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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Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)

Introduction to MQL5 (Part 8): Beginner's Guide to Building Expert Advisors (II)

This article addresses common beginner questions from MQL5 forums and demonstrates practical solutions. Learn to perform essential tasks like buying and selling, obtaining candlestick prices, and managing automated trading aspects such as trade limits, trading periods, and profit/loss thresholds. Get step-by-step guidance to enhance your understanding and implementation of these concepts in MQL5.
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Data label for timeseries mining (Part 2):Make datasets with trend markers using Python

Data label for timeseries mining (Part 2):Make datasets with trend markers using Python

This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
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Advanced Variables and Data Types in MQL5

Advanced Variables and Data Types in MQL5

Variables and data types are very important topics not only in MQL5 programming but also in any programming language. MQL5 variables and data types can be categorized as simple and advanced ones. In this article, we will identify and learn about advanced ones because we already mentioned simple ones in a previous article.
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Category Theory in MQL5 (Part 15) : Functors with Graphs

Category Theory in MQL5 (Part 15) : Functors with Graphs

This article on Category Theory implementation in MQL5, continues the series by looking at Functors but this time as a bridge between Graphs and a set. We revisit calendar data, and despite its limitations in Strategy Tester use, make the case using functors in forecasting volatility with the help of correlation.
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Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and  Control Flow Statements

Introduction to MQL5 (Part 2): Navigating Predefined Variables, Common Functions, and Control Flow Statements

Embark on an illuminating journey with Part Two of our MQL5 series. These articles are not just tutorials, they're doorways to an enchanted realm where programming novices and wizards alike unite. What makes this journey truly magical? Part Two of our MQL5 series stands out with its refreshing simplicity, making complex concepts accessible to all. Engage with us interactively as we answer your questions, ensuring an enriching and personalized learning experience. Let's build a community where understanding MQL5 is an adventure for everyone. Welcome to the enchantment!
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DoEasy. Controls (Part 23): Improving TabControl and SplitContainer WinForms objects

DoEasy. Controls (Part 23): Improving TabControl and SplitContainer WinForms objects

In this article, I will add new mouse events relative to the boundaries of the working areas of WinForms objects and fix some shortcomings in the functioning of the TabControl and SplitContainer controls.
Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)
Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)

Andrey Voitenko: Programming errors cost me $15,000 (ATC 2010)

Andrey Voitenko is participating in the Automated Trading Championship for the first time, but his Expert Advisor is showing mature trading. For already several weeks Andrey's Expert Advisors has been listed in the top ten and seems to be continuing his positive performance. In this interview Andrey is telling about his EA's features, errors and the price they cost him.
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Neural networks made easy (Part 53): Reward decomposition

Neural networks made easy (Part 53): Reward decomposition

We have already talked more than once about the importance of correctly selecting the reward function, which we use to stimulate the desired behavior of the Agent by adding rewards or penalties for individual actions. But the question remains open about the decryption of our signals by the Agent. In this article, we will talk about reward decomposition in terms of transmitting individual signals to the trained Agent.
Interview with Ruslan Ziyatdinov (ATC 2012)
Interview with Ruslan Ziyatdinov (ATC 2012)

Interview with Ruslan Ziyatdinov (ATC 2012)

The Championship keeps providing us with new discoveries, as well as new interesting Participants and unusual ideas implemented in the competition trading robots. While interviewing Ruslan Ziyatdinov (rusland1962), we learned about his simple approach to trading and found out why it is better to trade less frequently.
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Developing a Replay System — Market simulation (Part 05): Adding Previews

Developing a Replay System — Market simulation (Part 05): Adding Previews

We have managed to develop a way to implement the market replay system in a realistic and accessible way. Now let's continue our project and add data to improve the replay behavior.
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Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)

Creating a Trading Administrator Panel in MQL5 (Part III): Enhancing the GUI with Visual Styling (I)

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.
Market Diagnostics by Pulse
Market Diagnostics by Pulse

