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/trade-hypothesis-ideator

@26a6d52

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.

Use this Skill: https://skilld.dev/gh/tradermonty/claude-trading-skills/trade-hypothesis-ideator

This session only. Nothing lands on disk.

SKILL.md

≈79 tokens always: the name and description. ≈607 when used: this file. ≈4.3k more on demand in 12 files.

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

Source: SKILL.md on GitHub

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  • Gen Agent Trust Hub3d

    The skill performs dynamic execution by loading and running Python code from a calculated path outside its directory. It also handles external input data that is injected into prompts, creating an indirect prompt injection surface.

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    Risk: LOW · No issues

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    Score: 93/100 · 2 sections analyzed

Signed by skilld at 26a6d52. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 18 hours ago.

Activeupdated 4 months ago

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