Stock Exchange Market Performance and Data Analysis Techniques by using Machine Learning Approach

The financial price forecast is an effort to assess the long-term price of an organization’s portfolio or an instrument. Technical research examines the existing market trend using stock charts to predict the possible future path of the market in the sector. The success of the company is measured through data from the USA Stock Exchange companies. Many machine learning methods are possible, but extreme gradient boosting (XGBoost) is one of the most common methods used in this research. The term XGBoost refers to the engineering goal of increasing the computation capital limits for expanded tree algorithms. The USA-based stock exchange dataset provides a history of more than 16 years of stock exchange prices collected day by day. To draw a volume map that displays the number of stocks exchanged every day. This research has clarified the significance of OHLC and volume maps. The technical indicators are utilized to calculate and visualize predictions like Simple Moving Averages (SMA), Relative Strength Index, MACD, shift label columns, drop invalid samples, drop unnecessary columns, split into features and labels, and fine-tune the XGBoost Regressor.