Market Diagnostics by Pulse

In the article, an attempt is made to visualize the intensity of specific markets and of their time segments, to detect their regularities and behavior patterns.
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Wrapping ONNX models in classes

Wrapping ONNX models in classes

Object-oriented programming enables creation of a more compact code that is easy to read and modify. Here we will have a look at the example for three ONNX models.
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Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system

Building A Candlestick Trend Constraint Model(Part 3): Detecting changes in trends while using this system

This article explores how economic news releases, investor behavior, and various factors can influence market trend reversals. It includes a video explanation and proceeds by incorporating MQL5 code into our program to detect trend reversals, alert us, and take appropriate actions based on market conditions. This builds upon previous articles in the series.
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Developing a multi-currency Expert Advisor (Part 13): Automating the second stage — selection into groups

Developing a multi-currency Expert Advisor (Part 13): Automating the second stage — selection into groups

We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.
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Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs (III) – Adapter-Tuning

Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs (III) – Adapter-Tuning

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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Formulating Dynamic Multi-Pair EA (Part 3): Mean Reversion and Momentum Strategies

Formulating Dynamic Multi-Pair EA (Part 3): Mean Reversion and Momentum Strategies

In this article, we will explore the third part of our journey in formulating a Dynamic Multi-Pair Expert Advisor (EA), focusing specifically on integrating Mean Reversion and Momentum trading strategies. We will break down how to detect and act on price deviations from the mean (Z-score), and how to measure momentum across multiple forex pairs to determine trade direction.
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Automating Trading Strategies in MQL5 (Part 27): Creating a Price Action Crab Harmonic Pattern with Visual Feedback

Automating Trading Strategies in MQL5 (Part 27): Creating a Price Action Crab Harmonic Pattern with Visual Feedback

In this article, we develop a Crab Harmonic Pattern system in MQL5 that identifies bullish and bearish Crab harmonic patterns using pivot points and Fibonacci ratios, triggering trades with precise entry, stop loss, and take-profit levels. We incorporate visual feedback through chart objects like triangles and trendlines to display the XABCD pattern structure and trade levels.
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Neural networks made easy (Part 20): Autoencoders

Neural networks made easy (Part 20): Autoencoders

We continue to study unsupervised learning algorithms. Some readers might have questions regarding the relevance of recent publications to the topic of neural networks. In this new article, we get back to studying neural networks.
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Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)

Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)

We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.
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Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators

Building A Candlestick Trend Constraint Model (Part 1): For EAs And Technical Indicators

This article is aimed at beginners and pro-MQL5 developers. It provides a piece of code to define and constrain signal-generating indicators to trends in higher timeframes. In this way, traders can enhance their strategies by incorporating a broader market perspective, leading to potentially more robust and reliable trading signals.
Testing Visualization: Account State Charts
Testing Visualization: Account State Charts

Testing Visualization: Account State Charts

Enjoy the process of testing with charts, displaying the balance - now all the necessary information is always in view!
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Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)

Automated exchange grid trading using stop pending orders on Moscow Exchange (MOEX)

The article considers the grid trading approach based on stop pending orders and implemented in an MQL5 Expert Advisor on the Moscow Exchange (MOEX). When trading in the market, one of the simplest strategies is a grid of orders designed to "catch" the market price.
Creation of an Automated Trading System
Creation of an Automated Trading System

Creation of an Automated Trading System

You must admit that it sounds alluringly - you become a fortunate possessor of a program that can develop for you a profitable automated trading system (ATC) within a few minutes. All you need is to enter desirable inputs and press Enter. And - here you are, take your ATC tested and having positive expected payoff. Where thousands of people spend thousands of hours on developing that very unique ATC, which will "wine and dine", these statements soundб to put it mildly, very hollow. On the one hand, this really looks a little larger than life... However, to my mind, this problem can be solved.
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)
Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)

Graphical Interfaces X: Updates for the Rendered table and code optimization (build 10)

We continue to complement the Rendered table (CCanvasTable) with new features. The table will now have: highlighting of the rows when hovered; ability to add an array of icons for each cell and a method for switching them; ability to set or modify the cell text during the runtime, and more.
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Integrate Your Own LLM into EA (Part 2): Example of Environment Deployment

