MQL4 and MQL5 Programming Articles

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Study the MQL5 language for programming trading strategies in numerous published articles mostly written by you - the community members. The articles are grouped into categories to help you quicker find answers to any questions related to programming: Integration, Tester, Trading Strategies, etc.

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How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session

How to create a simple Multi-Currency Expert Advisor using MQL5 (Part 3): Added symbols prefixes and/or suffixes and Trading Time Session

Several fellow traders sent emails or commented about how to use this Multi-Currency EA on brokers with symbol names that have prefixes and/or suffixes, and also how to implement trading time zones or trading time sessions on this Multi-Currency EA.
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Neural networks made easy (Part 49): Soft Actor-Critic

Neural networks made easy (Part 49): Soft Actor-Critic

We continue our discussion of reinforcement learning algorithms for solving continuous action space problems. In this article, I will present the Soft Actor-Critic (SAC) algorithm. The main advantage of SAC is the ability to find optimal policies that not only maximize the expected reward, but also have maximum entropy (diversity) of actions.
Interview with Sergey Pankratyev (ATC 2012)
Interview with Sergey Pankratyev (ATC 2012)

Interview with Sergey Pankratyev (ATC 2012)

The Championship is coming to an end leaving us with vivid impressions of many unusual trading strategies. However, the trading robot of Sergey Pankratyev (s75) is showing really peculiar things - it is trading all 12 currency pairs opening only long positions. It is not an error but just a response to some certain market conditions.
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Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.
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The Liquidity Grab Trading Strategy

The Liquidity Grab Trading Strategy

The liquidity grab trading strategy is a key component of Smart Money Concepts (SMC), which seeks to identify and exploit the actions of institutional players in the market. It involves targeting areas of high liquidity, such as support or resistance zones, where large orders can trigger price movements before the market resumes its trend. This article explains the concept of liquidity grab in detail and outlines the development process of the liquidity grab trading strategy Expert Advisor in MQL5.
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Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)

Improve Your Trading Charts With Interactive GUI's in MQL5 (Part I): Movable GUI (I)

Unleash the power of dynamic data representation in your trading strategies or utilities with our comprehensive guide on creating movable GUI in MQL5. Dive into the core concept of chart events and learn how to design and implement simple and multiple movable GUI on the same chart. This article also explores the process of adding elements to your GUI, enhancing their functionality and aesthetic appeal.
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Matrices and vectors in MQL5: Activation functions

Matrices and vectors in MQL5: Activation functions

Here we will describe only one of the aspects of machine learning - activation functions. In artificial neural networks, a neuron activation function calculates an output signal value based on the values of an input signal or a set of input signals. We will delve into the inner workings of the process.
Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"
Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"

Andrey Voitenko (avoitenko): "Developers benefit from the ideas that they code? Nonsense!"

A Ukrainian developer Andrey Voitenko (avoitenko) is an active participant of the "Jobs" service at mql5.com, helping traders from all over the world to implement their ideas. Last year Andrey's Expert Advisor was on the fourth place in the Automated Trading Championship 2010, being slightly behind the bronze winner. This time we are discussing the Jobs service with Andrey.
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Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)

Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)

Today we will add some more resources to our EA. This interesting article can provide some new ideas and methods of presenting information. At the same time, it can assist in fixing minor flaws in your projects.
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Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Data Science and Machine Learning (Part 21): Unlocking Neural Networks, Optimization algorithms demystified

Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.
Testing Visualization: Functionality Enhancement
Testing Visualization: Functionality Enhancement

Testing Visualization: Functionality Enhancement

The article describes software that can make strategy testing highly similar to the real trading.
Expert System 'Commentator'. Practical Use of Embedded Indicators in an MQL4 Program
Expert System 'Commentator'. Practical Use of Embedded Indicators in an MQL4 Program

Expert System 'Commentator'. Practical Use of Embedded Indicators in an MQL4 Program

The article describes the use of technical indicators in programming on MQL4.
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Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor

