Hi Moda, this is how I do optimizations:
First I optimize over the maximum data, ie the full 200k to get a picture of how viable an idea is. If the number of variables is low, then i’ll run a 7x WF with max variance. If the result is good then I’ll look at the values for the most recent 1 or 2 sectors and use those in another back test over the full 200k. This gives OOS performance for the recent months, plus the data from before the IS period.
If the algo has a lot of variables then I skip the 7x WF and just do one 70-30 WF over 25% of the data (50k), then back test @200k. If it still looks good, I run it through the Robustness Tester which you can read about here:
https://www.prorealcode.com/topic/day-month-year-strategy-robustness-tester/
If there are various sets of values that do well OOS then I pick the one that does best in the Robustness tester.
I would be v curious to know what method other people use to arrive at a final set of values.
