In this Research,ten representative technical indexes are generated by processing Ping An Bank stock data,and the values of technical indexes are preprocessed as the input of multiple linear regression based on machine learning,BP neural net-work and LSTM neural network respectively,training through the models to predict the rise and fall of the stock,and then compare the performance of the three models in the prediction accuracy and the calculation of annualized rate of return,it is confirmed that the LSTM neural network model has the best effect on the prediction of the nonlinear stock trend.Then a trading timing strategy based on LSTM pattern classification is designed to obtain a higher annual rate of return,and it's a more viable strategy,Finally,it is feasible to use the LSTM model to carry out quantitative trading.
国家科技期刊平台
NSTL
万方数据
