Robert Carver on How To Build Successful Trading Strategies
Macro Hive Conversations With Bilal Hafeez
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How to Reduce Overfitting in Your Back Tests
The importance of data is important to reduce overfitting when doing back tests. So one thing to avoid is so-called in-sample fencing. It means that the returns and back tests tend to be inflated and look better than they are. The second issue with very high back test returns is they can encourage you to use too much leverage. And finally, it's quite rare to see statistically significant evidence that you should definitely prefer one model over another.
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