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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Neural networks made easy (Part 58): Decision Transformer (DT)

Neural networks made easy (Part 58): Decision Transformer (DT)

We continue to explore reinforcement learning methods. In this article, I will focus on a slightly different algorithm that considers the Agent’s policy in the paradigm of constructing a sequence of actions.
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Neural networks made easy (Part 23): Building a tool for Transfer Learning

Neural networks made easy (Part 23): Building a tool for Transfer Learning

In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.
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DoEasy. Controls (Part 26): Finalizing the ToolTip WinForms object and moving on to ProgressBar development

DoEasy. Controls (Part 26): Finalizing the ToolTip WinForms object and moving on to ProgressBar development

In this article, I will complete the development of the ToolTip control and start the development of the ProgressBar WinForms object. While working on objects, I will develop universal functionality for animating controls and their components.
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Creating an MQL5-Telegram Integrated Expert Advisor (Part 6): Adding Responsive Inline Buttons

Creating an MQL5-Telegram Integrated Expert Advisor (Part 6): Adding Responsive Inline Buttons

In this article, we integrate interactive inline buttons into an MQL5 Expert Advisor, allowing real-time control via Telegram. Each button press triggers specific actions and sends responses back to the user. We also modularize functions for handling Telegram messages and callback queries efficiently.
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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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Integrating Hidden Markov Models in MetaTrader 5

Integrating Hidden Markov Models in MetaTrader 5

In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.
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Expert Advisor based on the universal MLP approximator

Expert Advisor based on the universal MLP approximator

The article presents a simple and accessible way to use a neural network in a trading EA that does not require deep knowledge of machine learning. The method eliminates the target function normalization, as well as overcomes "weight explosion" and "network stall" issues offering intuitive training and visual control of the results.
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Developing a Calendar-Based News Event Breakout Expert Advisor in MQL5

Developing a Calendar-Based News Event Breakout Expert Advisor in MQL5

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.
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Neural networks made easy (Part 73): AutoBots for predicting price movements

Neural networks made easy (Part 73): AutoBots for predicting price movements

We continue to discuss algorithms for training trajectory prediction models. In this article, we will get acquainted with a method called "AutoBots".
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Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis

Build Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis

How best can we combine multiple strategies to create a powerful ensemble strategy? Join us in this discussion as we look to fit together three different strategies into our trading application. Traders often employ specialized strategies for opening and closing positions, and we want to know if our machines can perform this task better. For our opening discussion, we will get familiar with the faculties of the strategy tester and the principles of OOP we will need for this task.
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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!
Interview with Nikolay Kositsin: multicurrency EA are less risky (ATC 2010)
Interview with Nikolay Kositsin: multicurrency EA are less risky (ATC 2010)

Interview with Nikolay Kositsin: multicurrency EA are less risky (ATC 2010)

Nikolay Kositsin has told us about his developments. He believes multicurrency Expert Advisors are a promising direction; and he is an experienced developer of such robots. At the championships, Nikolay participates only with multicurrency EAs. His Expert Advisor was the only multicurrency EA among the prize winners of all the ATC contests.
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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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Fast trading strategy tester in Python using Numba

Fast trading strategy tester in Python using Numba

The article implements a fast strategy tester for machine learning models using Numba. It is 50 times faster than the pure Python strategy tester. The author recommends using this library to speed up mathematical calculations, especially the ones involving loops.
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Dynamic Swing Architecture: Market Structure Recognition from Swings to Automated Execution

Dynamic Swing Architecture: Market Structure Recognition from Swings to Automated Execution

This article introduces a fully automated MQL5 system designed to identify and trade market swings with precision. Unlike traditional fixed-bar swing indicators, this system adapts dynamically to evolving price structure—detecting swing highs and swing lows in real time to capture directional opportunities as they form.
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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.
Interview with Alexandr Artapov (ATC 2012)
Interview with Alexandr Artapov (ATC 2012)

Interview with Alexandr Artapov (ATC 2012)

It was during the second week of the Championship when the Expert Advisor of Alexandr Artapov (artall) found itself on the third position trading EURUSD and EURJPY. Then it briefly left TOP-10 to appear again after one month of struggle for survival. As it turned out, this trading robot is still having something up its sleeve.
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Developing a trading Expert Advisor from scratch (Part 27): Towards the future (II)

Developing a trading Expert Advisor from scratch (Part 27): Towards the future (II)

Let's move on to a more complete order system directly on the chart. In this article, I will show a way to fix the order system, or rather, to make it more intuitive.
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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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Price Action Analysis Toolkit Development (Part 23): Currency Strength Meter

