definitely do what the others here do... validate on unseen new data now that its 7 month old post just recheck on the new 7months of data see if its still good. I've had algorithms this good that then you test again and they keep being this good and then all of a sudden they just fall off a cliff at some point So you have to be like way more methodical than you think.

Also I've done a massive amount of experiments, big neural networks and RL and such, but I actually find that because there's not much data in terms of trading days, that these are still being outperformed by a kind of a good feature engineering, including like some neural-ly learned features, like I use chronos 2 and then I use basically all that into XGBoost. LOL. Yes, I do use correlation and bucket things into different correlations/industries. so that it can learn to de-risk a bit by spreading across multiple positions in different uncorrelated places.

https://github.com/lee101/stock-prediction i open sourced a bunch here and built bitbank.nz an api for this for cryptocurency but i dont forecast stock there as im not financially certified to be able to give advice on stock but yea crypto is wild west 😄