Application Of Deep Reinforcement Learning In Portfolio

Asset management is a broad field,and portfolio is one of the most concerned issues in this field.Investors will disperse fixed assets to different investment products in proportion,hoping to obtain maximum returns while controlling risks,and choose the best scheme among various investment schemes.It is difficult to use traditional computing tools and financial models to get the optimal investment plan in the unstable financial market.With the continuous development of deep reinforcement learning in recent years,many researchers have proved that using deep reinforcement learning to make a portfolio can get good results.In order to obtain the optimal investment scheme and maximize investors’ returns,this paper adopts the classic model of deep deterministic strategy gradient(DDPG)algorithm based on deep reinforcement learning.On this basis,the algorithm model is verified and optimized to carry out portfolio management and maximize the cumulative returns of asset investment.The research content of this paper is as follows,(1)Study the performance of multiple Alpha factors in DDPG modelA DDPG algorithm model with a variety of Alpha factors is built as the input feature of the deep reinforcement learning model,so that the agent can consider the environment information more comprehensively when making decisions,so as to improve the accuracy of decision-making.Analyze the influence of adding Alpha factor on portfolio returns,compare the return performance of the portfolio after adding different factors,and get the Alpha factor suitable for integrating into DDPG model.(2)Construct DDPG algorithm model based on multi-head self-attention mechanismA DDPG algorithm model based on multi-head self-attention mechanism is constructed to increase the importance weight of key features of input data,so that the algorithm can better capture the interactive relationship of features.The addition of multiple attention mechanisms can explain and quantify the importance of each feature,which helps to enhance the representation of the model.The comparison experiment proves that the improved algorithm has good effect on the portfolio.(3)Design and implement the intelligent portfolio management system platformDesign and implement portfolio management system.The system completes the implementation of the two algorithms in the portfolio management system platform.The system is tested using cases through functional testing and performance testing to verify that the system meets the main functions of the portfolio and can guarantee the operability of the system services.