Innovative Portfolio Optimization Using Deep Q-Network Reinforcement Learning

Traditional algorithmic trading strategies often struggle to adapt to changing market conditions, leading to suboptimal portfolio management. This paper addresses the problem of optimizing stock portfolio management using Deep Q-Network (DQN) reinforcement learning. Authors implement a DQN model to automate buy, hold, and sell decisions across a diversified portfolio of high-profile stocks. Historical stock data is used to train and evaluate the model through simulated trading scenarios. The results show that the DQN model can effectively manage portfolio allocation and maximize returns over various time periods. Specifically, the DQN agent achieved a notable 11.24% ROI with TQQQ, demonstrating its capability to adapt to different market conditions and optimize trading strategies. Our study offers valuable insights and practical methodologies for applying deep reinforcement learning in financial markets. Furthermore, the findings highlight areas for future improvement, including the incorporation of more sophisticated market indicators, alternative data sources, and advanced reinforcement learning algorithms to enhance model robustness and adaptability.