In Marcos Lopez de Prado's book, Advances in financial machine learning, he recommends using the combinatorial purged cross-validation(CPCV) for backtesting. His motivation is sensible. Through the method, we can test our trading strategy with more scenarios and we can reduce the false discovery rate.
Our training set is not continuous anymore with his CPCV method. As an example, our training set may combine the two time series, A(20018-01-01~2019-01-01) and B(2019-07-01~2020-01-01). We would have a big price jump at the time point where A and B are connected as there is a 6 months gap.
But with his cross-validation method, we consider the time series as one continuous time series and calculate all quantities. We will have discontinuities of quantities such as features, target labels, indicators, etc at the point. These discontinuities in the quantities bother me.
The discontinuities are artificial at most. I don't think we will meet the price jump in real-life and I feel the scenarios generated by CPCV are not the real one. Why do we bother to check our model in the unreal scenarios?
Or do we need to take an additional preprocessing step to make the price continuously? (I doubt this though as Lopez didn't mention the step at all.)