Reinforcement Learning-Optimized Trading Strategies: A Deep Q-Network Approach for High-Frequency Finance

High-frequency trading (HFT) in financial markets requires sophisticated strategies to swiftly react to market dynamics and exploit profitable opportunities. This study aims to develop a novel trading strategy for high-frequency finance using reinforcement learning techniques, specifically Deep Q-Networks (DQN). The objective is to optimize trading decisions in real-time, leveraging historical market data and learning from past experiences to maximize profitability. Introduce a powerful and adaptable framework that can recognize intricate patterns and make the best trading decisions in dynamic market conditions by utilizing DQN. Experimental results demonstrate the effectiveness of the proposed Deep Q-Network approach in optimizing trading strategies for high-frequency finance. Our study highlights the potential of reinforcement learning, specifically Deep Q-Networks, in revolutionizing high-frequency trading strategies. Provide a framework that can learn on its own and optimize trading decisions by utilizing artificial intelligence. This will provide a competitive edge in the fast-paced financial markets of today.