关键词: 股票交易策略, 深度强化学习, 模仿学习, 行为克隆, 对决DQN

Abstract:

In order to improve the return of stock investment and reduce the risk, this paper introduces the idea of behavior cloning in imitation learning into the deep reinforcement learning framework to design a stock trading strategy. In the process of strategy design, the dueling deep Q-learning (DQN) algorithm and behavior cloning are combined, which enables the agent to imitate the decision of pre-constructed investment expert while exploring autonomously. A numerical experiment is conducted on selected stocks from different industries, which illustrates that the designed trading strategy is superior to the comparison strategies in terms of the return and risk metrics such as the annualized percentage yield (APY), Sharpe ratio (SR), and Calmar ratio (CR). The research result shows that combining imitation learning and deep reinforcement learning enables the agent to simultaneously have the abilities of exploration and imitation, and thus improves the generalization ability of the model and the applicability of the strategy. 

Key words: stock trading strategy, deep reinforcement learning, imitation learning, behavior cloning, dueling deep Q-learning network (DQN)