Integrate Your Own LLM into EA (Part 2): Example of Environment Deployment

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.
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Neural networks made easy (Part 66): Exploration problems in offline learning

Neural networks made easy (Part 66): Exploration problems in offline learning

Models are trained offline using data from a prepared training dataset. While providing certain advantages, its negative side is that information about the environment is greatly compressed to the size of the training dataset. Which, in turn, limits the possibilities of exploration. In this article, we will consider a method that enables the filling of a training dataset with the most diverse data possible.
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Color buffers in multi-symbol multi-period indicators

Color buffers in multi-symbol multi-period indicators

In this article, we will review the structure of the indicator buffer in multi-symbol, multi-period indicators and organize the display of colored buffers of these indicators on the chart.
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DoEasy. Controls (Part 5): Base WinForms object, Panel control, AutoSize parameter

DoEasy. Controls (Part 5): Base WinForms object, Panel control, AutoSize parameter

In the article, I will create the base object of all library WinForms objects and start implementing the AutoSize property of the Panel WinForms object — auto sizing for fitting the object internal content.
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Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)

Neural Networks in Trading: Scene-Aware Object Detection (HyperDet3D)

We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.
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High frequency arbitrage trading system in Python using MetaTrader 5

High frequency arbitrage trading system in Python using MetaTrader 5

In this article, we will create an arbitration system that remains legal in the eyes of brokers, creates thousands of synthetic prices on the Forex market, analyzes them, and successfully trades for profit.
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William Gann methods (Part I): Creating Gann Angles indicator

William Gann methods (Part I): Creating Gann Angles indicator

What is the essence of Gann Theory? How are Gann angles constructed? We will create Gann Angles indicator for MetaTrader 5.
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DoEasy. Controls (Part 29): ScrollBar auxiliary control

DoEasy. Controls (Part 29): ScrollBar auxiliary control

In this article, I will start developing the ScrollBar auxiliary control element and its derivative objects — vertical and horizontal scrollbars. A scrollbar is used to scroll the content of the form if it goes beyond the container. Scrollbars are usually located at the bottom and to the right of the form. The horizontal one at the bottom scrolls content left and right, while the vertical one scrolls up and down.
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Population optimization algorithms: Artificial Bee Colony (ABC)

Population optimization algorithms: Artificial Bee Colony (ABC)

In this article, we will study the algorithm of an artificial bee colony and supplement our knowledge with new principles of studying functional spaces. In this article, I will showcase my interpretation of the classic version of the algorithm.
Ten Basic Errors of a Newcomer in Trading
Ten Basic Errors of a Newcomer in Trading

Ten Basic Errors of a Newcomer in Trading

There are ten basic errors of a newcomer intrading: trading at market opening, undue hurry in taking profit, adding of lots in a losing position, closing positions starting with the best one, revenge, the most preferable positions, trading by the principle of 'bought for ever', closing of a profitable strategic position on the first day, closing of a position when alerted to open an opposite position, doubts.
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Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps

Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps

Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
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Integrate Your Own LLM into EA (Part 1): Hardware and Environment Deployment

Integrate Your Own LLM into EA (Part 1): Hardware and Environment Deployment

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
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Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift

Timeseries in DoEasy library (part 50): Multi-period multi-symbol standard indicators with a shift

In the article, let’s improve library methods for correct display of multi-symbol multi-period standard indicators, which lines are displayed on the current symbol chart with a shift set in the settings. As well, let’s put things in order in methods of work with standard indicators and remove the redundant code to the library area in the final indicator program.
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Developing an MQTT client for MetaTrader 5: a TDD approach

Developing an MQTT client for MetaTrader 5: a TDD approach

This article reports the first attempts in the development of a native MQTT client for MQL5. MQTT is a Client Server publish/subscribe messaging transport protocol. It is lightweight, open, simple, and designed to be easy to implement. These characteristics make it ideal for use in many situations.