When backtesting a trading strategy using Walk Forward Analysis/Optimization, I see people split each window into training and testing sets.
Suppose you want to select the best combination of MAs for your MACD strategy, you would first try the different combinations on your training set, select the best one, and report the results you get with those parameters on your test set.
However, when the strategy in place is a machine-learning model, shouldn't we have a validation set as we have in every other ML problem? The training set would be for learning the model parameters, the validation set to select the best-performing hyper-parameters, and finally, the test set to report the model results.
I haven't seen anywhere where a validation set is used in WFA/WFO. I would appreciate any input or extra resources on this topic.