Why do ML strategies usually break during high vol periods?
u/SonRocky ·
Reddit — r/algotrading
· April 04, 2026 at 22:24
· ⬆ 15 pts
· 💬 30 comments
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Summary
The post analyzes why machine learning (ML) trading strategies often fail during high-volatility periods, such as geopolitical crises.
The author's thesis is that high-volatility periods represent a fundamentally different market regime where optimized models fail because: 1) they underweight crisis data in training, 2) feature relationships break, and 3) market dynamics like liquidity change.
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Everyone has been hit with the volatility spike since the recent war started. We saw a lot of large HFs and MMs that are usually consistent showing unusual drawdowns in the past month. Now the main question is what breaks during high volatility periods?
The consistent ML strategy that has been working for years seems to be taking a hit, and it's not because of what you think. It's not because it's lacking data or that it can't handle volatillity.
It has seen the last Iran war and maybe even the COVID crisis. But when training a strategy on a large period of time, the optimizer does its job and optimizes for the entire dataset. Which means that even if it was trained on the high volatility period, it accounts for only a small fraction of the training data, so its effect on the final model is negligible.
Another problem is that those carefully curated features and feature relationships that the model has been using just broke the moment the IV and RV spiked. It's not that it's noisier, but the relationships and signals themselves changed during this time period.
Furthermore, even if you survived all of these changes, and the model signal still has predictive power, the market dynamics themselves changed. Spreads widened, liquidity changed, so a once profitable positive EV strategy is now in the red.
The market during a crisis isn't a louder version of the normal market, it's a different market entirely. The problem is that most models were never designed to know the difference.