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.

翻译：越来越多的股票交易策略采用深度强化学习（DRL）算法构建，但原本广泛应用于游戏领域的DRL方法难以直接适配金融数据低信噪比和不均匀性的特点，因此存在性能不足的问题。本文为挖掘隐藏信息，提出了一种基于级联LSTM的DRL股票交易系统：首先利用LSTM从股票日度数据中提取时序特征，随后将提取的特征输入智能体进行训练，同时强化学习中的策略函数也使用另一LSTM进行训练。在美国市场DJI和中国市场SSE50上的实验表明，我们的模型在累计收益率和夏普比率上优于先前基线模型，且这一优势在新兴市场——中国股票市场中更为显著。这表明我们提出的方法是一种构建自动化股票交易系统的有前景的路径。