<ul><li>This paper presents a deep learning algorithm for anomaly detection in high-frequency trading data.</li><li>The algorithm utilizes a staged sliding window Transformer architecture to capture multi-scale temporal features.</li><li>Experimental results show that the proposed method outperforms traditional and deep learning approaches in terms of accuracy, F1-Score, and AUC-ROC.</li><li>The model provides important support for market supervision but suffers from false positives.</li></ul>

A Deep Learning Approach to Anomaly Detection in High-Frequency Trading Data

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