The paradoxical situation of making correct back tests in financial markets - Best Strategies 4 Trading .com

One of the most difficult problems with creating predictive models in financial markets is to find a system that will have a high accuracy in different market conditions. The identification of the transition between different market regimes will create several contradictory problems, when using machine learning. The machine learning classifier will almost for sure start to memorize the connections between features and targets instead of finding the general relations between the two if the data is not split into two set, one for training and one for testing. This is a standard procedure and called out of sample testing. If the test time period with out of sample testing is not sufficient long enough, the window in time may not cover all different market regimes.