Design And Implementation Of Stock Market Analysis System Based On Entanglement Theory

The stock market analysis system refers to an information system that provides stock market information and can help investors effectively analyze the stock market.In the context of the deep integration of Internet technology and the financial industry,a variety of stock analysis softwares began to appear.Users can obtain massive stock market information on the Internet.However,through the research of many stock software,it is found that although most software can provide comprehensive data and parameters,they cannot provide effective trading advice.There is still a lot of research space for the combination of technical analysis method and stock system.Entanglement theory is a popular domestic financial market technical analysis method.It inherits and integrates the advantages of various foreign analysis theories and has a good practical effect on the investment market.Entanglement theory can not only serve as a scientific stock analysis idea to help users conduct technical analysis,but also provide investors with accurate buying and selling suggestions through its unique buying and selling point theory,which plays a very important auxiliary role in actual stock trading decisions.This thesis first summarizes the ideas of entanglement theory and implements it by programming after designing it into algorithms.At the same time,this thesis designs and develops a stock market analysis system on the basis of investigating the basic requirements of stock market investors,combined with the latest technology of the Internet,and taking entanglement theory as the core module.This system uses the C/S architecture,builds the client based on the PyQT5 framework,and uses the ECharts component to intuitively display the stock trend data and technical indicator data to users.At the same time,the Flask framework and APScheduler timing task framework are used to build back-end services to complete the real-time calculation of the entanglement theory algorithm.In order to ensure the scalability of the entanglement theory algorithm module,while implementing each algorithm unit,this thesis uses the template method pattern to optimize the entanglement algorithm architecture.In addition,aiming at the lag problem of entanglement theory’s trading points,this thesis completes the advance confirmation of entanglement theory’s trading points by using the ARIMA time series forecasting model.Furthermore,this thesis uses technologies such as Redis caching and HTTP long-polling to ensure concurrency and real-time performance in massive stock data scenarios,thereby improving user experience.In addition,this thesis tests and verifies the optimization effect of the time series model through experiments,and the results prove its feasibility in shortening the time delay of buying and selling points.At the same time,this thesis also conducts a comprehensive test of the system to verify the correctness of the function and the quality of the system performance.This thesis studies and designs and implements a system that combines the trading point of entanglement theory and the analysis of stock market conditions.The system has certain practical value in guiding investors’trading decisions.