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trade-hypothesis-ideator — Claude Code skill: hypotheses from trade

Turns trade logs, journal notes and market data into falsifiable hypotheses with experiment designs and kill criteria.

Install into Claude Code · Skills CLI (Vercel) All methods and platforms ↓
npx skills add tradermonty/claude-trading-skills --skill trade-hypothesis-ideator -a claude-code
Open on GitHub

What trade-hypothesis-ideator does

The skill takes a normalized JSON bundle containing one or more of trade_log, journal_snippets, market_data or observations, and returns 1 to 5 hypothesis cards. Each card gets a verdict: pursue, revise or discard. Ranking happens in a second pass, after a critique step.

Work runs through scripts/run_hypothesis_ideator.py twice. Pass one normalizes the bundle and extracts an evidence summary. Pass two ranks the hypotheses and formats the output. Add --export-strategies and every pursue card is also written out as strategy_<hypothesis_id>.yaml plus metadata_<hypothesis_id>.json, in a format compatible with edge-finder-candidate/v1. The generation and critique prompts live in prompts/.

You need Python 3.9+ with pyyaml installed. No external API keys, since the calculation stays local. If all you have is scattered notes rather than a structured bundle, convert them to JSON first — pass one has nothing to normalize otherwise.

Output lands in hypothesis_cards_<date>.json and a readable hypothesis_cards_<date>.md with experiment designs and kill criteria. That pair is what you take to review before committing money to a test.

What it helps with

  • turn scattered trade logs into falsifiable hypotheses
  • rate evidence strength and drop weak ideas
  • write experiment designs and exit conditions up front
  • export a selected strategy to YAML for edge-finder
  • compare several ideas against one ranking

Example prompts for the agent

  • here is a JSON bundle with my trade log and notes, generate and rank hypotheses
  • run pass two and export the pursue cards to strategy.yaml
  • tell me which hypotheses in this bundle are worth testing and which to drop

How to install trade-hypothesis-ideator

1Skills CLI (Vercel)

npx skills add tradermonty/claude-trading-skills --skill trade-hypothesis-ideator -a claude-code

2Manually: put the files in place

~/.claude/skills/trade-hypothesis-ideator/SKILL.md

Copy the whole skill folder; for one project use .claude/skills/trade-hypothesis-ideator/

Step by step, with commands for each platform: How to install skills in Claude Code: five ways

Questions about trade-hypothesis-ideator

How do I install trade-hypothesis-ideator in Claude?

Run npx skills add tradermonty/claude-trading-skills --skill trade-hypothesis-ideator -a claude-code. The skill lands at ~/.claude/skills/trade-hypothesis-ideator/SKILL.md.

What does trade-hypothesis-ideator do?

It generates 1 to 5 hypothesis cards from an input JSON bundle, critiques and ranks them, and can export pursue cards into strategy.yaml and metadata.json.

What do I need to run it?

A JSON bundle with at least one of trade_log, journal_snippets, market_data or observations, plus Python 3.9+ with pyyaml. No external API keys are required.

Source text

SKILL.md · MIT
---
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 `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):

```bash
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):

```bash
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

© tradermonty · MIT · tradermonty/claude-trading-skills

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