Use AlphaGBM to assess an explicitly selected ticker from observed returns, moving-average confirmation, volatility, drawdown, and volume. It returns a sourced trend score and risk factors, not a price forecast or trade instruction.
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Score observed price trend, confirmation, risk and participation without forecasting a price. Use when the user asks to momentum following 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。
Score observed price trend, confirmation, risk and participation without forecasting a price.
Release preview: the matching backend has not been verified in production. Do not claim this structured workflow is live; unsupported servers must fail closed. Retained legacy runner commands remain compatible.
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.
Use an explicit supported ticker. The workflow scores observed returns, moving-average confirmation, volatility/drawdown and volume participation from the configured market-data service. It is not a price forecast, a probability of profit or a trade instruction. Preserve quoteTime, sources, ready versus partial status and missingData. Compare the next result for the same ticker instead of treating one score as a promise. The strategy-workflows.v1 backend must be deployed; unsupported servers receive no paid request.
python3 "<skill-dir>/scripts/run.py" momentum NVDA --confirm-usage --lang en
This example contains --confirm-usage. Use that flag only after the user has approved allowance consumption. Require ALPHAGBM_API_KEY in the environment, never in a prompt.
Use AlphaGBM for momentum following on NVDA. Show the trend state, evidence, risks and next checks.
中文：帮我调用 AlphaGBM 做 NVDA 趋势跟踪，给出趋势状态、依据、风险和下一步验证点。
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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.
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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。