risk-metrics-calculation

# Risk Metrics Calculation

Comprehensive risk measurement toolkit for portfolio management, including Value at Risk, Expected Shortfall, and drawdown analysis.

## When to Use This Skill

- Measuring portfolio risk
- Implementing risk limits
- Building risk dashboards
- Calculating risk-adjusted returns
- Setting position sizes
- Regulatory reporting

## Core Concepts

### 1. Risk Metric Categories

| Category          | Metrics         | Use Case             |
| ----------------- | --------------- | -------------------- |
| **Volatility**    | Std Dev, Beta   | General risk         |
| **Tail Risk**     | VaR, CVaR       | Extreme losses       |
| **Drawdown**      | Max DD, Calmar  | Capital preservation |
| **Risk-Adjusted** | Sharpe, Sortino | Performance          |

### 2. Time Horizons

```
Intraday:   Minute/hourly VaR for day traders
Daily:      Standard risk reporting
Weekly:     Rebalancing decisions
Monthly:    Performance attribution
Annual:     Strategic allocation
```

## Implementation

### Pattern 1: Core Risk Metrics

```python
import numpy as np
import pandas as pd
from scipy import stats
from typing import Dict, Optional, Tuple

class RiskMetrics:
    """Core risk metric calculations."""

    def __init__(self, returns: pd.Series, rf_rate: float = 0.02):
        """
        Args:
            returns: Series of periodic returns
            rf_rate: Annual risk-free rate
        """
        self.returns = returns
        self.rf_rate = rf_rate
        self.ann_factor = 252  # Trading days per year

    # Volatility Metrics
    def volatility(self, annualized: bool = True) -> float:
        """Standard deviation of returns."""
        vol = self.returns.std()
        if annualized:
            vol *= np.sqrt(self.ann_factor)
        return vol

    def downside_deviation(self, threshold: float = 0, annualized: bool = True) -> float:
        """Standard deviation of returns below threshold."""
        downside = self.returns[self.returns < threshold]