Mql5 Advanced:Code Machine Learning Eas With Neural Networks

Posted By: ELK1nG

Mql5 Advanced:Code Machine Learning Eas With Neural Networks
Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.93 GB | Duration: 3h 44m

Learn to code a neural network into Expert advisors for a better machine learning based trading performance with MQL5

What you'll learn

How to code a neural network into an expert advisor

How to improve EA performance with Neural networks

How to code cost averaging into a neural network EA

How to train a neural network in real time

Requirements

Basic MQL5 knowledge

Description

The financial markets are becoming increasingly complex and fast-paced, making it challenging for traders to keep up with the latest trends and changing market conditions. This course is meant to boost the capabilities of every trader by solving critical challenges using the evolutionary power of machine learning.Machine learning empowers us with the ability to develop advanced algorithms which have unparalleled capabilities. With the power of advanced algorithms, you can gain a deeper understanding of market trends, identify profitable trades, and make data-driven decisions.Our course is designed to give you a practical understanding of how machine learning can be used in algorithmic trading with the MQL5 language. I will guide you through the basics of machine learning, including how it works, its key concepts, and the different types of algorithms. From there, we will explore how to apply these algorithms to algorithmic trading using the MQL5 language including how to collect and analyze market data, how to build predictive models, and how to use these models to make trading decisions.We will also cover the essential tools and techniques in a way that regardless of whether you're a beginner or an experienced trader, you will learn practical strategies to optimize your trading performance, minimize risks, and increase profits.Our course is not just about theory; we offer a hands-on approach, with plenty of real-world examples and case studies to illustrate how machine learning can be applied in trading. You will learn how to develop your own trading strategies, test them using historical data, and optimize them for maximum profitability.Finally, this course is taught by a leading expert in the field, with years of experience in both trading and programming. You can trust that you're learning from the best, and that the skills you acquire in this course will be highly valuable in today's competitive trading landscape.This course is the perfect way to take your trading skills to the next level. With practical insights, real-world examples, and the latest tools and techniques, you will be equipped to make better, data-driven trading decisions and achieve greater success. Click that enroll button now and let's get started on this exciting journey!

Overview

Section 1: Developing the RSI OBOS EA

Lecture 1 The RSI OBOS trading strategy

Lecture 2 Getting indicator values

Lecture 3 Setting up a custom chart appearance

Lecture 4 Creating objects from the CTrade class

Lecture 5 Counting the number of trades

Lecture 6 Coding entry signals

Lecture 7 Testing trade entry signals

Lecture 8 Trade entry

Lecture 9 Closing trades

Section 2: Introduction to machine learning

Lecture 10 Introduction to machine learning

Lecture 11 Machine learning categories

Lecture 12 Steps involved in developing a machine learning EA

Lecture 13 Introduction to Neural Networks

Section 3: Coding a Neural Network into the RSI OBOS strategy

Lecture 14 Setting up Neural network nodes and weights

Lecture 15 Data cleaning and preprocessing

Lecture 16 Hidden layer calculations

Lecture 17 Output layer calculations

Lecture 18 Testing the Neural network enhanced EA

Section 4: Cost Averaging trade management

Lecture 19 Preparing the global scope

Lecture 20 Adding cost averaging signals

Lecture 21 Entering cost averaging trades

Lecture 22 Testing the effect of cost averaging

Section 5: Continuous neural network training

Lecture 23 Coding a back propagation function

Lecture 24 Testing the back propagation enhanced EA

Section 6: Conclusion

Lecture 25 Conclusion

Anyone willing to add neural networks into their EAs.,Anyone willing to improve their EA performance with machine learning,Anyone willing to upgrade their MQL5 skills to the next level