UQ-LOB：给限价订单簿预测加上可信度评估的不确定性量化模块
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
做量化交易的同学可以看看，这个模块能告诉模型哪些预测可以信，最有信心的 10% 预测 F1 直接到 0.88。
UQ-LOB 是一个轻量级不确定性量化模块，可附加到任意预训练 LOB 编码器上，为每个预测输出校准的置信度。在 7 个加密资产、52 亿条 LOB 事件的数据上，UQ-regression 在 5、10、15 秒三个预测窗口上达到接近名义值的 68% 区间覆盖率。只取最有信心的 10% 预测时，方向性 macro F1 提升 0.11-0.15（回归变体）和 0.05-0.11（分类变体）。在 5 秒窗口的大幅价格变动上，最高置信档的方向性 F1 达 0.88（下跌）和 0.83（上涨）。
UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting
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.