Understanding Programming Paradigms (Part 2): An Object-Oriented Approach to Developing a Price Action Expert Advisor

Learn about the object-oriented programming paradigm and its application in MQL5 code. This second article goes deeper into the specifics of object-oriented programming, offering hands-on experience through a practical example. You'll learn how to convert our earlier developed procedural price action expert advisor using the EMA indicator and candlestick price data to object-oriented code.
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Population optimization algorithms: Fish School Search (FSS)

Population optimization algorithms: Fish School Search (FSS)

Fish School Search (FSS) is a new optimization algorithm inspired by the behavior of fish in a school, most of which (up to 80%) swim in an organized community of relatives. It has been proven that fish aggregations play an important role in the efficiency of foraging and protection from predators.
Who Is Who in MQL5.community?
Who Is Who in MQL5.community?

Who Is Who in MQL5.community?

The MQL5.com website remembers all of you quite well! How many of your threads are epic, how popular your articles are and how often your programs in the Code Base are downloaded – this is only a small part of what is remembered at MQL5.com. Your achievements are available in your profile, but what about the overall picture? In this article we will show the general picture of all MQL5.community members achievements.
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Timeseries in DoEasy library (part 52): Cross-platform nature of multi-period multi-symbol  single-buffer standard indicators

Timeseries in DoEasy library (part 52): Cross-platform nature of multi-period multi-symbol single-buffer standard indicators

In the article, consider creation of multi-symbol multi-period standard indicator Accumulation/Distribution. Slightly improve library classes with respect to indicators so that, the programs developed for outdated platform MetaTrader 4 based on this library could work normally when switching over to MetaTrader 5.
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Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Neural networks made easy (Part 55): Contrastive intrinsic control (CIC)

Contrastive training is an unsupervised method of training representation. Its goal is to train a model to highlight similarities and differences in data sets. In this article, we will talk about using contrastive training approaches to explore different Actor skills.
Graphics in DoEasy library (Part 85): Graphical object collection - adding newly created objects
Graphics in DoEasy library (Part 85): Graphical object collection - adding newly created objects

Graphics in DoEasy library (Part 85): Graphical object collection - adding newly created objects

In this article, I will complete the development of the descendant classes of the abstract graphical object class and start implementing the ability to store these objects in the collection class. In particular, I will create the functionality for adding newly created standard graphical objects to the collection class.
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DoEasy. Controls (Part 18): Functionality for scrolling tabs in TabControl

DoEasy. Controls (Part 18): Functionality for scrolling tabs in TabControl

In this article, I will place header scrolling control buttons in TabControl WinForms object in case the header bar does not fit the size of the control. Besides, I will implement the shift of the header bar when clicking on the cropped tab header.
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Implementing the Janus factor in MQL5

Implementing the Janus factor in MQL5

Gary Anderson developed a method of market analysis based on a theory he dubbed the Janus Factor. The theory describes a set of indicators that can be used to reveal trends and assess market risk. In this article we will implement these tools in mql5.
Visual Testing of the Profitability of Indicators and Alerts
Visual Testing of the Profitability of Indicators and Alerts

Visual Testing of the Profitability of Indicators and Alerts

What indicator of trading alerts or just the methods of their calculating to use is usually decided when testing EAs using these alerts. However, it is not always possible/necessary/reasonable to write an EA for each indicator. You can promptly calculate the profitability of trading on the alerts from other indicators, using a special indicator that collects their alerts itself and draws a picture of ideal trading with them. It can help you both make a visual estimate of the results obtained and quickly choose most optimal parameters.
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Graphics in DoEasy library (Part 100): Making improvements in handling extended standard graphical objects

Graphics in DoEasy library (Part 100): Making improvements in handling extended standard graphical objects

