Turn trade logs, journal notes, and market observations into ranked trading hypotheses that experiments can test and disprove. Produce cards with experiment designs and stopping criteria, with optional strategy file exports for hypotheses marked for further investigation.
中文简介正在准备中。当前显示英语简介。
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.
实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
trade_log, journal_snippets, market_data, observationspyyaml installedprompts/system_prompt.mdprompts/developer_prompt_template.md (inject {{evidence_summary}})prompts/critique_prompt_template.md.pursue hypotheses via Step H strategy exporter.python3 skills/trade-hypothesis-ideator/scripts/run_hypothesis_ideator.py \
--input skills/trade-hypothesis-ideator/examples/example_input.json \
--output-dir reports/
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
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 for pursue cardsmetadata_<hypothesis_id>.json — (Optional) Provenance metadata for exported strategiesreferences/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还没有评价。欢迎分享你的使用感受。
无需登录即可发表。欢迎分享使用感受、提问，或说明在哪些环境下无法运行。
还没有评论。来写第一条吧。
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.
中文简介正在准备中。当前显示英语简介。
Analyze chart images showing how broadly US stocks are participating in market trends. Assess medium-term conditions and short-term signals, then produce an English report with positioning guidance, alternative scenarios, and risks based on the visible data.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
Turn VCP screening results, which identify narrowing price swings, into breakout trade plans. Calculate entry, stop-loss, target prices, and position sizes using worst-case entries, while applying portfolio risk limits and producing Alpaca-compatible order templates.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。
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.
中文简介正在准备中。当前显示英语简介。