发现订单簿隐性恶化阶段，提前预测市场压力。
Early Detection of Latent Microstructure Regimes in Limit Order Books
- 构建三阶段生成模型，识别隐性恶化期。
- 平均提前18.6个时间步预警，精度100%。
- 适合高频交易与系统风险监控者。
订单簿可能从稳定快速转入压力状态，但传统预警信号如订单流失衡和短期波动率本质上是反应性的。我们通过一个三阶段因果生成过程（稳定 → 隐性积累 → 压力）形式化这一局限，其中隐性恶化阶段在可观测压力出现前提供了预测窗口。在温和的时间漂移与状态持续性假设下，我们证明了隐性积累阶段的可识别性，并推导出严格正的期望提前量及非平凡的早期检测概率。提出一种基于触发机制的检测器，结合最大值聚合、上升沿条件与自适应阈值。在200次模拟中，该方法实现平均提前量+18.6±3.2个时间步，精度完美，覆盖中等，优于经典变点与微观结构基线。对一周比特币/美元订单簿数据的初步应用显示一致正提前量，而基线仍为反应式。结果在低信噪比和短积累阶段有所下降，符合理论预期。
原文摘要 · Abstract (English)
Limit order books can transition rapidly from stable to stressed conditions, yet standard early-warning signals such as order flow imbalance and short-term volatility are inherently reactive. We formalise this limitation via a three-regime causal data-generating process (stable $\to$ latent build-up $\to$ stress) in which a latent deterioration phase creates a prediction window prior to observable stress. Under mild assumptions on temporal drift and regime persistence, we establish identifiability of the latent build-up regime and derive guarantees for strictly positive expected lead-time and non-trivial probability of early detection. We propose a trigger-based detector combining MAX aggregation of complementary signal channels, a rising-edge condition, and adaptive thresholding. Across 200 simulations, the method achieves mean lead-time $+18.6 \pm 3.2$ timesteps with perfect precision and moderate coverage, outperforming classical change-point and microstructure baselines. A preliminary application to one week of BTC/USDT order book data shows consistent positive lead-times while baselines remain reactive. Results degrade in low signal-to-noise and short build-up regimes, consistent with theory.
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