Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview

Since we started documenting this EA in this series from the very first article Developing a trading Expert Advisor from scratch, it has undergone various changes and improvements while maintaining the same on-chart order system model. It is very simple and functional. However, in many situations, it is not suitable for real trading. Of course, we could add some things to the original system so that we have some information about orders, both open and pending. But this would turn our code into a Frankenstein which would ultimately make a real nightmare out of the improvement process. Even if you have your own methodology, it will be lost over time as the code gets too big and too complicated. So, we need to create a completely different system in terms of the order model we use. At the same time, it should be easy for the trader to understand, while providing all the information we need to work safely.

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• Developing a trading Expert Advisor from scratch (Part 18): New order system (I)
• Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
• Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II)
• Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)
• Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)
• Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)
• Developing a trading Expert Advisor from scratch (Part 12): Times and Trade (I)
• Developing a trading Expert Advisor from scratch (Part 11): Cross order system
• Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators
• Developing a trading Expert Advisor from scratch (Part 9): A conceptual leap (II)
• Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap
• Developing a trading Expert Advisor from scratch (Part 7): Adding Volume at Price (I)
• Multiple indicators on one chart (Part 06): Turning MetaTrader 5 into a RAD system (II)
• Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)
• Multiple indicators on one chart (Part 03): Developing definitions for users
• Multiple indicators on one chart (Part 04): Advancing to an Expert Advisor
• Multiple indicators on one chart (Part 02): First experiments

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If you have ever tried creating or modifying a complex indicator, you may know some of the problems that arise when increasing the amount of buffers: you'll need to declare tons of double arrays for the buffers, set them as buffers, configure all of them...
You can set as buffers arrays that are contained inside of objects
In this article, we'll explore the options that this trick will give us and come with solutions to be able to apply this trick in any other situation using Object Oriented Programming.

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We continue to study models and unsupervised learning algorithms. We have already considered data clustering algorithms. In this article, I will explore a solution of problems related to dimensionality reduction. Essentially, these are certain data compression algorithms that are widely used in practice. Let us study the implementation of one of these algorithms and see how it can be used in building our trading model.

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1. Neural networks made easy
2. Neural networks made easy (Part 2): Network training and testing
3. Neural networks made easy (Part 3): Convolutional networks
4. Neural networks made easy (Part 4): Recurrent networks
5. Neural networks made easy (Part 5): Multithreaded calculations in OpenCL
6. Neural networks made easy (Part 6): Experimenting with the neural network learning rate
7. Neural networks made easy (Part 7): Adaptive optimization methods
8. Neural networks made easy (Part 8): Attention mechanisms
9. Neural networks made easy (Part 9): Documenting the work
10. Neural networks made easy (Part 10): Multi-Head Attention
11. Neural networks made easy (Part 11): A take on GPT
12. Neural networks made easy (Part 12): Dropout
13. Neural networks made easy (Part 13): Batch Normalization
14. Neural networks made easy (Part 14): Data clustering
15. Neural networks made easy (Part 15): Data clustering using MQL5
16. Neural networks made easy (Part 16): Practical use of clustering
17. Neural networks made easy (Part 17): Dimensionality reduction

In this article, I would like to share my experiments with neural networks. After reading a large amount of information available on MQL5, I came to the conclusion that the theory is sufficient. There are plenty of good articles, libraries and source codes. Unfortunately, all this data does not lead to a logical conclusion – a profitable trading system. Let's try to fix this.

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In this new article from our series, we will learn a new technical tool that can be used in our favor especially if we combine it with other technical tools. We will learn how to create a trading system by the Bear's Power technical indicator. As we do in our article in this series that we try to understand the root of things to use them in an effective way.

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Neural networks sound like this fancy new thing that seems as a way forward to build holy grails trading systems, many traders are stunned by the programs made of neural networks, as they seem to be good at predicting market movements basically, they are good at any task at hand.

In this article, we are going to see the basics of a neural network and answer some of the basic questions that I think are important for an ML enthusiast to understand for them to master this subject.

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In the previous article, Developing a trading Expert Advisor from scratch (Part 18), we implemented some fixes, changes and adjustments in the order system, aiming at creating a system that would allow different trading on NETTING and HEDGING accounts, since there are differences in the account operations. But now it is time to finally make the order system completely visual so that we can eliminate the message box and analyze what values are in each position without it.

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• Developing a trading Expert Advisor from scratch (Part 19): New order system (II)
• Developing a trading Expert Advisor from scratch (Part 18): New order system (I)
• Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
• Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II)
• Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)
• Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)
• Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)
• Developing a trading Expert Advisor from scratch (Part 12): Times and Trade (I)
• Developing a trading Expert Advisor from scratch (Part 11): Cross order system
• Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators
• Developing a trading Expert Advisor from scratch (Part 9): A conceptual leap (II)
• Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap
• Developing a trading Expert Advisor from scratch (Part 7): Adding Volume at Price (I)
• Multiple indicators on one chart (Part 06): Turning MetaTrader 5 into a RAD system (II)
• Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)
• Multiple indicators on one chart (Part 03): Developing definitions for users
• Multiple indicators on one chart (Part 04): Advancing to an Expert Advisor
• Multiple indicators on one chart (Part 02): First experiments

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In this new article from our series, we will learn about a new technical tool that can be used in our favor to enhance our trading. We learn about the Bull's Power indicator which is one of the technical indicators that can be used to give us insights into bulls measurement the same as we will see in this article.

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In the previous article, Developing a trading Expert Advisor from scratch (Part 19), we focused on code changes which were implemented to enable the operation of the new order system. Since these changes have been implemented, I can focus 100% on the real problem. This is to implement the order system, which is 100% visual and understandable for those who trade without having to know the tick value or where to place an order to earn X, or where to set Stop Loss in order not to lose Y.

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• Developing a trading Expert Advisor from scratch (Part 19): New order system (II)
• Developing a trading Expert Advisor from scratch (Part 18): New order system (I)
• Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)
• Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II)
• Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)
• Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)
• Developing a trading Expert Advisor from scratch (Part 13): Time and Trade (II)
• Developing a trading Expert Advisor from scratch (Part 12): Times and Trade (I)
• Developing a trading Expert Advisor from scratch (Part 11): Cross order system
• Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators
• Developing a trading Expert Advisor from scratch (Part 9): A conceptual leap (II)
• Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap
• Developing a trading Expert Advisor from scratch (Part 7): Adding Volume at Price (I)
• Multiple indicators on one chart (Part 06): Turning MetaTrader 5 into a RAD system (II)
• Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)
• Multiple indicators on one chart (Part 03): Developing definitions for users
• Multiple indicators on one chart (Part 04): Advancing to an Expert Advisor
• Multiple indicators on one chart (Part 02): First experiments

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In the prior article, we discussed the basics of a neural network and build a very basic and static MLP, but we know in real-life applications we are not going to need a simple 2 inputs and 2 hidden layers nodes in the network to the output, something we built last time.
My point is that we need something dynamic. A dynamic code that we can change the parameters and optimize without breaking the program. If you use python-keras library to build a neural network you will have to do less work of configuring and compiling even complex architectures, that is something that I want us to be able to achieve in MQL5.

Just like I did on the Linear regression part 3 which is one among the must-read in this article series, I introduced the matrix/vector form of models to be able to have flexible models with an unlimited number of inputs.

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• Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified
• Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design

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