Multi-type data fusion framework based on deep reinforcement learning for algorithmic trading

In recent years, research on algorithmic trading based on machine learning has been increasing. One challenge faced is getting an accurate representation of the stock market environment from multi-type data. Most existing algorithmic trading studies analyze the stock market based on a relatively single data source. However, with the complicated stock market environment, different types of data reflect the changes in the stock market from different perspectives, and how to obtain the temporal features of different types of data and integrate them to obtain a deeper representation of the stock market environment are still problems to be solved. To tackle these problems, in this study, we combine deep learning and reinforcement learning (RL) and propose a multi-type data fusion framework with deep reinforcement learning (MSF-DRL) that integrates stock data, technical indicators and candlestick charts, in which technical indicators can reduce the impact of noise in stock data. In the process of learning trading strategies under the MSF-DRL framework, the temporal features of stock data and technical indicators are extracted through a long short-term memory (LSTM) network, and a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) are successively used to extract the features of the candlestick chart. The fused features are used as the input of the RL module, which makes trading decisions on this basis. To verify the effectiveness of the MSF-DRL framework, we conducted comparative experiments on datasets composed of Chinese stocks and some stocks of the S&P 500 stock market index. Compared with the other trading strategies, our trading strategy can obtain more profits and a higher Sharpe ratio.