发现订单簿预测的计算量与预测误差存在幂律关系，据此设计低延迟高效模型。
The Inference-Compute Frontier and a Latency-Efficient Architecture for Limit Order Book Prediction
- 通过幂律拟合揭示计算量与预测损失的规律性关系。
- 提出FastBiNLOB模型，在更低延迟下超越现有最优指标。
- 适合关注高频交易系统性能优化的研究者和工程师。
我们研究了限价订单簿预测中是否存在类似缩放定律的推理-计算前沿。基于FI-2010数据集及从小型决策树到神经网络订单簿架构的一系列模型，发现预测损失与结构化前向计算量之间的实际经验前沿可用幂律很好地描述。特别地，将MLPLOB排除在外时，对低-中等计算量非MLPLOB前沿的幂律拟合可跨多个数量级外推，并在被排除的高计算量MLPLOB目标前沿上达到$R^2=0.941$。类似地在延迟空间进行的实验结果则显著较弱，表明延迟并非仅仅是噪声化的计算。利用这一差距，我们提出了FastBiNLOB，一种由硬件友好型时间与特征混合操作构建的密集轴分离式订单簿混合器。在五次种子实验中，FastBiNLOB在显著低于现有发表的SOTA架构的延迟下，超过了已发布的$y_{10}$和$y_{100}$宏观F1指标。
原文摘要 · Abstract (English)
We study whether a scaling-law-style inference-compute frontier appears in limit order book prediction. Using FI-2010 and a suite of models ranging from small decision trees to neural LOB architectures, we find that the realized empirical frontier of predictive loss versus structural forward work is well summarized by a power law. In particular, with MLPLOB held out as an architecture family, a power-law fit to the low- and mid-compute non-MLPLOB frontier extrapolates across multiple orders of magnitude and attains $R^2=0.941$ on the excluded high-compute MLPLOB target frontier. A similar exercise in latency space gives substantially weaker results, showing that latency is not merely noisy compute. We use this gap to motivate FastBiNLOB, a dense axis-separable LOB mixer built from hardware-friendly temporal and feature mixing operations. In a five-seed experiment, FastBiNLOB exceeds the published $y_{10}$ and $y_{100}$ macro-F1 targets at notably lower latency than existing published SOTA architectures.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。