because you don't consider that my in-sample data is all the meaningful data there is
my out-of-sample data is tomorrow's failure
what else you want me to test it on?
different asset? generated white noise? Mozart's 40th symphony?
I will answer that by explaining what I did (without implying to be mr know it all!).
I've picked February & March 2020 as my in-sample period (1M data). Why? Because February had a relative low volatility, but March was sky high. So this short period contained at lot of valuable information. Then I ran a ton of backtests from February 2020 till May 2021, just to see how my models (I use ML, hence 'models') behave in- and out-of-sample. By doing so, you see exactly how easy it is to over-fit a model.