Research On Stock Market Index Trend And Inflection Point Prediction Based On CNN-GRU Algorithm

Stock time series data has strong complexity and huge amount of data.The traditional technical index analysis method or simple machine learning algorithm often can not obtain ideal analysis results.In this thesis,the characteristics of securities data are analyzed and processed,combined with technical indicators,the feature attributes of the stock data are extracted,and then the machine learning algorithm is used to deeply analyze and process the stock securities data,and better analysis results are obtained.This thesis mainly introduces the following contents:Aiming at the problem that stock price index trend is difficult to predict,this thesis proposes a research model of stock index trend prediction based on trend channel and CNN-GRU-Attention.Firstly,combined with the idea of piecewise linear representation fixed window PAA algorithm and top-down TD algorithm,a PTD(PAA-TD)linear piecewise representation algorithm is proposed to represent stock index data piecewise linearly and establish trend channel through stock high and low points.Then use the value evaluation method to filter out the invalid channels,sort the effective trend channels from high to low according to the value,retain the trend channel with the highest score,and build the trend channel identification model based on the value evaluation method.Finally,the medium and long-term trend prediction model CNN-GRU-Attention model is constructed to predict the medium and long-term trend of stock index data.Firstly,the basic models of RNN-Attention,LSTM-Attention and GRU-Attention are constructed by using the attention mechanism,the basic model GRU-Attention with the best performance is selected by comparison,and the deep features of stock time series data are extracted by CNN and sent to the GRU-Attention model.The stock index trend prediction model constructed in this thesis shows good prediction performance and high prediction accuracy.In view of the emergence of stock inflection point indicates that the stock trend is about to change,from upward trend to downward trend or from downward trend to upward trend.Based on the relationship between stock inflection point and stock trend,this thesis proposes an inflection point prediction model based on multi feature fusion of DT-CNN-GRU model.Based on the trend channel model,the characteristic data of stock trend attributes are extracted.On the basis of piecewise linear representation,the inflection point information of stock is extracted.It also extracts the stock technical index information that can reflect the trend attribute.Input the trend attribute features and technical index data features into the constructed classification algorithm to classify the effective inflection points and invalid inflection points,and input the results of Decision Tree DT algorithm with good classification effect into the constructed CNN-GRU model to predict the stock inflection points.The experimental results show that the inflection point prediction model based on DT-CNN-GRU model has high prediction accuracy and better prediction effect than the comparison model.