trade-hypothesis-ideator
tradermonty/claude-trading-skills
Generate falsifiable trade strategy hypotheses from market data and trade logs with experiment designs and kill criteria.
What is trade-hypothesis-ideator?
This skill takes structured input bundles of trade logs, journal entries, and market observations to generate ranked hypothesis cards with experiment designs and kill criteria. Use it to bridge qualitative trading observations into quantifiable hypotheses before committing capital to validate a new strategy.
- Generate 1-5 structured hypothesis cards from normalized input bundles with evidence extraction
- Critique and rank hypotheses with verdicts (pursue, revise, discard)
- Export pursue-ranked hypotheses as strategy.yaml files compatible with edge-finder-candidate/v1
- Provide experiment designs and kill criteria for each hypothesis
- Output human-readable markdown summaries alongside JSON hypothesis cards
How to install trade-hypothesis-ideator
npx skills add https://github.com/tradermonty/claude-trading-skills --skill trade-hypothesis-ideator- Input JSON bundle containing one or more of: trade_log, journal_snippets, market_data, observations
- Python 3.9 or later
- pyyaml Python package installed
How to use trade-hypothesis-ideator
- 1.Prepare a JSON input bundle with your trade logs, journal snippets, market data, or observations
- 2.Run Pass 1 to normalize evidence and extract summaries using run_hypothesis_ideator.py with --input flag
- 3.Run Pass 2 to critique, rank hypotheses, and optionally export strategies using --hypotheses and --export-strategies flags
- 4.Review the output hypothesis_cards JSON and markdown files to see ranked hypotheses with verdicts
- 5.Export pursue-ranked hypotheses to strategy.yaml files for use with edge-finder or other backtesting tools
Use cases
- Convert trade journal observations into testable hypotheses with defined kill criteria
- Generate multiple strategy ideas from a collection of profitable trades to identify repeatable patterns
- Bridge qualitative market observations into quantitative experiment designs before backtesting
- Export validated hypotheses as strategy configurations for automated edge-finding tools
- Rank competing trade ideas by evidence quality and feasibility
- Quantitative traders developing new strategy ideas
- Traders maintaining trade journals who want to systematize hypothesis generation
- Strategy researchers designing experiments to validate trading edges
- Teams building automated trading systems who need structured hypothesis input
trade-hypothesis-ideator FAQ
Your input should be a JSON object containing one or more of these keys: trade_log (array of trades), journal_snippets (array of text entries), market_data (OHLCV or other market observations), and observations (array of qualitative notes). See examples/example_input.json for a template.
pursue = hypothesis has strong evidence and clear experiment design, worth validating; revise = hypothesis has merit but needs refinement or more evidence; discard = hypothesis lacks sufficient evidence or has logical flaws.
Yes. Use the --export-strategies flag to generate strategy.yaml files compatible with edge-finder-candidate/v1, along with metadata.json files tracking provenance.
No. This is a pure calculation skill that works entirely offline with your input data and built-in prompts.
The skill supports multiple patterns including mean-reversion, momentum, event-driven, and others documented in references/hypothesis_types.md.
Full instructions (SKILL.md)
Source of truth, from tradermonty/claude-trading-skills.
name: trade-hypothesis-ideator description: > 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.
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
pyyamlinstalled - No external API keys required (pure calculation skill)
Workflow
- Receive input JSON bundle.
- Run pass 1 normalization + evidence extraction.
- Generate hypotheses with prompts:
prompts/system_prompt.mdprompts/developer_prompt_template.md(inject{{evidence_summary}})
- Critique hypotheses with
prompts/critique_prompt_template.md. - Run pass 2 ranking + output formatting + guardrails.
- Optionally export
pursuehypotheses 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 criteriastrategy_<hypothesis_id>.yaml— (Optional) Edge-finder-compatible strategy export forpursuecardsmetadata_<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
Related skills
More from tradermonty/claude-trading-skills and the wider catalog.

trade-performance-coach
Review closed trades for process adherence, risk discipline, and behavior patterns—not buy/sell advice.

trader-memory-core
Track investment theses from screening to closed position with postmortem analysis and P&L reporting.

trading-skills-navigator
Route trading goals to the right workflow, skillset, and setup path without executing trades.

uptrend-analyzer
Diagnose market breadth health using free Uptrend Ratio data—no API key required.

us-market-bubble-detector
Quantitative bubble risk assessment using objective market metrics and strict data-driven scoring.

us-stock-analysis
Comprehensive fundamental and technical analysis for US stocks with real-time data and investment reports.