A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks
This work addresses performance shortcomings in automated stock trading systems for financial markets, but it is incremental as it builds on existing DRL and LSTM methods.
The paper tackled the challenge of adapting deep reinforcement learning to financial data with low signal-to-noise ratios by proposing a system using cascaded LSTM networks, achieving higher cumulative returns and Sharpe ratios compared to baseline models in US and Chinese markets, with more significant gains in the Chinese market.
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.