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OrderFlowDiT: Transport-Regularized Diffusion Transformer for Probabilistic Limit-Order Supply Forecasting

Jung Hun Phee, Tucker Balch · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.22727 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

我々は、現在の注文書と最近の注文流量を考慮して、次の5分間に提出された限界注文量の条件分布を研究する。 課題は、価格の配置、総量、および予測不確実性を共同でキャプチャする必要がある。 調整方向の拡散損失は、価格の移動を明示的に考慮しませんが、単位量の標準化は、総量に関する情報を削除します。 選択された総合統計に関する合意はまた、共同フィールド配布に忠誠を確立しません。 我々は、不規則なオファーのための条件の拡散トランスフォーマーであるOrderFlowDiTを導入し、30 U.S を超える輸送条件の正当化に関する初期の正確な比較で、これらのモデルは、

Machine translation (Meta M2M100); the English original follows.

Abstract

We study the conditional distribution of limit-order volume submitted over the next five minutes, given the current order book and recent order flow. The task requires capturing price placement, total volume, and predictive uncertainty jointly. Coordinate-wise diffusion losses do not explicitly account for price displacement, while normalizing fields to unit mass removes information about total volume. Agreement on selected summary statistics also does not establish fidelity to the joint field distribution. We introduce OrderFlowDiT, a conditional Diffusion Transformer for unnormalized bid and ask volume-at-price fields, trained with an unbalanced optimal-transport (UOT) auxiliary loss. We also develop a full-field evaluation framework that combines a transport cost sensitive to price placement and total volume with proper scoring rules and calibration diagnostics. In the primary comparison on 1950 validation conditions across 30 U.S. equities, adding UOT reduces full-field transport cost by 45.8% and the transport energy score by 26.1% relative to training with mean squared error alone. Both measures improve for every stock. These findings support incorporating price geometry and volume into generative modeling, with field accuracy and predictive uncertainty evaluated together.

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Topics: Scoring rule, Computer science, Probabilistic logic, Calibration, Bayesian probability, Joint probability distribution

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