Risk Management Checklist for Algorithmic Trading

1. Strategy Design

• Clearly define risk tolerance and loss limits.
• Include stop-loss and take-profit rules in strategy.
• Confirm trades comply with regulatory requirements.

2. Position Sizing

• Use fixed percentage or volatility-based sizing.
• Limit single trade exposure relative to total capital.
• Rebalance positions regularly to maintain diversification.

3. Backtesting and Validation

• Test strategy on diverse and out-of-sample historical data.
• Include transaction costs, slippage, and realistic market conditions.
• Monitor drawdowns, risk-adjusted returns, and consistency.

4. Execution and Monitoring

• Automate trade execution with reliable broker APIs.
• Implement real-time monitoring for abnormal behavior or errors.
• Set up alerts for strategy breaches or unexpected losses.

5. Emergency and Contingency Plans

• Define automated kill switches to stop all trading.
• Prepare manual intervention methods for critical situations.
• Test emergency procedures regularly.

6. Portfolio-Level Risk Controls

• Diversify across uncorrelated assets and strategies.
• Limit maximum total drawdown for portfolio.
• Review and adjust strategies according to changing markets.

7. Continuous Improvement

• Keep models and data updated for accuracy.
• Use AI and machine learning cautiously for risk predictions.
• Educate team members on risk management principles.