I used to train one classifier on everything, bull, bear, and range. Now I train three. Separate models for each regime. Each has different label generation logic and different relevant features. For example, trend volatility means something in a bull market that it doesn’t in a chop. Yes, it’s more compute. But the lift in precision outweighs the overhead. Especially when you’re deploying to trade live, regime confusion is costly [[Period-Specific Models]] [[ML]] [[Classification]] [[Serendipity]]