Mql5 Machine Learning 01: Neural Networks For Algo-Trading

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A firm and steadfast introduction to Machine Learning and Neural network application in Algorithmic trading with MQL5

What you'll learn
Introduction to Data science
Introduction to Artificial intelligence
Introduction to Machine learning
Coding Neural networks in MQL5
Training Neural Networks in MQL5

Requirements
MQL5 Beginner knowledge

Description
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 this by training our neural network using back propagation to identify and classify the market into bullish and bearish classes.Join us in this course and prepare to be astonished by the sheer power of neural networks. This course is not for the faint of heart, but for those who dare to explore the boundless frontiers of artificial intelligence. Prepare to be challenged, immersed, and captivated as you embark on this intellectual adventure.So Click that enroll button now!! And Unleash your curiosity,

Overview
Section 1: Overview of Machine learning
Lecture 1 Data science, Artificial intelligence and Machine learning
Lecture 2 Types of Machine learning
Lecture 3 Introduction to Neural Networks
Lecture 4 Feed Forward Neural Network Architecture
Section 2: Introduction to Neural Networks
Lecture 5 ForwardPass on a spreadsheet
Lecture 6 Mean squared error on a spread sheet
Lecture 7 Backward pass on a spread sheet
Lecture 8 Gradient descent on a spread sheet
Section 3: Vector and Matrix Datatypes
Lecture 9 Linear Algebra, Vectors and Matrices
Lecture 10 Declaring Matrices and Vectors
Lecture 11 Initializing Matrices and Vectors
Lecture 12 Copying Data into Matrices and Vectors
Lecture 13 Copying Timeseries Data into Matrices and Vectors
Lecture 14 Matrices and Vector Operations
Lecture 15 Manipulating Matrices
Section 4: Data Collection
Lecture 16 Neural Network Architecture
Lecture 17 General EA parameters
Lecture 18 Setting the Live calculation interval
Lecture 19 Creating Data Vessels
Lecture 20 Initializing Handles
Lecture 21 Collecting indicator Data
Lecture 22 Data Normalization
Lecture 23 Initializing Weights and Bias
Section 5: Forward Pass
Lecture 24 Converting Matrices to Vectors
Lecture 25 Converting Vectors to Matrices
Lecture 26 Neuron Calculations
Lecture 27 Forward Function
Section 6: Neural Network Training
Lecture 28 Searching for Patterns
Lecture 29 Removing an index from a Vector
Lecture 30 Removing Matrix Rows and Columns
Lecture 31 Confusion Matrix Declaration
Lecture 32 Populating the Confusion Matrix
Lecture 33 Model Accuracy and Precision
Lecture 34 Recall / Sensitivity Calculation
Lecture 35 Specificity calculation
Lecture 36 F1 Score calculation
Lecture 37 Support calculation
Lecture 38 Predictive Metrics averages
Lecture 39 Creating Data classes
Lecture 40 One Hot Encoding
Lecture 41 Loss Function Options
Lecture 42 Batch Forward Pass
Lecture 43 Back Propagation training
Lecture 44 Prediction Presentation
Lecture 45 Model Training
Section 7: Model Testing
Lecture 46 Displaying Probability Signals
Lecture 47 Visually testing the model
Lecture 48 Assignment
Section 8: Conclusion
Lecture 49 Conclusion
Anyone wishing to use machine learning in algorithmic trading

Homepage

https://www.udemy.com/course/mql5-machine-learning-01-neural-networks-for-algo-trading/

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