sadly - I find that blog article empty. nicely written, good theory overview, near zero practical value
It provides basic guidelines. Don't expect effective 'recipes' in this secretive area.
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> enough trades (say 50+)
that's like couple days
to my mind - tells nothing
That's just a number to give an indication, just like reserving 10-25% from your data set for OOS testing.
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> not a guarantee for robustness
I don't believe there is any
True, but I provide you some guidelines to increase the likelihood. Up to you what do with that, it's your broker account.
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> This largely depends on the complexity of your approach, the more complex, the higher the likelihood of over-fitting.
already mentioned - think it's much more important when working with machine learning
> Believe me, even with 10 years of data with 1000+ trades you can have over-fitting
I know it's there. just don't think it's that easy to pick it out by delaying tests on some particular data
Yes, this is esp true with ML, but you can basically over-fit any method. I don't know about the complexity of your approach, but those in-sample stats are way too optimistic.
