Modern generative models for limit order books (LOBs) can reproduce realistic market dynamics, but remain fundamentally passive: they either model what typically happens without accounting for hypothetical future market conditions, or they require interaction with another agent to explore alternative outcomes. This limits their usefulness for stress testing, scenario analysis, and decision-making. We propose \textbf{DiffLOB}, a regime-conditioned \textbf{Diff}usion model for controllable and counterfactual generation of \textbf{LOB} trajectories. DiffLOB explicitly conditions the generative process on future market regimes--including trend, volatility, liquidity, and order-flow imbalance, which enables the model to answer counterfactual queries of the form: ``If the future market regime were X instead of Y, how would the limit order book evolve?'' Our systematic evaluation framework for counterfactual LOB generation consists of three criteria: (1) \textit{Controllable Realism}, measuring how well generated trajectories can reproduce marginal distributions, temporal dependence structure and regime variables; (2) \textit{Counterfactual validity}, testing whether interventions on future regimes induce consistent changes in the generated LOB dynamics; (3) \textit{Counterfactual usefulness}, assessing whether synthetic counterfactual trajectories improve downstream prediction of future market regimes.

翻译：现代限价订单簿（LOBs）生成模型能够复现真实的市场动态，但其本质上仍是被动的：它们要么仅模拟通常发生的情况而未考虑假设的未来市场条件，要么需要与另一智能体交互以探索替代结果。这限制了它们在压力测试、情景分析和决策制定中的实用性。我们提出 \textbf{DiffLOB}，一种基于状态条件的 \textbf{扩散}模型，用于 \textbf{限价订单簿}轨迹的可控及反事实生成。DiffLOB 显式地将生成过程条件于未来市场状态——包括趋势、波动性、流动性和订单流失衡，这使得模型能够回答如下形式的反事实查询：“如果未来市场状态是 X 而非 Y，限价订单簿将如何演变？” 我们为反事实限价订单簿生成构建的系统性评估框架包含三个标准：(1) \textit{可控真实性}，衡量生成轨迹在多大程度上能复现边际分布、时间依赖结构和状态变量；(2) \textit{反事实有效性}，检验对未来状态的干预是否在生成的限价订单簿动态中引发一致的变化；(3) \textit{反事实实用性}，评估合成的反事实轨迹是否能改进对未来市场状态的下游预测。