Stock Trading Signal Filtering Based on Bagging-RF-LR Model

Buy and sell signals in stock trends are related to the yield of investors. In this paper, trading signal filtering is regarded as a binary classification problem, and a stock trading signal filtering model based on Bagging, Random Forest(RF) and Logistic Regression(LR) is proposed. Firstly, the trading information of different indexes in the stock market is mined according to the selected attributes. Secondly, the optimal number of features is selected according to the comparative experimental results. Finally, a multi-classifier ensemble model is built, which based on Bagaging-RF -LR. The trading signals are put into the model, and the soft voting method is used to learn and classify the data. The experimental results show that the classification accuracy of the ensemble model reaches 61%, which is 1 % $\sim$ 2 % higher than that of the single classification model, and the mean ration increases from 145.19 % $\sim$ 166.48 % to 171.01%. The comparison of the experimental results shows that the Bagaging-RF-LR model is effective and has a good classification effect on the trading signal filtering problem.