MQL5 MACHINE LEARNING: Linear Regression for Algo Trading

Simple linear regression is a statistical method used to model the relationship between two variables: an independent variable (x) and a dependent variable (y). It assumes a linear relationship between the two variables and aims to find the best-fitting straight line that represents this relationship. The equation for a simple linear regression model is:y = ax + bWhere:y is the dependent variable (the variable we want to predict).

x is the independent variable (the variable used to make predictions). a is the slope of the line, representing the rate of change of y with respect to x. b is the y-intercept, representing the value of y when x is zero.

While simple linear regression is a statistical technique, it can also be considered as a machine learning algorithm. In machine learning, the goal is to build models that can learn from data and make predictions. Linear regression fits this framework because it learns the relationship between x and y from a given dataset and uses this learned relationship to make predictions for new data points.

As neural networks learn the best non-linear relationships between data by finding the weights that best fit the data, linear regression aims to find the best values of a and b that best describe the linear relationship between variables. In this course, our aim is to build a linear regression model in mql5 that seeks to predict the closing prices of a currency pair given its specific bar index. We shall start by creating a linear regression model on a spread sheet to basically explain the calculations involved in creating a linear regression model.