← 研踪 PaperTrailarXiv:2606.25986 · v2
The Inference-Compute Frontier for Limit Order Book Prediction
首次提交 2026-06-24 23:54
版本更新 2026-11-02 23:24
雷达观测 2026-11-04 23:41
摘要
We identify an empirical inference-compute frontier for limit order book prediction on FI-2010. A power law fitted to low- and mid-compute models extrapolates to an excluded MLPLOB-style mixer family whose work is $56$--$2{,}515$ times the largest budget in the corresponding fit. Across nine folds and five horizons, pooled $R^2$ is $0.934$ on the realized target frontier, with 59\% lower RMSE than constant endpoint extrapolation, and $0.731$ on all excluded operating points. Measured latency gives a weaker relationship and reorders model families. We therefore design FastBiNLOB to pack computation into dense temporal and feature mixing. H120 exceeds the published state-of-the-art macro-F1 on two of four horizons in every one of five seeds. Under our CPU/MPS protocol, H96 reduces median latency by 23.6\% relative to the W128 MLPLOB reference, and H120 by 60.2\% relative to a TLOB-inspired dual-axis attention reference.
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v2 · 2026-11-02 23:24 · 最新观测版本
The Inference-Compute Frontier for Limit Order Book Prediction
We identify an empirical inference-compute frontier for limit order book prediction on FI-2010. A power law fitted to low- and mid-compute models extrapolates to an excluded MLPLOB-style mixer family whose work is $56$--$2{,}515$ times the largest budget in the corresponding fit. Across nine folds and five horizons, pooled $R^2$ is $0.934$ on the realized target frontier, with 59\% lower RMSE than constant endpoint extrapolation, and $0.731$ on all excluded operating points. Measured latency gives a weaker relationship and reorders model families. We therefore design FastBiNLOB to pack computation into dense temporal and feature mixing. H120 exceeds the published state-of-the-art macro-F1 on two of four horizons in every one of five seeds. Under our CPU/MPS protocol, H96 reduces median latency by 23.6\% relative to the W128 MLPLOB reference, and H120 by 60.2\% relative to a TLOB-inspired dual-axis attention reference.
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