Ensemble Algorithm to Speculate Stock Trend by Analyzing Technical Indicators on Historical Data

Stock market investment is a popular and often lucrative investment option, as the stock market is unpredictable and can change rapidly. Utilizing past stock prices and volume information, technical analysis looks for patterns and trends that can be used to forecast future price movements. One popular technique for technical analysis is the use of technical indicators. Ensemble algorithms have gained prominence in recent years as a result of their capacity to increase the stability and accuracy of prediction models. This research proposed an ensemble algorithm for speculating stock trends using technical indicators on historical data. The aim of this study is to develop a technique for forecasting stock value trends using an ensemble algorithm and classifying it among two categories namely Buy and Sell. Technical indicators such as Relative Strength Index, Moving Average Convergence Divergence, Moving Average, Rate of Change, Exponential Moving Average, Average True Range are used to predict the trend of stock.