An Empirical Research On The Investment Strategy Of Stock Market Based On Deep Reinforcement Learning Model

The stock market in the entire financial market plays a very important role in how to obtain an effective trading signal in the stock market,and the stock market investment has been to explore the topic.In paper,we first summarize the theory and model of Deep Reinforcement Learning,and then construct the stock market Deep Reinforcement Learning model from the perspective of the construction of decision-making mechanism of automated stock market trading based on the Deep Learning and Reinforcement Learning,combined with the model of the Deep Reinforcement Learning theory.It is effective to use the Deep Reinforcement Learning model to construct the strategy in the stock market investment strategy.The results of the evaluation results show that the Deep Reinforcement model is effective to the KD index transaction,The transaction model constructed by the Deep Reinforcement Learning can be applied to the construction of investment strategy.At the same time,it is found that the strategy of this paper is a large profit strategy,which needs to diversify the investment to reduce the investment risk and realize the big profit.This paper constructs a stock market investment strategy model based on Deep Reinforcement Learning,and validates the validity of the model through empirical data,and reveals the inherent logic of Deep Reinforcement Learning in the construction of stock market investment strategy.This has made a useful reference for the construction of investors' investment model,the construction of stock market investment strategy,the application of artificial intelligence in financial investment field and the improvement of investor strategy yield.