论文标题
一种新型的深入增强学习基于自动股票交易系统的使用，使用级联的LSTM网络
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks
论文作者
论文摘要
越来越多的股票交易策略是使用深厚的强化学习（DRL）算法构建的，但是最初在游戏社区中广泛使用的DRL方法并不能直接适应具有低信噪比和不均衡性的财务数据，因此遭受了绩效缺点。在本文中，为了捕获隐藏的信息，我们提出了使用级联LSTM的基于DRL的股票交易系统，该系统首先使用LSTM从库存日常数据中提取时间序列功能，然后将提取的功能馈送到代理商进行培训，而策略在加强方面还使用其他LSTM进行培训。在美国市场和中国股票市场的DJI的实验表明，我们的模型在累积回报和敏锐的比率方面优于先前的基线模型，并且在中国股票市场（一个合并的市场）中，这一优势更为重要。这表明我们提出的方法是建立自动股票交易系统的有前途的方法。
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.
