Topic: How does the "experts" avoid overfitting?
I've been messing around many weeks with the concept. I tried everything but ultimately I always end up with overfitted strategies. Take a look at this.
This is a portfolio of top 50 strategies:
From 1 Jan 2022 to 1 June 2025
30% of the generation is OOS, meaning that from 1 June 2024 to 1 June 2025 it's OOS data

It seems to be pretty good, 1 year of OOS solid results. Now suppose we run live this portfolio from 1 June 2025 to 5 Dec 2025:
These are the classic results you get from a stategy that was overfitted during the In-Sample. Actually, the act of choosing the best OOS strategies is just a another way of overfitting, no different than tuning the periods of an indicator to make the equity curve look better.
I've tried every combination of IS-OOS that came to my mind, but ultimately trough EA studio I only came up with overfitted strategies that starts to perform bad as soon as they are introduced to true OOS data.
Does anyone have the same problem? How do you solved?
Thanks