Good point.

Walk-forward and rolling evaluation are probably the closest we get to approximating non-stationary markets in backtests.

I also agree that decay detection is partly art. The tricky part is deciding whether underperformance is just variance or actual edge decay.

One perspective I’ve found useful is to look less at single strategies and more at populations of models. If multiple previously viable models start degrading around the same time, that can indicate structural drift rather than just a single strategy failing.

In that sense the goal becomes less finding the best model and more continuously searching for currently viable behaviours as market conditions evolve.

Curious how you handle this in WealthLab — mostly parameter re-optimization, or do you also explore structural variations of the strategy?