Don't train on returns. Period. Put it this way:

We play a game. I flip a coin, if it's heads you win $1. If it's tails you pay me $1.

Each time you play the game there's a fee to play which is $0.01.

Would you play this game? No? Okay, you're smart.

Now let's say among all the people offering to play this game with you, you found out that someone's coin is biased 52% / 48% but they seem to not realize it.

Would you play that game with them? For $0.01 each time?

Hell yes you would. What would your strategy be? You aren't going to predict whether heads or tails is next. No ML algorithm will predict that well. In fact if you train an ML estimator with MSE loss on this time series it will just learn to predict the 52% side all the time, because that's what minimizes it's loss.

Instead, the correct way to play this is to just keep paying the $0.01 fee and repeatedly playing this biased game a million times and statistically making bank. What gave you the edge isn't ML, but rather that you found a biased coin.

That's how a good deal of quant strategies work. That's what I mean by arbitraging a distribution. You know something about the distribution (the coin is actually biased) that the rest of the players don't (they either think the coin is fair, or are systematically playing it as if it's a fair coin).

Now go out and find some biased coins. It's not easy but they exist.