Stock Price Prediction using Random Forest Classifier and Backtesting

Stock price prediction is an attempt to determine the future price of company stock and(or) other categories traded on the National Stock Exchange[NSE]. A stock’s future price forecast that is accurate might result in a sizable profit. The prediction of the price of a stock is a very important topic considering that the raw data is ambiguous. Though many consider this to be an ideal machine learning problem, the solution for it is far from perfect. Many people are actively researching/funding research to derive a solution for this problem. Usually the methodologies for prediction fall into three broad categories which can (and might) overlap:1.Core research (Manual evaluation): Core research (or) Manual evaluation is a procedure by which investors of the stock market manually calculate the returns on their invested amount by estimating the stock price based on news articles, market movement, etc.2.Technical Research (Tracking): Technical Research (or) Tracking is a procedure in which the stock is monitored using a software application which can track the stock value according to historical data and displays relevant information for investors.3.Technical Techniques (Machine Learning): Technical Techniques (or) Machine Learning is a procedure by which an application is enabled to become more accurate at predicting outcomes. With the use of these techniques, investors can get a better-estimated prediction.We aim to use machine learning as a procedure and overlap it with a concept of tracking known as backtesting to provide stock price predictions.