Tag Archives: Overfitting

In defense of a quantitative approach to financial markets

I have the feeling that there is some subtle yet spread misconception about data-driven research in financial markets and I will take this article: Seeking Alpha – Not Even Wrong: Why Data-Mined Market Predictions Are Worse Than Useless by Justice Litle … Continue reading

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Feature selection in trading algorithms

Lately I have been looking for a more systematic way to get around overfitting and in my quest I found it useful to borrow some techniques from the Machine Learning field. If you think about it, a trading algorithm is … Continue reading

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Trimmed performance estimators

This is a quick follow-up on my previous post on Quantile normalization. Instead of removing just the top X quantile of returns/trades when optimizing a strategy’s parameters space, my recent approach has been to remove the top and bottom X quantiles, … Continue reading

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Underfitting, misfitting and understanding alpha’s drivers

While overfitting is certainly a challenge, falling for the opposite extreme is also a possibility. Reporting part of an interview of William Echkardt from Futures magazine (which I would recommend to read in full from here): “I can talk a little … Continue reading

Posted in On backtesting, Trading Strategies Design | Tagged , , , , , | 1 Comment