In the current article, I will eliminate obvious flaws in simultaneous handling of extended (and standard) graphical objects and form objects on canvas, as well as fix errors detected during the test performed in the previous article. The article concludes this section of the library description.
Expert Advisors Based on Popular Trading Systems and Alchemy of Trading Robot Optimization (Part  V)
Expert Advisors Based on Popular Trading Systems and Alchemy of Trading Robot Optimization (Part  V)

Expert Advisors Based on Popular Trading Systems and Alchemy of Trading Robot Optimization (Part V)

In this article the author offers ways to improve trading systems described in his previous articles. The article will be interesting for traders that already have some experience of writing Expert Advisors.
Interview with Mariusz Zarnowski (ATC 2012)
Interview with Mariusz Zarnowski (ATC 2012)

Interview with Mariusz Zarnowski (ATC 2012)

As December 28 is approaching, the list of leaders of the Automated Trading Championship 2012 is becoming clearer. With only two weeks to go until the end of the Championship, Mariusz Zarnowski (zrn) from Poland stands a good chance to be in the top three. His EA has already demonstrated how it can triple the initial deposit in just a couple of weeks.
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Developing a robot in Python and MQL5 (Part 1): Data preprocessing

Developing a robot in Python and MQL5 (Part 1): Data preprocessing

Developing a trading robot based on machine learning: A detailed guide. The first article in the series deals with collecting and preparing data and features. The project is implemented using the Python programming language and libraries, as well as the MetaTrader 5 platform.
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USD and EUR index charts — example of a MetaTrader 5 service

USD and EUR index charts — example of a MetaTrader 5 service

We will consider the creation and updating of USD index (USDX) and EUR index (EURX) charts using a MetaTrader 5 service as an example. When launching the service, we will check for the presence of the required synthetic instrument, create it if necessary, and place it in the Market Watch window. The minute and tick history of the synthetic instrument is to be created afterwards followed by the chart of the created instrument.
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DoEasy. Controls (Part 9): Re-arranging WinForms object methods, RadioButton and Button controls

DoEasy. Controls (Part 9): Re-arranging WinForms object methods, RadioButton and Button controls

In this article, I will fix the names of WinForms object class methods and create Button and RadioButton WinForms objects.
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MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

MQL5 Wizard Techniques you should know (Part 09): Pairing K-Means Clustering with Fractal Waves

K-Means clustering takes the approach to grouping data points as a process that’s initially focused on the macro view of a data set that uses random generated cluster centroids before zooming in and adjusting these centroids to accurately represent the data set. We will look at this and exploit a few of its use cases.
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Developing a trading Expert Advisor from scratch (Part 20): New order system (III)

Developing a trading Expert Advisor from scratch (Part 20): New order system (III)

We continue to implement the new order system. The creation of such a system requires a good command of MQL5, as well as an understanding of how the MetaTrader 5 platform actually works and what resources it provides.
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DRAKON visual programming language — communication tool for MQL developers and customers

DRAKON visual programming language — communication tool for MQL developers and customers

DRAKON is a visual programming language designed to simplify interaction between specialists from different fields (biologists, physicists, engineers...) with programmers in Russian space projects (for example, in the Buran reusable spacecraft project). In this article, I will talk about how DRAKON makes the creation of algorithms accessible and intuitive, even if you have never encountered code, and also how it is easier for customers to explain their thoughts when ordering trading robots, and for programmers to make fewer mistakes in complex functions.
Metalanguage of Graphical Lines-Requests. Trading and Qualified Trading Learning
Metalanguage of Graphical Lines-Requests. Trading and Qualified Trading Learning

Metalanguage of Graphical Lines-Requests. Trading and Qualified Trading Learning

The article describes a simple, accessible language of graphical trading requests compatible with traditional technical analysis. The attached Gterminal is a half-automated Expert Advisor using in trading results of graphical analysis. Better used for self-education and training of beginning traders.
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Developing a Replay System — Market simulation (Part 20): FOREX (I)

Developing a Replay System — Market simulation (Part 20): FOREX (I)

