Evaluation of Deep Reinforcement Learning Based Stock Trading

Stock is one of the most important targets in investment. However, it is challenging to manually design a profitable strategy in the highly dynamic and complex stock market. Modern portfolio management usually employs quantitative trading, which utilizes computers to support decision-making or perform automated trading. Deep reinforcement learning (Deep RL) is an emerging machine learning technology that can solve multi-step optimal control problems. In this article, we propose a method to model multi-stock trading process according to reinforcement learning theory and implement our trading agents based on two popular actor-critic algorithms: A2C and PPO. We train and evaluate the agents on two datasets from 2010–2021 Chinese stock market multiple times. The experimental results show that both agents can achieve an annual return rate that outstrips the baseline by 8.8% and 16.8% on average on two datasets, respectively. Asset curve and asset distribution chart are plotted to prove that the policy the agent learned is reasonable. We also employ a track training strategy, which can further enhance the agent’s performance by about 7.7% with little extra training time.