Best posts made by wool.dewgong

I am actually not sure how my strategy could be back tested since i need to train it multiple times on historical data, for example last 2000 days (and build 3d array of historical data), choose the best performing model,, then run it for sometime (a week for example), retrain it on fresh data, re-run etc.
Can you advise how to test model based on machine learning (in my case reinforcement learning)?
"Or do you mean an algorithm that uses a sliding window? Well, in this case, multi-pass backtesting suits."
What do you mean by that? Hiw multi pass works exactly and how do you ensure that forward testing is does on untrained data?

Is there a way to save and load machine learning models?
Specifically, using stable baselines(tensorflow) or stable baseline 3(pytorch)?