Uncertainty aware mid price forecasts improve crypto trading signals
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
Artificial Intelligence
Summary
Knowing not just what a price will likely do next but how sure we are about it helps traders make better decisions. The authors introduce UQ-LOB, a tool that can attach to any existing model analyzing crypto trading data to estimate how confident it is about short-term price changes. This helps filter out unreliable predictions and focus on the ones most likely to be right. Their tests on massive real-world data showed the approach better identifies when predictions can be trusted, improving accuracy especially for bigger price moves over seconds.
What this means in practice
- •For quantitative trading teams: Enhance algorithmic trading by using predictions that include confidence scores to decide when to act on crypto price moves.
- •For financial data platform developers: Integrate uncertainty-aware forecasting modules that provide confidence metrics for short-term price movement predictions in live financial data services.
Authors
Derrick Gilchrist Edward Manoharan, Eljas Linna, Kestutis Baltakys, Hao Dong, Juho Kanniainen
Abstract
Forecasting short-horizon mid-price movements from limit order book (LOB) data is central to algorithmic trading, yet most deep LOB forecasters are point predictors: they output a direction or a displacement, but never indicate which of their forecasts can be trusted. We introduce UQ-LOB, a lightweight, encoder-agnostic uncertainty quantification module that attaches to any pretrained LOB encoder and, in the spirit of attentive neural processes, conditions each forecast on a context set of recently completed windows whose outcomes are already realised. The UQ-regression variant outputs a calibrated Gaussian over the future tick displacement, while the UQ-classification variant outputs a categorical distribution over down/up/stationary. Both expose a scalar confidence (predicted signal-to-noise ratio or class probability) that supports selective prediction. On 5.2 billion LOB events across seven cryptocurrency assets and horizons of 5, 10 and 15 seconds, UQ-regression attains near-nominal 68% interval coverage, and restricting to the most confident 10% of predictions raises directional macro F1 by 0.11-0.15 for UQ-regression and 0.05-0.11 for UQ-classification, at every horizon. On large, economically meaningful moves, the tightest confidence tier reaches a directional F1 of 0.88 (down) and 0.83 (up) at the 5-second horizon.