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Online Optimal Investment Portfolio Model Based on Deep Reinforcement Learning

Combining deep reinforcement learning with portfolio management is one of the research hotspots in the field of quantitative investment. In recent years, quantitative investment has become one of the research hotspots in the field of deep reinforcement learning. Combining portfolio management with deep reinforcement learning has become a hot research direction. The model in this paper combines the perception ability of deep learning and the online decision-making ability of reinforcement learning to construct an intelligent model of portfolio management based on deep reinforcement learning DDPG algorithm. This model addresses issues such as the limited ability of reinforcement learning models to perceive complex environments and the strong correlation between the internal features of state data, introducing the convolutional neural network into the DDPG model. The use of convolutional neural networks to process multi-dimensional state data improves the perception ability of the reinforcement learning model and solves the problem of data correlation. Therefore, the model in this paper can obtain the dynamic optimal investment portfolio within the specified trading time range. At the same time, this paper introduces the action vector into the state value representing the stock market environment, and solves the problem of static model. Experiments show that our model improves the income acquisition ability of the portfolio management model.