Stock Price Prediction Based on Relative Strength Index, Moving Average Convergence Divergence and XGBoost Model

Stock price prediction is crucial in shaping investment strategies, facilitating risk management, and aiding decision-making processes for both investors and financial institutions. Based on the trading data of ZTE's stock from 2015 to 2022, this paper utilizes XGBoost model to forecast the stock price and compares it with the actual value. The research includes calculation of exponential moving average and simple moving average, analysis of relative strength index, calculation of moving average convergence divergence, and application of XGBoost regression model for stock price prediction. By comparing the predicted value with the actual value, the prediction ability of XGBoost model is verified. The results of this study provide a feasible stock price prediction method for investors and financial institutions, and have important reference value for formulating investment strategies and managing risks.