Retrieves option scores through the published AlphaGBM interface and presents the successful response with its asset identity, dates, units, and data gaps intact. It helps review option candidates, while making clear that scores are not trade instructions or guaranteed fills.
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Options Score via the published AlphaGBM interface. Use for this focused function, not as a promise that every website API accepts API keys.
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。
Read access and evidence rules first. This package is a focused function; full research workflows are listed in the repository catalogue. Its runner is self-contained and requires only Python 3.9+.
python3 "<skill-dir>/scripts/run.py" options NVDA --limit 3 --confirm-usage
Requires ALPHAGBM_API_KEY. If the command uses --confirm-usage, first obtain approval to use the shared account allowance. Snapshot reads do not consume analysis credits, but still require account access.
Return only the successful API response, with its original asset identity, dates, units, missing-data flags and score type. Stock risk scores are not opportunity scores. Volatility fields can be missing; do not turn a snapshot into a fabricated 252-day IV Rank. Option candidates are not guaranteed fills or trade instructions. Never fall back silently to demo data. Nonzero exit must be surfaced as an error.
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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.
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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.
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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。