Research on the Application of Deep Learning in Data Processing of Financial High Frequency Trading

High-Frequency Trading (HFT) in finance is characterized by its high speed, frequency, and short holding periods, playing a crucial role in modern financial markets while also facing challenges such as high data noise, high dimensionality, and complex dynamics. To address these challenges, this paper proposes a hybrid model combining Long Short-Term Memory networks (LSTM) and Convolutional Neural Networks (CNN), optimized with Deep Reinforcement Learning (DRL) for trading strategies. LSTM is utilized to capture long-term trends and cyclical features in market data, whereas CNN extracts local features of short-term price fluctuations. Experimental results validate the superiority of this hybrid model in predicting financial market movements, particularly excelling in classification tasks with high accuracy and robustness. Compared to traditional machine learning methods like Support Vector Machines (SVM) and Random Forests, deep learning (DL) models handle complex patterns and nonlinear relationships within high-dimensional time series data more effectively. Furthermore, the optimization through DRL leads to more intelligent trading strategies, enhancing the overall system performance. This study provides significant reference and insights for the application of DL in handling HFT data in the financial domain.