Quantify tail risk and fragility for a ticker using 1-year daily returns.
npx skills add https://github.com/kavi-lin/stock --skill tail-risk-analyzer
Please help me install this Agent Skill. Skill: tail-risk-analyzer Source: https://github.com/kavi-lin/stock/tree/main/skills/tail-risk-analyzer Command: npx skills add https://github.com/kavi-lin/stock --skill tail-risk-analyzer
This Skill provides a data-backed measure of tail risk and fragility for a single ticker, based on 1-year daily returns, to support risk-aware investment decisions.
Run the tool with a ticker to compute its fragility score and recommended sizing.
High-intent search queries and answers about installing and using this skill.
To calculate tail risk and fragility for a single stock ticker, you need 1-year daily returns to compute excess kurtosis, skewness, VaR95, and annualized volatility. This process yields a structured fragility label and an investment multiplier for sizing.
Ticker fragility and tail risk are measured using excess kurtosis, skewness, VaR95, max drawdown, and annualized volatility. These metrics are calculated from 1-year daily returns to generate a ROBUST, MODERATE, or FRAGILE label.
You compute VaR95 and annualized volatility by fetching 1-year daily returns via yfinance and applying statistical functions from NumPy. This combination calculates the necessary risk metrics to evaluate a stock's fragility and inform sizing decisions.
Yes, you can use 1-year historical daily returns to determine position sizing by calculating a fragility label and an investment multiplier. This data-backed approach supports risk-aware investment decisions and per-stock sizing in workflows.
yfinance provides 1-year daily returns which are sufficient to calculate skewness and excess kurtosis when processed with NumPy. This data depth allows the tool to output a reliable fragility label and position multiplier for risk validation.
Using 1-year daily returns for risk validation limits the analysis to recent market conditions and may not capture long-term tail risk or rare black swan events. It outputs a current fragility label and multiplier but should be validated against broader historical contexts.
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