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Vol. 5 No. 1 (2024)
2024 Edition

Articles

Transformer-Based Anomaly Detection in High-Frequency Trading Data: A Time-Sensitive Feature Extraction Approach

Published 2024-08-22

  • Yining Zhang
    • ,
  • Guancong Jia
    • ,
  • Jiayan Fan

Yining Zhang
Applied Data Science, University of Southern California, CA, USA

Guancong Jia
Computer Science, Rice University, TX, USA

Jiayan Fan
Information Science, University of Michigan, MI, USA

Abstract

This paper presents a novel Transformer-based approach for anomaly detection in high-frequency trading data that leverages time-sensitive feature extraction techniques. The proposed method addresses the unique challenges of financial time series data, including high dimensionality, complex temporal dependencies, and the critical importance of timely detection. We introduce a specialized time-sensitive feature extraction framework that captures patterns at multiple time scales, integrated with a modified Transformer architecture featuring a self-feedback mechanism. This mechanism enhances detection sensitivity for subtle anomalies by reinforcing attention on potentially anomalous patterns. Comprehensive experiments on five high-frequency trading datasets from diverse markets demonstrate that our approach achieves superior performance compared to state-of-the-art methods, with an average F1 score of 0.90 and a 51-72% improvement in detection speed. Ablation studies confirm the significant contributions of the time-sensitive feature extraction and self-feedback components. The model's effectiveness is further validated through case studies on real-world trading anomalies, including flash crashes, spoofing patterns, and momentum ignition strategies. The computational efficiency of the approach enables real-time deployment in trading surveillance systems while maintaining high detection accuracy.

Keywords

High-frequency trading, anomaly detection, transformer models, time-sensitive feature extraction

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