Signal Integrity in High-Frequency Financial Inference
Financial ML has a problem that most domains don't: the model's own predictions affect the data distribution it's trained on. A model that correctly identifies an alpha signal will eventually cause that signal to be arbitraged away. If you don't account for this, your validation metrics are systematically optimistic.
We spent 18 months building a production inference system for a tier-1 quantitative trading desk. Here's what we learned.
The Feedback Problem
Standard ML assumes i.i.d. data. Financial time series is anti-i.i.d. — serial correlation, regime changes, and reflexivity (Soros's term) mean that the distribution at inference time is causally downstream of the model's previous predictions.
This manifests as overfitting to stale alpha: a model trains well, goes live, makes money, and within 60–120 days has arbitraged its own edge away.
Our Approach
We built a feedback-aware training loop that:
This isn't a novel idea — it's related to equilibrium modeling in game theory. The engineering novelty is making it tractable at the data scales involved (2TB of tick data, 8M simulated trades per round).
Results
Live alpha half-life improved from 73 days to 194 days. Sharpe ratio degradation over 6 months: -12% (vs. -58% for baseline).