Application Of Entanglement Theory In Stock Trend Analysis System

With the popularity of stock trading today and the close integration of the Internet and the financial industry,there is various stock trend analysis software available on the market.After studying the current mainstream stock software,it is found that few systems can provide users with constructive guidance on buying and selling for stock trend analysis.Therefore,stock trend analysis systems based on reliable analysis techniques to guide users’ trading tend to be studied.As a stock market analysis method that incorporates the advantages of various domestic and foreign analysis theories,entanglement theory has two major theories,morphology,and backward mechanics,and is clear and scientific enough to guide users in stock trading.The application and improvement of entanglement theory will enhance the usability of stock analysis systems.In this paper,we study the analytical theory and ideas of entanglement theory,summarize them into an algorithm,and implement them using Python language programming.Based on this algorithm,the trends of hundreds of stocks are analyzed and the buy and sell points of stocks are obtained.Based on this,this paper designs experiments to trade based on the entanglement theory of buy and sell points and finds out that most stocks can achieve better returns.Based on this,this thesis explores the possibility of applying the entanglement theory in a stock trend analysis system,builds a C/S architecture for stock trend analysis,and applies the technical knowledge of software engineering to optimize the system in both functional and non-functional aspects.In the process of studying the application of entanglement theory,this paper finds that there are still deficiencies in entanglement theory.The paper uses the ARIMA-CNN-LSTM model,which uses historical stock prices as training data,to predict stock price trends in advance,and then uses interval set theory to analyze buying and selling points,which optimizes the lag problem to some extent.In addition,the analysis means of entanglement theory can fail in some cases of buy and sell points.This paper evaluates the accuracy of buy and sell points,and based on the analysis of the characteristics of entanglement theory,a rule engine is developed,which allows users to add custom auxiliary rules to improve the accuracy of buy and sell points by using the backtest function to identify buy and sell points.This thesis investigates the entanglement theory and applies it to a stock trend analysis system,which has practical value in assisting investors’ trading decisions.