Score ETF trend, risk, liquidity, cost and available tracking and theme signals while preserving data gaps. Use when the user asks to etf strategy with AlphaGBM. Use the bundled Python runner; never silently replace real results with demos.
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See how to installReview the instructions the agent will receive before you install.
Score ETF trend, risk, liquidity, cost and available tracking and theme signals while preserving data gaps.
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 a plain, non-leveraged ETF ticker. The workflow scores observed trend, risk, liquidity, cost, tracking, theme and event inputs only when supplied by the provider. Leveraged and inverse ETFs are rejected. Expense ratio may be available from the provider; tracking error, theme and event signals may remain missing. Preserve status and missingData, and never fill them from model memory or a demo. The strategy-workflows.v1 backend must be deployed; unsupported servers receive no paid request.
python3 "<skill-dir>/scripts/run.py" etf SPY --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 to evaluate an ETF strategy opportunity for SPY, showing factor evidence, missing data and next checks.
中文：帮我调用 AlphaGBM 评估 SPY 的 ETF 策略机会，列出分项依据、缺失数据和下一步核验点。
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