Mql5 Machine Learning: Linear Regression For Algo Trading

A complete guide to developing linear regression based models for algorithmic trading in MQL5

What you'll learn

The concept of Linear Regression and its application in Algorithmic Trading

How to Develop a Linear Regression model on a spread sheet

How to code a Linear Regression model Indicator in MQL5

How to develop a Linear Regression Strategy and code an Expert advisor in MQL5

Requirements

Basics of MQL5

Description

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. We shall then develop our linear regression model as an mql5 indicator by coding it using the mql5 programming language. After that, we shall develop our trading strategy as an mql5 expert advisor coded using the mql5 algorithmic trading language. We shall use the linear regression model we created as an indicator to analyze data and find patterns we can use to profit from the market. We shall base our trading logic on the fact that if price goes beyond one or two standard deviations from its predicted or expected price, it has to reverse and go back to its expected price. Hence our strategy will be a mean reversion type of strategy.For those that are still finding their way with MQL5, as long as you understand the basics of MQL5, this course is for you. We will patiently guide you through every step of the strategy development process and walk you through every line of code we shall craft. Hopefully, by the end of the course, you will have gained the necessary skills to code similar models and trading strategies and be able to appreciate how linear regression models can be an asset in developing your own trading ideas based on the ideas that shared in this course.So hit hard on that enroll button now and join me in this incredible journey of coding a linear regression model using the mql5 algorithmic trading language.

Overview

Section 1: Introduction to Linear Regression

Lecture 1 What you will Learn

Lecture 2 Introduction to Linear Regression

Lecture 3 Choosing Dependent and Independent variables

Lecture 4 Generating Predictions

Lecture 5 Calculating the standard deviation

Section 2: Coding a Linear Regression Model Indicator

Lecture 6 Setting Indicator Properties

Lecture 7 Creating Indicator Buffers

Lecture 8 Checking for Data points value validity

Lecture 9 Setting buffer indices and plot labels

Lecture 10 Declaring local variables

Lecture 11 Calculating the gradient and y intercept

Lecture 12 Regression Line calculations

Section 3: Coding a Linear Regression model Based Expert advisor

Lecture 13 Linear Regression Mean Reversion Strategy

Lecture 14 General EA parameters

Lecture 15 Changing the calculation interval

Lecture 16 Creating Trade Objects

Lecture 17 Getting indicator values

Lecture 18 Regulating trade count

Lecture 19 Calculating the Position Size

Lecture 20 Generating EA Signals

Lecture 21 Executing Trades

Lecture 22 Calculating the average entry price

Lecture 23 Closing positions

Lecture 24 Testing the strategy

Section 4: Conclusion

Lecture 25 Conclusion

Anyone willing to learn about the applications of Linear Regression in Market analysis and timeseries forecasting