The Algorithm Of Srock Recommendation System

With the continuous expansion of China's capital market,more and more companies are publicly issuing stocks in the market and raising funds.Similarly,along with the continuous development of the national economy,the income level of residents continues to increase,and more and more residents will purchase stocks as an important means of investment and financing.However,China's stock investment market is still dominated by individual investors.Stocks are selected from a large number of stocks and recommended to individual investors,especially investors who lack investment experience and corresponding professional knowledge to provide decision support.This paper classifies the recommendation methods of stocks,and clearly proposes personalized recommendation methods and non-personalized recommendation methods.The personalized recommendation method is to learn from the personalized recommendation algorithm in the commodity field and transplant it into the stock personalized recommendation field for an investor.It is difficult to obtain private data and recommended stocks based on personal investment historical data and personal risk preference characteristics,may not be able to achieve good market performance,so the focus of this paper is Non-personalized recommendation for all investors.This paper focuses on the traditional methods of non-personalized recommendation,and divides it into two types: traditional non-personalized recommendation methods and quantitative non-personalized recommendation methods.In the field of non-personalized recommendation,stock selection and timing are always important issues that cannot be avoided in the stock investment process.In the process of constructing stock recommendation recommendation method,this paper adopts the basic idea of GARP strategy,selects the representative benchmark combination of CSI 800 Index,and selects effective single factor from many indicators,and then constructs a Multi-factor combination,and I used sensitivity analysis of the portfolio recommendation effect of the combination to further optimized the selection process of the portfolio.After sorting out the data mining classification algorithm and analyzing the principle of SVM algorithm in detail,this paper proposes a timing model based on support vector machine algorithm.Specifically,the daily frequency data of the CSI 800 Index is selected and processed,and the independent variables are set to classify and predict the dependent variable of the CSI 800 index.After empirical test,it is proved that the recommendation algorithm model of this stock selection has a good effect in the field of stock non-personalized recommendation.