risk-analysis

name	risk-analysis
description	Analyze investment risk metrics for stocks including volatility, beta, VaR, and downside risk. Use when the user asks about "risk", "volatility", "how safe", "beta", or wants to understand risk profile.

Risk Analysis Skill

Purpose

This skill performs comprehensive risk analysis on stocks, helping investors understand the risk characteristics and potential downside of their investments.

When to Use

Use this skill when the user:

• Asks about risk level or safety of a stock
• Wants to understand volatility
• Requests beta calculation (market sensitivity)
• Needs Value at Risk (VaR) analysis
• Asks "how risky is this stock?"
• Wants downside risk metrics

Capabilities

• Calculate multiple volatility measures
• Compute beta (market correlation)
• Value at Risk (VaR) estimation
• Downside deviation and Sortino ratio
• Maximum drawdown analysis
• Risk categorization (low/medium/high)

Workflow

1. Identify the Stock and Benchmark

• Extract ticker symbol from user's request
• Default benchmark: S&P 500 (ticker: SPY or ^GSPC)
• User can specify alternate benchmark

2. Determine Analysis Period

• Default: 1 year (good balance for risk metrics)
• Longer periods (2-5 years) provide more stable estimates
• Shorter periods (3-6 months) capture recent risk changes

3. Use the Helper Script

The helper script analyze_risk.py performs comprehensive risk analysis:

uv run python .claude/skills/risk-analysis/analyze_risk.py \
  --ticker TSLA \
  --benchmark ^GSPC \
  --period 1y \
  --confidence 0.95 \
  --output tmp/tsla_risk.json

Arguments:

• --ticker (required): Stock ticker symbol to analyze
• --benchmark (optional): Benchmark ticker (default: ^GSPC for S&P 500)
• --period (optional): Time period (3mo, 6mo, 1y, 2y, 5y). Default: 1y
• --confidence (optional): Confidence level for VaR (0.90, 0.95, 0.99). Default: 0.95
• --output (required): Output file path (use absolute path in tmp/)

4. Interpret the Results

Provide user-friendly interpretation of risk metrics:

Volatility:

• < 15%: Low risk (stable, blue-chip stocks)
• 15-30%: Moderate risk (typical stocks)

• 30%: High risk (volatile growth stocks, crypto-related)

Beta:

• < 0.8: Less volatile than market (defensive)
• 0.8-1.2: Similar to market volatility

• 1.2: More volatile than market (aggressive)

Value at Risk (VaR):

• Worst expected loss at given confidence level
• Example: "95% VaR of -3.5%" means 95% chance daily loss won't exceed 3.5%

Sharpe Ratio:

• < 1: Poor risk-adjusted returns
• 1-2: Good risk-adjusted returns

• 2: Excellent risk-adjusted returns

Sortino Ratio:

• Similar to Sharpe but only penalizes downside volatility
• Higher is better
• More relevant for evaluating downside risk

Output Format

The helper script returns JSON with this structure:

{
  "ticker": "TSLA",
  "benchmark": "^GSPC",
  "analysis_date": "2026-02-03",
  "period": "1y",
  "price_data": {
    "current_price": 245.32,
    "period_return_pct": 15.67,
    "price_range": {
      "min": 182.45,
      "max": 278.91
    }
  },
  "volatility_metrics": {
    "annualized_volatility_pct": 42.15,
    "downside_deviation_pct": 28.34,
    "volatility_category": "High"
  },
  "market_risk": {
    "beta": 1.85,
    "correlation_with_market": 0.62,
    "beta_interpretation": "85% more volatile than market"
  },
  "value_at_risk": {
    "confidence_level": 0.95,
    "daily_var_pct": -3.45,
    "monthly_var_pct": -12.87,
    "interpretation": "95% confidence that daily loss won't exceed 3.45%"
  },
  "risk_adjusted_metrics": {
    "sharpe_ratio": 0.87,
    "sortino_ratio": 1.23,
    "max_drawdown_pct": -35.42,
    "max_drawdown_date": "2025-08-15"
  },
  "risk_assessment": {
    "overall_risk": "High",
    "risk_factors": [
      "High volatility (42.15%)",
      "Beta significantly above 1 (1.85)",
      "Large maximum drawdown (-35.42%)"
    ],
    "suitable_for": "Aggressive investors with high risk tolerance"
  }
}

Risk Assessment Guidelines

Overall Risk Classification

Low Risk (Suitable for conservative investors):

• Volatility < 15%
• Beta < 0.8
• Max drawdown < 15%
• Example: Utility stocks, treasury bonds

Moderate Risk (Suitable for balanced investors):

• Volatility 15-30%
• Beta 0.8-1.2
• Max drawdown 15-30%
• Example: Blue-chip stocks, diversified ETFs

High Risk (Suitable for aggressive investors):

• Volatility > 30%
• Beta > 1.2
• Max drawdown > 30%
• Example: Growth stocks, tech startups, crypto-related

Important Considerations

1. Time Period Matters: Risk metrics can change over time
2. Past ≠ Future: Historical volatility doesn't guarantee future volatility
3. Context is Key: High risk isn't bad if returns justify it (check Sharpe/Sortino)
4. Diversification: Portfolio risk < sum of individual stock risks

Example Usage

User: "How risky is Tesla stock?"

Agent Workflow:

1. Identify ticker: TSLA
2. Use default benchmark: ^GSPC (S&P 500)
3. Use default period: 1y
4. Run: uv run python .claude/skills/risk-analysis/analyze_risk.py --ticker TSLA --output tmp/tsla_risk.json
5. Read and interpret results:
   • "Tesla has HIGH risk with 42% annualized volatility"
   • "It's 85% more volatile than the market (beta 1.85)"
   • "There's a 5% chance of losing more than 3.45% in a single day"
   • "Suitable for aggressive investors comfortable with high volatility"

User: "Compare the risk of AAPL vs TSLA"

Agent Workflow:

1. Run risk analysis on both stocks
2. Compare key metrics (volatility, beta, VaR, Sharpe ratio)
3. Provide relative risk assessment
4. Recommend based on user's risk tolerance

Important Notes

• Beta calculation requires benchmark data
• VaR assumes normal distribution (may underestimate tail risk)
• Longer time periods provide more reliable risk estimates
• Always use absolute paths for output files (in tmp/)
• Use uv run python to execute (not plain python)