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Use a Bayesian intrinsic-growth valuation model to evaluate whether a company's market value…
Translate market-moving news into investable alpha hypotheses by mapping observed demand changes to revenue lines, supply chains, small-cap financial elasticity, market misclassification, validation metrics, downside risks, and position-sizing conditions. Use when the user
$ npx -y skills add haskaomni/serenity-skill --skill serenity-alpha --agent claude-codeHow it fires
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Translate market-moving news into investable alpha hypotheses by mapping observed demand changes to revenue lines, supply chains, small-cap financial elasticity, market misclassification, validation metrics, downside risks, and position-sizing conditions. Use when the user
name: serenity-alpha description: Translate market-moving news into investable alpha hypotheses by mapping observed demand changes to revenue lines, supply chains, small-cap financial elasticity, market misclassification, validation metrics, downside risks, and position-sizing conditions. Use when the user shares news, product launches, technology breakthroughs, procurement signals, supply-chain changes, earnings-call clues, or asks for Serenity-style alpha analysis, small-cap beneficiaries, or "news to financial statement" translation.
Do not ask only whether the news is impressive. Ask whether an already-observable demand change can rewrite a smaller company's financial statements.
Use this skill to convert news into a testable alpha hypothesis:
news -> observed demand change -> revenue/profit transmission -> small-cap elasticity -> validation path
Treat outputs as research hypotheses, not investment advice. Verify current prices, market caps, filings, earnings calls, and news from reliable sources before naming securities or making time-sensitive claims.
For U.S.-listed companies, use SEC filings as the factual base for reported fundamentals and management disclosure when available. `edgartools` is a good optional helper for this step because it can retrieve company filings, XBRL financial statements, filing text, insider transactions, ownership forms, and recent 8-K disclosures.
If the environment does not already have it, install with `pip install edgartools` or `uv pip install edgartools`. The import package is `edgar`, not `edgartools`. SEC access requires an identity; set `EDGAR_IDENTITY="Name email@example.com"` in the environment or call `from edgar import set_identity; set_identity("name@example.com")` before requests.
Minimal usage pattern:
from edgar import Company
company = Company("AAPL")
filings = company.get_filings(form="10-Q")
financials = company.get_financials()
income = financials.income_statement()Use it to support the analysis, not to replace the framework:
When using SEC data, cite the filing form and filing date in the reasoning, and state when the data is stale relative to the news item.
Start and end with the company that best fits the alpha hypothesis.
Identify whether the item is merely narrative or a demand inflection. Prioritize evidence that has already happened:
If demand is not observable, classify the idea as watchlist-only.
Use this sentence structure:
Because X is happening, demand for Y is increasing. That demand can flow to A/B/C companies through revenue line Z. Company N may matter because its market cap, revenue base, or business purity is small enough for the demand shock to change reported results.
For each candidate, map the mechanism to income-statement or balance-sheet items:
Look for "small shovels," not only obvious mega-cap winners. Rank candidates higher when they have:
Use this rough screen:
alpha elasticity ~= incremental demand impact / current company scale
Ask:
Misclassification creates alpha only when the relabeling can be validated by numbers, not just story.
Convert the thesis into observable checkpoints. Use the most relevant indicators:
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