Tag Archives: data snooping

Is Your Hedge Fund Using Your Money To Test Trading Strategies?

Developing trading strategies is a tedious and time consuming task with a low success rate. Significance of results often cannot be inferred from historical tests and validation samples due to data snooping, especially when many trials are involved. As a … Continue reading

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Machine Learning Rediscovered

Attempting to discover trading algos via machine learning is a dangerous practice because of the problem of multiple comparisons. Although some have recently rediscovered machine leaning applications for algo development, they usually avoid discussing the problems or fail to provide … Continue reading

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Using Random Data to Test the Integrity of Machine Learning Algorithms

In a recent blog I argued that the chances of finding a profitable trading algo via machine learning are for all practical purposes zero due to the large data-mining bias. One method of checking machine learning algorithms for the generation … Continue reading

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Adaptive Trend-Following Performance

This is a report of the performance of TFadapt, a trend-following system that adapts to changing market conditions, on historical data of 8 different international indices since the start of 1990. The outstanding and statistically significant performance of this system  in SPY … Continue reading

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Fooled By Out of Sample Testing

Developing trading systems on an in-sample of historical data and then testing the results on an out-of-sample is a widely used practice that hides some fundamental flaws. In this article I list some of the problems that may arise from the wrong application of … Continue reading

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Automated Discovery of Trading Systems

There are many ways of accomplishing automated trading strategy discovery. The idea is simple in principle and I have described it in more detail in my (out-of-print) books since 1999. In a nutshell, historical data series are used to discover robust … Continue reading

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