Newest papers

Probabilistic Limit Order Book Forecasting with Conditional Diffusion Models

Junyu Chen, Franklin Liu, Moshu Xu, Lijian Yang · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.25495 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

模型限制间隔订单(LOB)动态是市场预测和执行策略评估的核心。虽然扩散模型报告强有力的经验表现,现有的LOB快照模型通常缺乏学过的重复编码器和正式的统计保证。我们引入LOBGrad,一个条件 DDPM框架,用于概率 LOB预测,该框架结合了自我退缩的RNN编码器和基于任务的扩散发电机。特别是,我们为单步DDPM噪音预测目标建立了过度风险的Oracle不平等。我们进一步展示了在合并动态下,对调节窗口长度的依赖可以减少到对潜在状态尺寸的依赖。在B/TCUSDT,GOOGL和META的实验中,我们展示了LETAG的关键

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

Abstract

Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation. While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees. We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator. In particular, we establish an excess-risk oracle inequality for the one-step DDPM noise-prediction objective under exponential mixing and regularity assumptions. We further show that, under contractive dynamics, the dependence on the conditioning-window length can be reduced to a dependence on the latent-state dimension. Experiments on BTC/USDT, GOOGL, and META show that LOBGrad improves on its encoder-free diffusion ablation across all reported queue metrics and obtains competitive results against other baselines. It also reproduces the key stylized facts of LOB microstructure, and yields reasonably calibrated queue-based predictive intervals. A stylized downstream task illustrates the potential value of the generated forecasts.

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Topics: Probabilistic logic, Stylized fact, Autoregressive model, Computer science, Gaussian, Statistical model

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