Research On A-share Investment Strategy Based On Integrated Learning Algorithm

With the rapid economic growth and the continuous reform and improvement of China's capital market,the A-share market will become more open and transparent in the future,and the impact of the policy market will gradually reduce.By the end of 2019,the total market value of Shanghai and Shenzhen markets was more than 60 trillion yuan,with more than 3800 listed companies and more than 150 million investors.The scale of A-share is growing rapidly,and huge amounts of financial data are generated every trading day.The stock market is unpredictable,and investors' energy is limited,so it is impossible to study the investment targets one by one.However,due to the rapid development of information technology,quantitative investment should come into being.Quantitative investment refers to the process of designing and implementing investment trading strategy based on mathematics,finance,statistics or machine learning algorithm by using computer program.The stock market fluctuates frequently,and the stock price is affected by many factors.The traditional linear model often has poor prediction effect.With the development of artificial intelligence,machine learning algorithm is gradually applied to quantitative trading.Integrated learning is a very classical machine learning algorithm,which has more advantages than single algorithm.This paper focuses on the A-share investment strategy based on the integrated learning algorithm.The main work includes extracting relevant stock data from the Join Quant quantitative trading platform,and selecting 50 variables that usually affect the stock price to build a feature factor library.Using the Python programming language,the Random Forest and Ada Boost are combined with feature factor library to construct an integrated learning algorithm stock selection model.Use the integrated learning algorithm to train the stock sample data,divide the sample data into training data sets and verification data sets,use the model to perform in-sample training and cross-validation adjustment on the selected data,and finally use the out-of-sample data for testing.Through the comparative testing of CSI 300 and CSI 500,and all A-share samples,optimize the setting of model parameters,and comprehensively evaluate the model based on the final test results,select the 10 stocks with the highest predicted rise probability to establish an investment strategy portfolio,And backtest the portfolio.In this paper,the theory of Random Forest and Ada Boost algorithm is analyzed and elaborated,and the model is evaluated by using accuracy,AUC and other indicators.After analysis,the stock selection model constructed by these two machine learning algorithms is suitable for A-share market.The selected portfolio has achieved good returns in the test range,and can outperform the market index,which has reference significance for investors and quantitative enthusiasts.