Design And Implementation Of Stock Intelligence Forecasting System Based On Deep Network

The stock market is affected by various factors such as economic markets,political,which lead to the complex of its internal changes.With the rapid development of China's stock market and the expansion of investor scale,a large amount of transaction data of the stock market has been generated,from which it's difficult to obtain valuable information.The deep neural network has certain advantages in dealing with large amount of data and complex nonlinear mapping.Therefore,based on deep network technology,an intelligent stock forecasting system has been designed.The main work is summarized as follows:(1)Stock selection model: The problem of multi-impact factor quantitative stock selection is studied based on the stock financial indicators and stock change trend,and a stock trend identification algorithm is proposed to construct stock selection model.According to the improved stock trend identification algorithm,the method identifies the trend of the stock selection indicators set by the financial indicators and technical indicators,and it utilizes the principal component analysis method to deal with the stock selection indicators,then it inputs the processed stock data into the BP neural network to train the stock selection model.Compared with the existing stock selection methods,the proposed stock selection model has improved the accuracy of the stock selection by an average of 5.09%.(2)Stock price forecasting model: Because of the large amount of stock trading data and the complex nonlinear relationship,it forecasts stock price based on LSTM(Long Short Term Memory)deep neural network.We select investable stocks from the output of stock selection model.By studying the factors affecting stock price changes,we select stock trading basic data and stock technical indicator data to form the stock training data.The LSTM network is then used to construct the stock price forecasting model,and the parameters such as the effect time series length and network structure are optimized.Compared with the stock price forecasting model constructed using the basic stock trading data,the coefficient of determination of the stock price is improved by 2.4%,and the root mean square error is reduced by 0.12.(3)Designing and implementing of intelligent stock forecasting system: Based on stock selection model and stock price forecasting model,the intelligent stock forecasting system is designed and implemented.Django framework,Scikit-learn machine learning library and Keras deep learning library are used to build the intelligent stock forecasting system and complete the various functions of this system.The system can perform stock picking and stock price forecasting in real time,providing investors with meaningful investment decision advice,therefore,the risk of investors' investment is reduced and the high investment returns are obtained.