Researches advanced in financial trading systems based on reinforcement learning

Hengyue Zhu

doi:10.1117/12.2641867

10 November 2022 Researches advanced in financial trading systems based on reinforcement learning

Hengyue Zhu

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234824 (2022) https://doi.org/10.1117/12.2641867
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

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.

Citation Download Citation

Hengyue Zhu "Researches advanced in financial trading systems based on reinforcement learning", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234824 (10 November 2022); https://doi.org/10.1117/12.2641867

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