Stock Trading Strategies Based on Deep Reinforcement Learning

The purpose of stock market investment is to obtain more profits. In recent years, an increasing number of researchers have tried to implement stock trading based on machine learning. Facing the complex stock market, how to obtain effective information from multisource data and implement dynamic trading strategies is difficult. To solve these problems, this study proposes a new deep reinforcement learning model to implement stock trading, analyzes the stock market through stock data, technical indicators and candlestick charts, and learns dynamic trading strategies. Fusing the features of different data sources extracted by the deep neural network as the state of the stock market, the agent in reinforcement learning makes trading decisions on this basis. Experiments on the Chinese stock market dataset and the S&P 500 stock market dataset show that our trading strategy can obtain higher profits compared with other trading strategies.