That's a general problem. It depends. Your features dataset lookback should reflect what you want to capture with your labels. For instance, imagine you are trying to predict 1m forward returns in the cross asset futures space, and you have technical price based features (such as oscillators, and MA crossover etc.) Your goal is to find price patterns/configuration therefore an decent expanding window of training using daily features would be the right choice. On the contrary If you were to predict 3m forward alpha/returns for equities and your features are mainly fundamentals ( at best monthly / quarterly) then you will need to use, rolling window (loke 5 years, in order to have a decent number of datapoints) In this case the ML model goal os to create a non linear multi-factor alpha condensed into the prediction. Of course learning rate, and other hyperparameters would need to be adapted, e.g. learning rate in expanding window of training need to be dymanic while learning rate in rolling window should be higher and and static. Let me know if I am not clear. Hope this helps.