通过分层对比学习，从订单簿中识别多层级操纵行为
Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning
- 构建级联订单簿表示架构，结合对比学习捕捉多层级异常模式
- 在多种模型上提升检测性能，基于Transformer的模型达最新水平
- 适用于金融风控、算法交易等需要复杂时序异常检测的场景
基于交易的操纵（TBM）严重破坏金融市场公平与稳定。其中，欺骗性操纵是最隐蔽且具有欺骗性的策略之一，其异常模式在多层级价格间呈现复杂分布，常被简化为单层级操纵。这些模式通常隐藏于限价订单簿（LOB）丰富的层次化信息中，因高维度和噪声难以有效利用。为此，我们提出一种结合级联LOB表示架构与监督对比学习的表征学习框架。大量实验表明，该框架在多种模型上均显著提升检测性能，基于Transformer的模型达到当前最优效果。此外，我们进行了系统性分析与消融研究，深入探讨多层级操纵特性及关键组件贡献，为复杂时间序列数据的表征学习与异常检测提供了更广泛洞见。
原文摘要 · Abstract (English)
Trade-based manipulation (TBM) undermines the fairness and stability of financial markets drastically. Spoofing, one of the most covert and deceptive TBM strategies, exhibits complex anomaly patterns across multilevel prices, while often being simplified as a single-level manipulation. These patterns are usually concealed within the rich, hierarchical information of the Limit Order Book (LOB), which is challenging to leverage due to high dimensionality and noise. To address this, we propose a representation learning framework combining a cascaded LOB representation architecture with supervised contrastive learning. Extensive experiments demonstrate that our framework consistently improves detection performance across diverse models, with Transformer-based architectures achieving state-of-the-art results. In addition, we conduct systematic analyses and ablation studies to investigate multilevel manipulation and the contributions of key components for detection, offering broader insights into representation learning and anomaly detection for complex time series data.
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