How We Handle Model Drift in a Market That Never Sits Still
Markets change regimes. Our models retrain nightly, but that is not enough. We built a drift detection system that triggers emergency retraining when feature distributions shift beyond thresholds.
Our models retrain every night on the latest 90 days of data. For most market conditions, this is sufficient. But markets shift regimes — volatility spikes, correlations break down, liquidity dries up — and a model trained on yesterday's data can give dangerous signals today.
We built a drift detection layer that monitors the distribution of each input feature in real time. When the Kolmogorov-Smirnov statistic for any key feature exceeds a threshold relative to the training distribution, we trigger an emergency retrain.
The system runs on a 15-minute sliding window. Feature distributions are compared against the training set using KS tests, and we maintain a drift score that's an exponentially weighted average of recent KS statistics. When the drift score crosses 0.15, we queue an emergency retrain that completes in under 20 minutes.
Since deploying this system, we have caught three significant regime shifts before they caused material signal degradation: the March 2026 liquidity event, a correlation breakdown between tech and energy sectors, and a sudden volatility compression in crypto markets.
The key design decision was making it automatic. Manual monitoring does not scale, and by the time a human notices drift, the damage is already done. The 20-minute retrain window means we lose at most one or two signal cycles before the model adapts.