Risk Assessment

This gives you the core risk metrics traders actually care about: historical volatility, beta against SPY, Value at Risk at both 95% and 99% confidence levels, max drawdown, and Sharpe ratio. You can pass in a position size to get dollar-based VaR, which is handy for actual position sizing decisions. It pulls data via yfinance and lets you specify the lookback period from one month to a year. The output is straightforward JSON that Claude can explain in plain language. If you're doing any kind of portfolio work or need to quantify downside risk beyond just looking at price charts, this covers the standard metrics without requiring you to spin up a Bloomberg terminal.

skills.sh installs: 105
GitHub stars: 406
last updated: Jun 11, 2026

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Install

$ npx -y skills add staskh/trading_skills --skill risk-assessment --agent claude-code

Installs into .claude/skills of the current project.

Files

Risk Assessment

Calculate risk metrics for stocks and positions.

Instructions

Note: If uv is not installed or pyproject.toml is not found, replace uv run python with python in all commands below.

uv run python scripts/risk.py SYMBOL [--period PERIOD] [--position-size SIZE]

Arguments

SYMBOL - Ticker symbol
--period - Analysis period: 1mo, 3mo, 6mo, 1y (default: 1y)
--position-size - Dollar amount for position-specific metrics (optional)

Output

Returns JSON with:

volatility - Historical volatility (annualized)
beta - Beta vs SPY
var_95 - 95% Value at Risk (daily)
var_99 - 99% Value at Risk (daily)
max_drawdown - Maximum drawdown in period
sharpe_ratio - Risk-adjusted return
position_risk - If position-size provided, dollar VaR

Explain what the risk metrics mean and suggest position sizing if relevant.

Dependencies

numpy
yfinance

Timezone

All timestamps and time-based calculations must use the America/New_York timezone. All JSON output must include generated_at (NY time string) and data_delay fields.

SKILL.md on GitHub