  • references/hypothesis_types.md428 B
  • requirements.txt123 B
  • schemas/hypothesis_card.schema.json3.0 KB
  • schemas/input_bundle.schema.json2.2 KB
  • schemas/output_bundle.schema.json1.5 KB
  • schemas/raw_hypotheses.schema.json388 B
  • scripts/pipeline/__init__.py0 B
  • scripts/pipeline/evidence_extractor.py3.8 KB
  • scripts/pipeline/format_output.py12.8 KB
  • scripts/pipeline/normalize.py6.6 KB
  • scripts/pipeline/ranking.py2.6 KB
  • scripts/pipeline/strategy_exporter.py9.8 KB
  • scripts/run_hypothesis_ideator.py6.7 KB
  • scripts/tests/conftest.py1.1 KB
  • scripts/tests/test_evidence_extractor.py2.7 KB
  • scripts/tests/test_format_output.py4.1 KB
  • scripts/tests/test_guardrails.py5.5 KB
  • scripts/tests/test_normalize.py5.3 KB
  • scripts/tests/test_ranking.py2.7 KB
  • scripts/tests/test_strategy_exporter.py8.0 KB

SKILL.md（原文）

インストールする前に、エージェントに与えられる指示の中身を確認できます。

Trade Hypothesis Ideator

Generate 1-5 structured hypothesis cards from a normalized input bundle, critique and rank them, then optionally export pursue cards into strategy.yaml + metadata.json artifacts.

When to Use

  • After gathering trade logs, journal entries, or market observations that suggest a potential edge
  • When you have a structured input bundle (JSON) with evidence snippets and want falsifiable hypotheses
  • To bridge qualitative observations into quantitative experiment designs
  • Before committing capital to validate a new strategy idea with kill criteria

Prerequisites

  • Input JSON bundle with one or more of: trade_log, journal_snippets, market_data, observations
  • Python 3.9+ with pyyaml installed
  • No external API keys required (pure calculation skill)

Workflow

  1. Receive input JSON bundle.
  2. Run pass 1 normalization + evidence extraction.
  3. Generate hypotheses with prompts:
    • prompts/system_prompt.md
    • prompts/developer_prompt_template.md (inject {{evidence_summary}})
  4. Critique hypotheses with prompts/critique_prompt_template.md.
  5. Run pass 2 ranking + output formatting + guardrails.
  6. Optionally export pursue hypotheses via Step H strategy exporter.

Scripts

  • Pass 1 (evidence summary):
python3 skills/trade-hypothesis-ideator/scripts/run_hypothesis_ideator.py \
  --input skills/trade-hypothesis-ideator/examples/example_input.json \
  --output-dir reports/
  • Pass 2 (rank + output + optional export):
python3 skills/trade-hypothesis-ideator/scripts/run_hypothesis_ideator.py \
  --input skills/trade-hypothesis-ideator/examples/example_input.json \
  --hypotheses reports/raw_hypotheses.json \
  --output-dir reports/ \
  --export-strategies

Output

  • hypothesis_cards_<date>.json — Ranked hypothesis cards with verdicts (pursue, revise, discard)
  • hypothesis_cards_<date>.md — Human-readable summary with experiment designs and kill criteria
  • strategy_<hypothesis_id>.yaml — (Optional) Edge-finder-compatible strategy export for pursue cards
  • metadata_<hypothesis_id>.json — (Optional) Provenance metadata for exported strategies

Resources

  • references/hypothesis_types.md — Taxonomy of hypothesis patterns (mean-reversion, momentum, event-driven, etc.)
  • references/evidence_quality_guide.md — Criteria for rating evidence strength and sample size requirements

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