Automatic Stock Profit Maximization via Deep Reinforcement Learning and Correlation Analysis

In this paper, we propose a novel Deep Deterministic Policy Gradient (DDPG)-based stock profit maximization approach with enhanced predictability and strategic decision-making by integrating correlation analysis between stocks. The proposed algorithm is designed to consider user budgets as well as evaluated to utilize real-world stock market data. Lastly, the performance of the proposed method is evaluated and verified that our proposed method outperforms the others.