发表机构
NVIDIA（英伟达）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究从聚合订单簿中部分识别FIFO执行，证明不同取消规则可导致被动执行结果显著差异，并建议执行策略需进行FIFO敏感性分析。
AI 中文摘要
价格档位限价订单簿（L2）数据揭示了聚合流动性，但未揭示价格-时间优先级所需的有序队列。因此，即使观察到的价格、数量和交易保持不变，被动执行回测也可能依赖于一个未观察到的取消分配规则。我们将从聚合快照中恢复按订单历史记录的问题框架化为一个条件部分识别问题：多个历史记录可以重现相同的聚合路径。在保持该路径、对账的市场移除、潜在订单分区和新增订单固定的情况下，我们的路径保持编译器仅在取消分配的前端、数量加权随机和后端规则之间变化。在该编译器类别内，我们建立了一次触及价格期间虚拟标记订单的前端-后端填充顺序。我们研究了2025年东京证券交易所七个月的同步数据，涉及两种交易活跃度不同的工具：RIC 1301.T和RIC 7911.T。十级L2快照提供簿状态，而L1交易通过对账推断市场移除和挂单侧。每个工具在相同的18个保留交易日中贡献了1,080个匹配的五分钟片段。激进基准在FIFO实现中保持不变，但被动执行对取消规则敏感。对于1301.T，前端而非后端取消使终止前完成率提高8.01个百分点，并将实现缺口减少1.010个基点。对于7911.T，相应的差异为7.39个百分点和0.384个基点。因此，观察上等价的聚合簿路径可能意味着经济上不同的被动执行结果。从聚合数据评估的执行策略应附带FIFO敏感性分析，而不是基于不可观察队列假设的单点估计报告。
英文摘要
Price-level limit order book (L2) data reveal aggregate liquidity but not the ordered queue required by price--time priority. Passive-execution backtests can therefore depend on an unobserved cancellation-allocation rule even when observed prices, quantities, and trades are held fixed. We frame recovery of market-by-order histories from aggregate snapshots as a conditional partial identification problem: multiple histories can reproduce the same aggregate path. Holding that path, reconciled market removals, latent order partitions, and additions fixed, our path-preserving compiler varies only cancellation allocation among front, quantity-weighted-random, and back rules. Within this compiler class, we establish front--back fill ordering for a virtual tagged order during one touch-price spell. We study seven months of synchronized 2025 Tokyo Stock Exchange data for two instruments with different trading activity: RIC 1301.T and RIC 7911.T. Ten-level L2 snapshots provide book states, while L1 trades permit inference of market removals and resting side through reconciliation. Each instrument contributes 1,080 matched five-minute episodes over the same 18 held-out trading days. The aggressive benchmark is invariant across FIFO realizations, but passive execution is sensitive to the cancellation rule. For 1301.T, front rather than back cancellation raises preterminal completion by 8.01 percentage points and reduces implementation shortfall by 1.010 bps. For 7911.T, the corresponding differences are 7.39 percentage points and 0.384 bps. Thus, observationally equivalent aggregate-book paths can imply economically different passive-execution outcomes. Execution policies evaluated from aggregate data should be accompanied by FIFO sensitivity analysis rather than reported as single-point estimates based on an unobservable queue assumption.
Comments9 pages, 1 figure, 2 tables
