Monte Carlo robustness and strategy stability

I might be wrong here, but from what I can tell, robustness using monte carlo is just another way at looking at stability.

i.e. strategies that have very high stability (near 1) have very high robustness, strategies that are not stable (the equity is not very straight) have poor Monte Carlo results.

Am I missing something?

if you mean randomize trade orders then yes, it can be said. but aslo i have many “straight” strategies which fail this test also.

I use two different Monte Carlo tests, from 2 different pieces of software – SQ and NT, at different points in my work flow.

1 MC in SQ, as part of a few different robustness tests (inc. same symbol 10 minutes more and less TF, then different, but correlated symbol on same TF). In this MC on SQ, I’m looking to see – is the maximum DD (in the MC test) greater than the profit on the same TF. So, looking for maximum DD risk in robustness to still be profitable vs the normal retest on that TF.

The, once it’s passed many OOS retests, multiple different robustness tests (as above, inc. previous Monte Carlo) and a WF (I do it manually, and the one in SQ, which always seems buggy for me, but the manual WF is the recheck), I do another MC test on the NT platform, but look at it slightly differently. Here, I’m looking to see if it’s 95% likely to be profitable (per NT) over the last rolling 365 days.

For the MC test in SQ, I’m trying to make it fail, so have almost everything selected. It’s a pain in the a$$ to have strategies fail after I load them on NT, so really only want to be doing that on strategies I’m super confident on. BUT, still happens that after many retests, robustness tests, and WF, when it’s loaded on NT, it still sometimes fail the testing on NT, before incubation (and once they’re incubating…pfff ugh they fail waaay more than they pass to live).