Optimizing Securities Investment with Deep Reinforcement Learning: A Comprehensive Approach

This paper presents a securities investment technique based on deep reinforcement learning and examines its effectiveness across multiple securities markets using historical data. The securities system is shaped by the actions of numerous investors, where each institution or individual investor adopts adaptive behaviors to achieve profitability. The interactions between each entity and the system, as well as between entities themselves, form a complex system. Reinforcement learning, a branch of machine learning, trains an agent's ability to achieve desired outcomes through the reinforcement of beneficial behaviors based on interactions between the agent and its environment. Due to its suitability for adaptation and control in complex systems, reinforcement learning is well-suited for training agents to participate in securities markets for profit. This paper proposes an algorithm that applies reinforcement learning to securities market investment, and experimental results demonstrate that the proposed algorithm can generate considerable returns.