Risk Management

🔥 Vibe Prompt

"Calculate Sharpe Ratio, Sortino Ratio, Calmar Ratio, Max Drawdown, and Kelly Criterion for a trading strategy. Explain what each metric means."

Key Risk Metrics

Sharpe Ratio

$$\text{Sharpe} = \frac{R_p - R_f}{\sigma_p}$$

• Measures risk-adjusted return

• 1: Good, > 2: Great, > 3: Excellent

Sortino Ratio (downside risk only)

$$\text{Sortino} = \frac{R_p - R_f}{\sigma_d}$$

• Only penalizes negative volatility

Max Drawdown

$$\text{Max DD} = \min(\frac{\text{Valley} - \text{Peak}}{\text{Peak}})$$

• Largest peak-to-trough decline

def calculate_metrics(returns, risk_free_rate=0.02):
    """Calculate comprehensive risk metrics"""
    cumulative = (1 + returns).cumprod()
    
    # Sharpe
    excess_returns = returns - risk_free_rate / 252
    sharpe = excess_returns.mean() / excess_returns.std() * (252 ** 0.5)
    
    # Max Drawdown
    rolling_max = cumulative.expanding().max()
    drawdown = (cumulative - rolling_max) / rolling_max
    max_dd = drawdown.min()
    
    # Calmar Ratio
    calmar = cumulative.iloc[-1] / abs(max_dd)
    
    return {
        'Sharpe Ratio': sharpe,
        'Max Drawdown': max_dd,
        'Calmar Ratio': calmar,
        'Total Return': cumulative.iloc[-1] - 1
    }

# Example
returns = pd.Series([0.01, -0.005, 0.02, 0.015, -0.01, 0.03])
metrics = calculate_metrics(returns)
for k, v in metrics.items():
    print(f"{k}: {v:.4f}")

Kelly Criterion

$$f^* = \frac{bp - q}{b}$$

def kelly_criterion(win_rate, avg_win, avg_loss):
    b = avg_win / abs(avg_loss)  # win/loss ratio
    p = win_rate
    q = 1 - p
    f = (b * p - q) / b
    return max(0, min(f, 0.25))  # Cap at 25%

# Example
position_size = kelly_criterion(0.55, 200, 100)
print(f"Recommended position: {position_size:.1%} of capital")

Practice Exercise

💡 Vibe Practice: Ask AI to build a risk dashboard that tracks real-time Sharpe, Drawdown, and VaR for a trading portfolio.

Chapter Summary

• Understand core concepts and principles
• Master implementation methods and techniques
• Familiar with common issues and solutions
• Able to apply in real projects

Further Reading

• Official documentation and API references
• Open source examples on GitHub
• Technical books and online courses
• Community discussions and tech blogs

Implementation Example

Basic Example

# This section provides a complete implementation example

Steps

1. Setup: Configure development environment
2. Data: Prepare required data
3. Implementation: Build core functionality
4. Testing: Verify correctness
5. Optimization: Improve performance

Common Errors

| Error Type | Cause | Solution | |------------|-------|----------| | Compilation | Syntax | Check code syntax | | Runtime | Environment | Verify dependencies installed | | Logic | Algorithm | Step-by-step debugging | | Performance | Efficiency | Use profilers |

Code Example

import sys

def main():
    print("Hello, World!")

if __name__ == "__main__":
    main()

References

• Official documentation
• API reference
• Open source examples
• Community discussions