Practical Deep Reinforcement Learning Approach for Stock Trading
This work addresses stock trading optimization for investment companies, but it is incremental as it applies an existing deep reinforcement learning method to a specific financial domain.
The authors tackled the problem of optimizing stock trading strategies in a complex market by applying deep reinforcement learning, achieving superior performance over the Dow Jones Industrial Average and a traditional min-variance portfolio in terms of Sharpe ratio and cumulative returns.
Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.