A Method Of Finding Stock Buying Points Based On Big Data Analysis And Moving Average

Nowadays,the stock market is booming,more and more shareholders are engaged in the stock market,and the rapid development of the stock market is closely related to the national economy,so the research have great significance for the study of stock investment.How to make a large probability profit is a general concern of stock investors,and many investors attach great importance to technical analysis,the study of technical analysis has important theoretical and practical significance.Moving average is the most important and basic technical analysis tool.In the past,there were many works in using moving average to study trading points of the stock.Nowadays,big data research is very popular,and quantitative investment technology has attracted much attention.Inspired by the previous work and the importance of the average line,and conforming to the development of big data research and quantitative investment technology,this paper attempts to develop a quantitative investment technology through the establishment of an algorithm(RFPO)based on big data analysis and the use of moving average to find the stock buying point,and carries on some optimization research,develops an algorithm(algorithm 4 in this paper)to find optimizing the algorithm(RFPO)mainly aimed at two-parameter(r and v)in a big probability,and respectively compiles the MATLAB language running program of the algorithm(RFPO)and algorithm 4,and further explains the method and tests the efficiency of the algorithm through the experiment.The first chapter briefly describes the relevant background and significance,explains problems to be studied and research methods,as well as the writing structure of the article in this paper.The second chapter sketches the basic knowledge of stock and stock trading,especially the basic knowledge of the moving average and using it for technical analysis.The third chapter introduces several important works about using moving average to study stock trading.The fourth chapter shows the specific application of moving average in other technical means.The five chapter,firstly,carries on the mathematics plan and the brief analysis thought;secondly,designs the algorithm,gives the method that finds the buying point;finally,compiles the MATLAB language running program of the algorithm,through the experiment further explains the method and tests the efficiency of the algorithm.Chapter six,firstly,considers the optimization problem of single parameter r and v respectively,and gives the corresponding algorithm;then,considers the optimization problem of two-parameter(r and v)comprehensively,and gives an algorithm to find optimization parameter aimed at parameter(r and v)in a big probability,and through the experiment further explains the method and tests the efficiency of the algorithm.The seventh chapter summarizes the whole paper,points out the shortcomings of the article and raises issues that need further study.On the one hand,this study can provide some enlightenment for how to determine the buying point,how to make the big probability profit,and other stock trading issues;on the other hand,it can provide some valuable reference for further research on stock trading issues from the perspective of big data application,machine learning,quantitative technology and so on.