Compare two authorized local AlphaGBM research records to identify changes in facts, scores, and judgments. The tool checks whether identities, versions, dates, and units match, and reports gaps without accessing account history or making a new paid query.
中文简介正在准备中。当前显示英语简介。
Compare research records you provide to see changes in facts, scores and judgments. Use when the user asks to investment review with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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。
Compare research records you provide to see changes in facts, scores and judgments.
Read access and evidence rules. Python 3.9+ is the only runtime dependency; no separate CLI or sibling Skill installation is required. Resolve <skill-dir> to the directory containing this file.
Read references/investment-review.md. Ask the user for two authorized local JSON results and replace the file placeholders with quoted absolute paths. Compare matching identities, versions, observation dates and units only. This runs locally without a key, API calls, account-history access or a new allowance charge. Do not interpret an unverified file hash as proof of provenance, a changed price as a correct thesis, or the comparison as a trading-performance backtest. Obtaining new stock/options evidence separately still needs permission and account allowance.
python3 "<skill-dir>/scripts/run.py" review --baseline <authorized-baseline.json> --current <authorized-current.json> --lang en
This command compares authorized local files without network access, a key or analysis-credit charge.
Use AlphaGBM to compare the two research records I provide. Show comparable changes, data gaps and judgments needing review. Process locally without reading cloud history.
中文：帮我用 AlphaGBM 对比我提供的前后两份研究记录，列出可比变化、数据缺口和需要重新核实的判断。仅在本地处理，不读取云端历史。
To compare two previous workflow results, read investment review. Use review --baseline <authorized-file> --current <authorized-file> --lang en (or zh). This is local comparison, not account-history access, automatic monitoring or a new paid query.
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Study the documented put-spread backtest method as a reference. This skill explains the technique and helps keep documented methods distinct from measured results; it does not fetch market data or run an API analysis.
中文简介正在准备中。当前显示英语简介。
Study a reference method for finding supply-chain bottlenecks. It explains the documented approach for research and discussion, while distinguishing a technique from measured results; it does not fetch live data, run API requests, or create alerts.
中文简介正在准备中。当前显示英语简介。
Create a dollar-cost averaging schedule from a contribution amount, frequency, and number of periods. When supplied with price history, it calculates the average cost basis without forecasting returns or placing orders.
中文简介正在准备中。当前显示英语简介。
Evaluate Hong Kong and China A-share dividend candidates with AlphaGBM across yield quality, sustainability, cash-flow coverage, quality, growth, valuation, and momentum. The result includes factor evidence and data gaps, helping users assess an opportunity without treating it as a return forecast or trade instruction.
中文简介正在准备中。当前显示英语简介。
Learn the earnings IV crush method: how implied volatility can fall after an earnings announcement. This reference helps explain the technique for research; it does not fetch live data or run an API analysis.
中文简介正在准备中。当前显示英语简介。
Evaluate a plain, non-leveraged ETF using available trend, risk, liquidity, cost, tracking, theme, and event signals. The workflow returns a factor-based opportunity score while preserving missing data and its sources for follow-up checks.
中文简介正在准备中。当前显示英语简介。