发表机构
Purdue University; Seoul National University（普渡大学; 首尔国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ReLOBGen通过从当前挂单中选单并掩蔽无效令牌，在生成时保证限价订单簿消息的可回放性，无需事后修正，实现100%回放性并提升市场真实性，速度较基线提升2.7-3.6倍。
AI 中文摘要
我们提出了ReLOBGen，一种生成限价订单簿（LOB）消息的方法，这些消息在构造上即可回放。回放能力是闭环市场模拟所必需的，然而现有的LOB消息生成器可能产生不可回放的原始消息，即与当前市场状态不一致的消息。因此，这些生成器依赖于事后修正或拒绝后重采样，这可能会改变回放消息的分布或增加推理成本。ReLOBGen则在生成过程中确保回放性：它从当前LOB中的挂单中选择被引用的订单，然后生成剩余的消息字段，使其与该订单及市场状态保持一致。为了实现逼真的引用选择，ReLOBGen从学习到的合格挂单分布中采样，该分布通过缓存的订单表示和上下文相关的查询高效计算。随后，它通过掩蔽无效令牌来强制剩余字段的一致性。这些组件共同使得无需事后修正或重采样即可高效生成逼真的消息。在500条消息的展开中，ReLOBGen实现了100%的回放性，提升了市场真实性，尤其是对于盘口最优报价统计和LOB消息的相对价格，并且每条回放消息相比LOBS5基线提供了2.7至3.6倍的加速。
英文摘要
We propose ReLOBGen, a method for generating limit order book (LOB) messages that are replayable by construction. Replayability is required for closed-loop market simulation, yet existing LOB message generators may produce non-replayable raw messages, i.e., messages inconsistent with the current market state. These generators therefore rely on post-hoc correction or rejection followed by resampling, which may alter the replayed message distribution or increase inference cost. ReLOBGen instead ensures replayability during generation: it selects the referenced order from the resting orders in the current LOB and then generates the remaining message fields to be consistent with that order and the market state. For realistic reference selection, ReLOBGen samples from a learned distribution over eligible resting orders, efficiently computed from cached order representations and a context-dependent query. It then enforces the consistency of the remaining fields by masking out invalid tokens. Together, these components enable efficient generation of realistic messages without post-hoc correction or resampling. In 500-message rollouts, ReLOBGen achieves 100% replayability, improves market realism, particularly for top-of-book statistics and the relative prices of LOB messages, and provides a $2.7\text{-}3.6\times$ speedup per replayed message over the LOBS5 baseline.
