Deep Reinforcement Learning Portfolio Model Based on Dynamic Selectors
In recent years,portfolio management problems have been extensively studied in the field of artificial intelligence,but there are some improvements in the existing quantitative trading methods based on deep learning.First of all,the prediction model of stocks is single,usually a model only trains a trading expert,and the decision of trading is only based on the prediction results of the model.Secondly,the data source used in the model is relatively single,only considering the stock's own data,ignoring the impact of the entire market risk on the stock.Aiming at the above problems,a reinforcement learning model based on dynamic se-lection predictor(DSDRL)is proposed.The model is divided into three parts.Firstly,the characteristics of stock data are extrac-ted and introduced into multiple predictors.Multiple prediction models are trained for different investment strategies,and the cur-rent optimal prediction results are obtained by dynamic selector.Secondly,the market environment evaluation module is used to quantify the current market risk and obtain the appropriate proportion of investment amount.Finally,based on the first two mo-dules,a deep reinforcement learning model is established to simulate the real trading environment,and the actual portfolio strate-gy is obtained based on the predicted results and the proportion of investment amount.In this paper,the daily k-line data of China Securities 500 and S & P 500 are used for test verification.The results show that the proposed model is superior to other refe-rence models in Sharpe rate and other indicators.
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