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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

Korean

우리는 현재 순서록과 최근 순서 흐름을 감안할 때 향후 5 분 동안 제출 된 제한 순서 볼륨의 조건부 분배를 연구합니다. 작업은 가격 배치, 총 볼륨 및 예측 불확실성을 공동으로 캡처하는 것을 요구합니다. 조정 방향의 배포 손실은 가격 이동을 명시적으로 계산하지 않으며, 단위 대량에 대한 필드를 표준화하면 총 볼륨에 대한 정보가 제거됩니다. 선택된 요약 통계에 대한 합의는 또한 공동 필드 배포에 대한 충실성을 확립하지 않습니다. 우리는 비정규화 된 입찰을위한 조건부 배포 변환기인 OrderFlowDiT를 도입합니다. 30 U.S.를 통한 볼륨 가격 필드의 유효성 조건에 대한 기본 정밀 비교에서 이러한 모델은

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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