Real-time Anomaly Detection in Dark Pool Trading Using Enhanced Transformer Networks

This paper uses an enhanced transformer network architecture to present a novel approach to real-time anomaly detection in dark pool trading environments. Dark pools facilitate anonymous large-volume trades and require sophisticated surveillance mechanisms to maintain market integrity. We propose a specialized transformer-based framework integrating advanced attention mechanisms with optimized processing pipelines for efficient anomaly detection. The system incorporates modified self-attention patterns and specialized feature engineering techniques for high-frequency trading data. Our implementation demonstrates significant improvements in detection accuracy and computational efficiency compared to existing approaches. Experimental evaluation on a comprehensive dataset of 2.5 million trading records shows a detection accuracy of 97.8% while maintaining a low false positive rate of 0.8%. The system achieves a processing latency of 2.3ms, representing a 45.2% improvement over baseline models. The architecture demonstrates robust performance across various market conditions and trading volumes, making it suitable for production environments. Our research contributes to advancing financial market surveillance systems by establishing new performance standards for real-time anomaly detection in dark pool trading environments.