Latency-Aware Prediction Thresholds

Most classifier tuning treats the decision threshold as a static tradeoff between false positives and false negatives, chosen once from a validation set. A trading signal has an extra wrinkle: by the time a prediction clears the pipeline — feature computation, model inference, risk checks, and network round-trip to the exchange — the market may have already moved on from the condition that triggered it. A latency-aware threshold adjusts the cutoff for how much of the signal's edge is expected to have decayed by the time an order can land.

Why a fixed threshold breaks down

If a signal's edge halves every few milliseconds (a common shape for short-horizon microstructure signals), a system with slower infrastructure is effectively trading on an older, weaker version of the same prediction than a faster competitor would be. Using the same probability cutoff regardless of pipeline latency means a slow system fires on trades that have already lost most of their expected value by the time the order reaches the exchange, quietly eating into what looked like a profitable edge in backtests that didn't model latency at all.

The practical fix is to measure or estimate signal decay as a function of time, and then set the threshold as a function of the specific pipeline's measured latency rather than a single global number — a firm running two venues with different round-trip times may need two different thresholds off the same underlying model.