The initial goal of this article is not to cover all the possibilities of Forex trading, but rather to adapt the system so that you can perform at least one market replay. We'll leave simulation for another moment. However, if we don't have ticks and only bars, with a little effort we can simulate possible trades that could happen in the Forex market. This will be the case until we look at how to adapt the simulator. An attempt to work with Forex data inside the system without modifying it leads to a range of errors.
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The Inverse Fair Value Gap Trading Strategy

The Inverse Fair Value Gap Trading Strategy

An inverse fair value gap(IFVG) occurs when price returns to a previously identified fair value gap and, instead of showing the expected supportive or resistive reaction, fails to respect it. This failure can signal a potential shift in market direction and offer a contrarian trading edge. In this article, I'm going to introduce my self-developed approach to quantifying and utilizing inverse fair value gap as a strategy for MetaTrader 5 expert advisors.
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Reimagining Classic Strategies (Part 12): EURUSD Breakout Strategy

Reimagining Classic Strategies (Part 12): EURUSD Breakout Strategy

Join us today as we challenge ourselves to build a profitable break-out trading strategy in MQL5. We selected the EURUSD pair and attempted to trade price breakouts on the hourly timeframe. Our system had difficulty distinguishing between false breakouts and the beginning of true trends. We layered our system with filters intended to minimize our losses whilst increasing our gains. In the end, we successfully made our system profitable and less prone to false breakouts.
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Developing a Replay System (Part 38): Paving the Path (II)

Developing a Replay System (Part 38): Paving the Path (II)

Many people who consider themselves MQL5 programmers do not have the basic knowledge that I will outline in this article. Many people consider MQL5 to be a limited tool, but the actual reason is that they do not have the required knowledge. So, if you don't know something, don't be ashamed of it. It's better to feel ashamed for not asking. Simply forcing MetaTrader 5 to disable indicator duplication in no way ensures two-way communication between the indicator and the Expert Advisor. We are still very far from this, but the fact that the indicator is not duplicated on the chart gives us some confidence.
Fallacies, Part 2. Statistics Is a Pseudo-Science, or a Chronicle of Nosediving Bread And Butter
Fallacies, Part 2. Statistics Is a Pseudo-Science, or a Chronicle of Nosediving Bread And Butter

Fallacies, Part 2. Statistics Is a Pseudo-Science, or a Chronicle of Nosediving Bread And Butter

Numerous attempts to apply statistical methods to the objective reality, i.e. to financial series, crash when met with the nonstationarity of processes, "fat tails" of accompanying probability distributions, and insufficient volume of financial data.In this publication I will try to refer not to the financial series as such, but to their subjective presentation - in this case, to the way a trader tries to halter the series, i.e. to the trading system. The eduction of statistical regularities of the trading results process is a rather enthralling task. In some cases quite true conclusions about the model of this process can be made, and these can be applied to the trading system.
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Automated Parameter Optimization for Trading Strategies Using Python and MQL5

Automated Parameter Optimization for Trading Strategies Using Python and MQL5

There are several types of algorithms for self-optimization of trading strategies and parameters. These algorithms are used to automatically improve trading strategies based on historical and current market data. In this article we will look at one of them with python and MQL5 examples.
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Statistical Arbitrage with predictions

Statistical Arbitrage with predictions

We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
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Deep Learning GRU model with Python to ONNX  with EA, and GRU vs LSTM models

Deep Learning GRU model with Python to ONNX with EA, and GRU vs LSTM models

We will guide you through the entire process of DL with python to make a GRU ONNX model, culminating in the creation of an Expert Advisor (EA) designed for trading, and subsequently comparing GRU model with LSTM model.
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Neural networks made easy (Part 33): Quantile regression in distributed Q-learning

Neural networks made easy (Part 33): Quantile regression in distributed Q-learning

We continue studying distributed Q-learning. Today we will look at this approach from the other side. We will consider the possibility of using quantile regression to solve price prediction tasks.