Originally Posted by Lapsa
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.

In- and out-of-sample
[Linked Image]

Out-of-sample
[Linked Image]

Last edited by Grant; 02/05/22 12:02.