通过仿真数据精准识别订单簿中临时流动性消失的机械性原因。
When Quotes Crumble: Detecting Transient Mechanical Liquidity Erosion in Limit Order Books
- 用代理模拟器构建真实市场动态，生成可追踪的流动性衰减数据。
- 神经模型相比规则基线提升36%的AUC，跨不同市场条件表现稳定。
- 适合量化交易、市场监管与高频交易系统优化的研究者使用。
我们研究电子限价订单簿中瞬时流动性侵蚀（“报价崩塌”）的检测问题，其中可见的报价恶化可能源于机械性流动性撤回或信息性重新定价。利用ABIDES代理仿真器，我们构建了一个多代理环境，其中崩塌由做市商的随机状态切换引发，提供了真实市场数据中无法获得的时间分辨基准真值。我们开发了一套检测流程，基于订单簿特征识别机械驱动的报价衰减，并训练神经模型输出校准后的崩塌概率。实验表明，该框架能可靠地依据代理级真值识别崩塌事件，神经模型相比规则基线实现+36% AUC提升，在正常、高波动、牛市和熊市条件下均表现稳健。对时间特征及真值机制依赖结构的消融研究确认，该框架在独立与自相关流动性撤回动态下均具有泛化能力。
原文摘要 · Abstract (English)
We study the detection of transient liquidity erosion ("crumbling quotes") in electronic limit order books, where observable quote deterioration may reflect either mechanical liquidity withdrawal or informational repricing. Using the ABIDES agent-based simulator, we construct a multi-agent environment in which crumbling emerges from stochastic regime switches in a market maker, providing time-resolved ground truth unavailable in real market data. We develop a detection pipeline that identifies mechanically driven quote erosion using order book features, and train a neural model to produce calibrated crumbling probabilities. Experiments demonstrate that the proposed framework reliably identifies crumbling events against agent-level ground truth, with the neural model achieving +36% AUC improvement over rule-based baselines and robust performance across normal, high-volatility, bull, and bear market conditions. Ablation studies on temporal features and varying the dependence structure of the ground-truth mechanism confirm that the framework generalizes across both independent and autocorrelated liquidity withdrawal dynamics.
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