  More and more stock trading strategies are constructed using deep
reinforcement learning (DRL) algorithms, but DRL methods originally widely used
in the gaming community are not directly adaptable to financial data with low
signal-to-noise ratios and unevenness, and thus suffer from performance
shortcomings. In this paper, to capture the hidden information, we propose a
DRL based stock trading system using cascaded LSTM, which first uses LSTM to
extract the time-series features from stock daily data, and then the features
extracted are fed to the agent for training, while the strategy functions in
reinforcement learning also use another LSTM for training. Experiments in DJI
in the US market and SSE50 in the Chinese stock market show that our model
outperforms previous baseline models in terms of cumulative returns and Sharp
ratio, and this advantage is more significant in the Chinese stock market, a
merging market. It indicates that our proposed method is a promising way to
build a automated stock trading system.
