Analyzing Deep Reinforcement Learning Strategies for Enhanced Profit Generation and Risk Mitigation in Algorithm Stock Trading

Stock trading holds a pivotal role for investors seeking to navigate the intricate world of financial markets with the dual objectives of maximizing profits and minimizing risks. Algorithmic trading, driven by Deep Reinforcement Learning, has emerged as a pivotal tool for automating trading operations. However, the dynamic and often turbulent nature of financial data poses formidable challenges to maintaining consistent performance. Formulating profitable trading strategies in such an environment remains an ongoing and substantial challenge. In response to these challenges, our research endeavors to establish an efficient stock trading system by harnessing the predictive prowess of Deep Q-Networks (DQN). Our DQN-based model has showcased outstanding performance, achieving a remarkable reward score of 1.125, thereby reinforcing its reputation for reliability and resilience in the stock trading domain. Significantly, our research highlights the consistent superiority of DQN over two established reinforcement learning methods, Proximal Policy Optimization (PPO) and Advantage Actor-Critic, particularly when applied to Apple stocks.