How to avoid overfitting when backtesting stocks?

@tyrel_franecki 

1. Use a sufficiently large dataset: Ensure that your backtesting dataset includes a significant amount of historical data to provide a more accurate representation of market conditions and trends.
2. Use cross-validation techniques: Implement techniques such as k-fold cross-validation to assess the model's performance on various subsets of the data. This helps to validate the model's performance and reduce the risk of overfitting.
3. Regularize your model: Apply regularization techniques, such as L1 and L2 regularization, to penalize complex models and prevent them from fitting noise in the data.
4. Avoid complex models: Simplicity is often better when it comes to building predictive models for stock backtesting. Avoid using overly complex models that can easily overfit the data.
5. Use out-of-sample testing: Test your model's performance on unseen data to ensure that it generalizes well to new observations. This helps to verify that the model is not just memorizing the training data.
6. Monitor performance metrics: Keep track of key performance metrics, such as accuracy, precision, recall, and F1 score, to evaluate the model's performance and detect any signs of overfitting.
7. Consider ensemble methods: Combine multiple models to create an ensemble model that can provide more robust predictions and reduce the risk of overfitting.

By following these tips and best practices, you can minimize the risk of overfitting when backtesting stocks and improve the overall reliability of your trading strategies.