Anomaly Pattern Recognition and Risk Control in High-Frequency Trading Using Reinforcement Learning

Abstract

This paper presents a novel reinforcement learning approach for anomaly pattern recognition and risk control in high-frequency trading environments. Market manipulation schemes have evolved significantly, requiring advanced computational methods for detection and mitigation. We introduce a comprehensive framework integrating kernel-based dimensionality reduction techniques with sequential deep learning architectures to identify complex manipulation patterns across multiple time scales. Our approach employs multivariate statistical methods for outlier detection while incorporating temporal dependencies through specialized neural network structures. The risk-aware reinforcement learning system optimizes trading policies with explicit consideration of downside risk, utilizing dynamic threshold adjustment mechanisms that adapt to evolving market conditions. We implement multi-objective reinforcement learning to balance return maximization with risk minimization, enabling customizable risk-return profiles aligned with specific investor preferences. Experimental validation on extensive financial market datasets demonstrates superior performance compared to traditional methods, achieving 92% detection accuracy with false positive rates below 3%. The proposed framework demonstrates particular robustness during periods of elevated market volatility, reducing maximum drawdown by 28.5% while maintaining competitive returns. The integration of interpretable components enhances regulatory compliance and trader acceptance in production environments.