PortPortfolio Management based on Deep Reinforcement Learning Method with Data Augment

Abstract

Most existing reinforcement learning (RL)-based portfolio management model do not consider the financial indicators. They more focus on the changes in prices and interest rates. In this paper, we propose a portfolio management strategy based on the framework of Deep Deterministic Policy Gradient (DDPG). To assess the effectiveness of our strategy, we compare DDPG using financial indicators with DDPG using only price as a feature, which simulate the best performance in the financial market. Experimentally, we select a set of stocks with low correlation and compare the accumulative portfolio value of our strategy with other strategies, including Uniform Buy and Hold, Exponential Gradient, and Universal Portfolios. Through experimentation, we find that our strategy outperforms most of the other strategies, with an accumulative portfolio value of 5.782. Furthermore, our strategy exhibits a Sharp Ratio of 2.064, which is only 5.2% lower than the best-performing strategy.