Risk Management in Algorithmic Trading

Essential Risk Management Principles

Risk management is the most critical aspect of algorithmic trading. Even the best strategy can fail without proper risk controls.

Key Risk Management Techniques

• Position Sizing - Never risk more than 1-2% per trade
• Stop Losses - Always define your maximum acceptable loss
• Diversification - Don't put all capital in one strategy
• Maximum Drawdown - Set limits on portfolio losses

Python Implementation

def calculate_position_size(capital, risk_per_trade, stop_loss_pct):
    """
    Calculate position size based on risk management rules
    """
    risk_amount = capital * risk_per_trade
    position_size = risk_amount / stop_loss_pct
    return position_size

# Example usage
portfolio_value = 100000
risk_per_trade = 0.02  # 2%
stop_loss = 0.05  # 5%

position = calculate_position_size(portfolio_value, risk_per_trade, stop_loss)
print(f"Position size: ${position:.2f}")

Remember: preserving capital is more important than making profits. A good risk management system will keep you trading for the long term.