用扩散模型生成更真实的订单簿成交量快照，支持可控的流动性模拟。
DiffVolume: Diffusion Models for Volume Generation in Limit Order Books
- 基于扩散模型，结合历史成交量和时段信息生成未来成交量。
- 生成的快照更贴近真实数据的分布、空间相关性和自相关衰减特征。
- 可生成假设流动性场景下的反事实数据，提升预测模型性能。
限价订单簿（LOB）动态建模是市场微观结构研究的核心问题。尽管已有研究尝试使用生成对抗网络处理订单簿，但生成高维成交量快照并保留强时间依赖与流动性相关模式仍具挑战。本文提出一种条件扩散模型（DiffVolume），用于生成未来订单簿成交量快照。我们在三个维度评估模型：（1）真实性——在给定历史成交量与时间段条件下，模型更准确还原边际分布、空间相关性及自相关衰减；（2）反事实生成——通过额外引入目标流动性轮廓，实现对假设流动性情景的可控生成；（3）下游预测——合成的反事实数据可显著提升未来流动性预测模型性能。结果表明，DiffVolume为真实且可控的订单簿成交量生成提供了强大灵活的框架。
原文摘要 · Abstract (English)
Modeling limit order books (LOBs) dynamics is a fundamental problem in market microstructure research. In particular, generating high-dimensional volume snapshots with strong temporal and liquidity-dependent patterns remains a challenging task, despite recent work exploring the application of Generative Adversarial Networks to LOBs. In this work, we propose a conditional \textbf{Diff}usion model for the generation of future LOB \textbf{Volume} snapshots (\textbf{DiffVolume}). We evaluate our model across three axes: (1) \textit{Realism}, where we show that DiffVolume, conditioned on past volume history and time of day, better reproduces statistical properties such as marginal distribution, spatial correlation, and autocorrelation decay; (2) \textit{Counterfactual generation}, allowing for controllable generation under hypothetical liquidity scenarios by additionally conditioning on a target future liquidity profile; and (3) \textit{Downstream prediction}, where we show that the synthetic counterfactual data from our model improves the performance of future liquidity forecasting models. Together, these results suggest that DiffVolume provides a powerful and flexible framework for realistic and controllable LOB volume generation.
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