Price Action Analysis Toolkit Development (Part 23): Currency Strength Meter

Do you know what really drives a currency pair’s direction? It’s the strength of each individual currency. In this article, we’ll measure a currency’s strength by looping through every pair it appears in. That insight lets us predict how those pairs may move based on their relative strengths. Read on to learn 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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Price Action Analysis Toolkit Development (Part 30): Commodity Channel Index (CCI), Zero Line EA

Price Action Analysis Toolkit Development (Part 30): Commodity Channel Index (CCI), Zero Line EA

Automating price action analysis is the way forward. In this article, we utilize the Dual CCI indicator, the Zero Line Crossover strategy, EMA, and price action to develop a tool that generates trade signals and sets stop-loss (SL) and take-profit (TP) levels using ATR. Please read this article to learn how we approach the development of the CCI Zero Line EA.
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MQL5 Wizard Techniques you should know (Part 42): ADX Oscillator

MQL5 Wizard Techniques you should know (Part 42): ADX Oscillator

The ADX is another relatively popular technical indicator used by some traders to gauge the strength of a prevalent trend. Acting as a combination of two other indicators, it presents as an oscillator whose patterns we explore in this article with the help of MQL5 wizard assembly and its support classes.
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Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap

Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap

The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.
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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MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library

MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library

Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.
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Introduction to MQL5 (Part 13): A Beginner's Guide to Building Custom Indicators (II)

Introduction to MQL5 (Part 13): A Beginner's Guide to Building Custom Indicators (II)

This article guides you through building a custom Heikin Ashi indicator from scratch and demonstrates how to integrate custom indicators into an EA. It covers indicator calculations, trade execution logic, and risk management techniques to enhance automated trading strategies.
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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.
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Developing a multi-currency Expert Advisor (Part 17): Further preparation for real trading

Developing a multi-currency Expert Advisor (Part 17): Further preparation for real trading

Currently, our EA uses the database to obtain initialization strings for single instances of trading strategies. However, the database is quite large and contains a lot of information that is not needed for the actual EA operation. Let's try to ensure the EA's functionality without a mandatory connection to the database.
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Integrating MQL5 with data processing packages (Part 5): Adaptive Learning and Flexibility

Integrating MQL5 with data processing packages (Part 5): Adaptive Learning and Flexibility

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.
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Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks

Build Self Optimizing Expert Advisors With MQL5 And Python (Part II): Tuning Deep Neural Networks

Machine learning models come with various adjustable parameters. In this series of articles, we will explore how to customize your AI models to fit your specific market using the SciPy library.
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Chart Synchronization for Easier Technical Analysis

Chart Synchronization for Easier Technical Analysis

Chart Synchronization for Easier Technical Analysis is a tool that ensures all chart timeframes display consistent graphical objects like trendlines, rectangles, or indicators across different timeframes for a single symbol. Actions such as panning, zooming, or symbol changes are mirrored across all synced charts, allowing traders to seamlessly view and compare the same price action context in multiple timeframes.
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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.
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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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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.
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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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Trend criteria in trading

Trend criteria in trading

Trends are an important part of many trading strategies. In this article, we will look at some of the tools used to identify trends and their characteristics. Understanding and correctly interpreting trends can significantly improve trading efficiency and minimize risks.
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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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MQL5 Wizard Techniques you should know (Part 80): Using Patterns of Ichimoku and the ADX-Wilder with TD3 Reinforcement Learning

MQL5 Wizard Techniques you should know (Part 80): Using Patterns of Ichimoku and the ADX-Wilder with TD3 Reinforcement Learning

This article follows up ‘Part-74’, where we examined the pairing of Ichimoku and the ADX under a Supervised Learning framework, by moving our focus to Reinforcement Learning. Ichimoku and ADX form a complementary combination of support/resistance mapping and trend strength spotting. In this installment, we indulge in how the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm can be used with this indicator set. As with earlier parts of the series, the implementation is carried out in a custom signal class designed for integration with the MQL5 Wizard, which facilitates seamless Expert Advisor assembly.
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From Novice to Expert: Predictive Price Pathways

From Novice to Expert: Predictive Price Pathways

Fibonacci levels provide a practical framework that markets often respect, highlighting price zones where reactions are more likely. In this article, we build an expert advisor that applies Fibonacci retracement logic to anticipate likely future moves and trade retracements with pending orders. Explore the full workflow—from swing detection to level plotting, risk controls, and execution.