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Researches advanced in financial trading systems based on reinforcement learning

The financial trading system has always been a hot application field in the reinforcement learning community, which aims to predict market trends and maximize profits. However, due to the complexity of the financial market environment, it is still a challenging topic to construct a model that adapts to different market environments based on reinforcement learning. In this paper, we provide a comprehensive survey of recent advances in the field of financial trading systems according to the different design strategies of reinforcement learning, which mainly includes the algorithms based on q-learning and algorithms based on actor-critic. We further analyze the performance of representative methods on common data sets and summary the existing problems of reinforcement learning in financial trading systems and raise some possible improvements in the future.