06. Backtesting and Forward Testing
AI For Trading C6 L4 A05 Backtesting And Forward Testing V3
Understanding Backtesting and Forward Testing in Trading AI Models
Backtesting and forward-testing are essential for evaluating the performance of AI models in trading.
Backtesting
- Utilizes past data to simulate how a model would have performed in real market conditions.
- Important for assessing if the model adheres to financial metrics like profit, loss, and drawdown.
- Begins by making buying/selling decisions on historical test data.
- Prevents look-ahead bias by using information available only up to each decision point.
Forward Testing
- Applies the model to live market data to gauge real-time performance.
- Often starts with paper trading to simulate results without financial risk.
- Further validates the model's ability to handle current market conditions.
Key Considerations
- Models must perform well in both testing types to prove their robustness.
- Identifying overfitting or poor generalization may require revisiting model tuning.
- Ongoing monitoring is necessary to ensure adaptability as market conditions evolve.
- Drafting a final model involves training on complete data before actual deployment.
SOLUTION:
- Back-testing evaluates a model's performance using historical data before implementing it in live trading.
- Forward-testing involves using a model in a simulated live environment to assess its predictive power.