Review: the application of deep reinforcement learning to quantitative trading in financial market

As an effective learning paradigm to realize general artificial intelligence, deep reinforcement learning (DRL) has achieved significant results in a series of practical quantitative trading applications in financial market, becoming the mainstream method in this field. Firstly, a detailed introduction to the basic concepts and principles of deep reinforcement learning were provided. On this basis, a systematic review was conducted on the application and practical progress of DRL in quantitative trading in financial market, covering the application of different types of DRL, such as policy-based algorithm models, value-based algorithm models, and actor-critic algorithm models in quantitative trading in financial market. The advantages of DRL in quantitative trading in financial market were further explored, pointing out DRL could adjust trading strategies based on dynamic changes in the market environments to adapt to different market environments. Secondly, the challenges faced by DRL in quantitative trading in financial market were pointed out, including data quality issues, model stability issues, overfitting issues, etc. Finally, we outlooked the future development trend of DRL in the field of quantitative trading in financial market. It is believed that with the continuous optimization of algorithms and the improvement of computing power, DRL will play a more important role in the field of quantitative trading in financial market, providing more accurate and reliable support for investment decisions.