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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.