Design of stock quantitative trading algorithm based on deep reinforcement learning
In view of the difficulties of predicting future price trend and intelligent asset portfolio allocationwith limited da-tain quantitative stock trading,theDeepAR model was used to predict the futureprice trend of stocks.Based on these trends,16 promising stocks were selected based on their potential price changesand the SAC model was used for intelligent asset alloca-tion.The results showed thatthe stock selection of theDeepAR model can help the SAC model realize intelligent asset portfolio allocation and the quantitative decisions of the SAC model can also achieve desirable results.Over a period of 4month,a return rate of 10.79％and an annualized return rate of 32.37％were achieved.Compared withthe SSE Index andthe CSI 300 Index,there were significant excess return rates of 12.47％and 21.48％,respectively.In addition,a Sharpe ratio of 1.3％and a maxi-mum retracement rate of 29％were achieved in the 2016-2022 retest.
deep reinforcement learningquantitative tradingexcess returnstock forecast
万方数据
维普
