MQL5 MACHINE LEARNING 01: Neural Networks For Algo-Trading

In this course, our primary objective is to introduce you to the realm of Machine learning with neural networks using the most powerful algorithmic trading language, MQL5. Our aim is to give you a solid foundation to principles and concepts you will need in developing self optimizing softwares that learn from data the same way that the human brain learns.This course is structured for complete beginners to machine learning. There is no prior knowledge of statistics, linear algebra or complex mathematical understanding needed. You will be breast fed everything and we will simplify all processes and content without eliminating its value or impact in your learning.In this course, we shall first introduce you to data science and how it relates to artificial intelligence and machine learning. Then we shall take a closer look at machine learning and the types of models involved in machine learning processes. I shall then briefly introduce you to the world of Neural networks, the types of neural networks commonly used in algorithmic trading and the processes involved in designing a neural network model.To get an idea of the concepts and processes involved in neural network calculations, training and prediction, we shall build a very simple neural network in excel from scratch and train it to identify a buy signal from the RSI indicator and Moving average. This will be very useful in helping you understand the foundation of supervised learning with neural networks, enabling you to follow through the MQL5 coding process with ease.In this course, we shall use matrices and vector data types instead of simple arrays to store most of our data. So we shall introduce you to these new datatypes from scratch by looking at their declaration, their initialization and how to manipulate them.We shall then code a neural network on MQL5 from scratch, which aims to find hidden patterns in the RSI and Bollinger band indicators that are suggestive of a bullish market or a bearish market. We shall do th