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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Graphics in DoEasy library (Part 90): Standard graphical object events. Basic functionality
Graphics in DoEasy library (Part 90): Standard graphical object events. Basic functionality

Graphics in DoEasy library (Part 90): Standard graphical object events. Basic functionality

In this article, I will implement the basic functionality for tracking standard graphical object events. I will start from a double click event on a graphical object.
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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: Bacterial Foraging Optimization (BFO)

Population optimization algorithms: Bacterial Foraging Optimization (BFO)

E. coli bacterium foraging strategy inspired scientists to create the BFO optimization algorithm. The algorithm contains original ideas and promising approaches to optimization and is worthy of further study.
Interview with Leonid Velichkovsky: "The Biggest Myth about Neural Networks is Super-Profitability" (ATC 2010)
Interview with Leonid Velichkovsky: "The Biggest Myth about Neural Networks is Super-Profitability" (ATC 2010)

Interview with Leonid Velichkovsky: "The Biggest Myth about Neural Networks is Super-Profitability" (ATC 2010)

The hero of our interview Leonid Velichkovski (LeoV) has already participated in Automated Trading Championships. In 2008, his multicurrency neural network was like a bright flash in the sky, earning $110,000 in a certain moment, but eventually fell victim to its own aggressive money management. Two years ago, in his interview Leonid share his own trading experience and told us about the features of his Expert Advisor. On the eve of the ATC 2010, Leonid talks about the most common myths and misconceptions associated with neural networks.
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Parafrac Oscillator: Combination of Parabolic and Fractal Indicator

Parafrac Oscillator: Combination of Parabolic and Fractal Indicator

We will explore how the Parabolic SAR and the Fractal indicator can be combined to create a new oscillator-based indicator. By integrating the unique strengths of both tools, traders can aim at developing a more refined and effective trading strategy.
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Reimagining Classic Strategies (Part 15): Daily Breakout Trading Strategy

Reimagining Classic Strategies (Part 15): Daily Breakout Trading Strategy

Human traders had long participated in financial markets before the rise of computers, developing rules of thumb that guided their decisions. In this article, we revisit a well-known breakout strategy to test whether such market logic, learned through experience, can hold its own against systematic methods. Our findings show that while the original strategy produced high accuracy, it suffered from instability and poor risk control. By refining the approach, we demonstrate how discretionary insights can be adapted into more robust, algorithmic trading strategies.
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Parafrac Oscillator: Combination of Parabolic and Fractal Indicator

Parafrac Oscillator: Combination of Parabolic and Fractal Indicator

We will explore how the Parabolic SAR and the Fractal indicator can be combined to create a new oscillator-based indicator. By integrating the unique strengths of both tools, traders can aim at developing a more refined and effective trading strategy.
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Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs

Building AI-Powered Trading Systems in MQL5 (Part 1): Implementing JSON Handling for AI APIs

In this article, we develop a JSON parsing framework in MQL5 to handle data exchange for AI API integration, focusing on a JSON class for processing JSON structures. We implement methods to serialize and deserialize JSON data, supporting various data types like strings, numbers, and objects, essential for communicating with AI services like ChatGPT, enabling future AI-driven trading systems by ensuring accurate data handling and manipulation.
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Category Theory in MQL5 (Part 1)

Category Theory in MQL5 (Part 1)

Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that attracts comments and discussion while hopefully furthering the use of this remarkable field in Traders' strategy development.
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Developing a trading Expert Advisor from scratch (Part 22): New order system (V)

Developing a trading Expert Advisor from scratch (Part 22): New order system (V)

Today we will continue to develop the new order system. It is not that easy to implement a new system as we often encounter problems which greatly complicate the process. When these problems appear, we have to stop and re-analyze the direction in which we are moving.
Selection and navigation utility in MQL5 and MQL4: Adding "homework" tabs and saving graphical objects
Selection and navigation utility in MQL5 and MQL4: Adding "homework" tabs and saving graphical objects

Selection and navigation utility in MQL5 and MQL4: Adding "homework" tabs and saving graphical objects

In this article, we are going to expand the capabilities of the previously created utility by adding tabs for selecting the symbols we need. We will also learn how to save graphical objects we have created on the specific symbol chart, so that we do not have to constantly create them again. Besides, we will find out how to work only with symbols that have been preliminarily selected using a specific website.
Technical Analysis: How Do We Analyze?
Technical Analysis: How Do We Analyze?

Technical Analysis: How Do We Analyze?

This article briefly describes the author's opinion on redrawing indicators, multi-timeframe indicators and displaying of quotes with Japanese candlesticks. The article contain no programming specifics and is of a general character.
CCI indicator. Three transformation steps
CCI indicator. Three transformation steps

CCI indicator. Three transformation steps

In this article, I will make additional changes to the CCI affecting the very logic of this indicator. Moreover, we will be able to see it in the main chart window.
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Developing a Trading Strategy: Using a Volume-Bound Approach

Developing a Trading Strategy: Using a Volume-Bound Approach

In the world of technical analysis, price often takes center stage. Traders meticulously map out support, resistance, and patterns, yet frequently ignore the critical force that drives these movements: volume. This article delves into a novel approach to volume analysis: the Volume Boundary indicator. This transformation, utilizing sophisticated smoothing functions like the butterfly and triple sine curves, allows for clearer interpretation and the development of systematic trading strategies.
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Parallel Particle Swarm Optimization

Parallel Particle Swarm Optimization

The article describes a method of fast optimization using the particle swarm algorithm. It also presents the method implementation in MQL, which is ready for use both in single-threaded mode inside an Expert Advisor and in a parallel multi-threaded mode as an add-on that runs on local tester agents.
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Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection

Introduction to MQL5 (Part 19): Automating Wolfe Wave Detection

This article shows how to programmatically identify bullish and bearish Wolfe Wave patterns and trade them using MQL5. We’ll explore how to identify Wolfe Wave structures programmatically and execute trades based on them using MQL5. This includes detecting key swing points, validating pattern rules, and preparing the EA to act on the signals it finds.
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Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network

Experiments with neural networks (Part 5): Normalizing inputs for passing to a neural network

Neural networks are an ultimate tool in traders' toolkit. Let's check if this assumption is true. MetaTrader 5 is approached as a self-sufficient medium for using neural networks in trading. A simple explanation is provided.
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Pair Trading: Algorithmic Trading with Auto Optimization Based on Z-Score Differences

Pair Trading: Algorithmic Trading with Auto Optimization Based on Z-Score Differences

In this article, we will explore what pair trading is and how correlation trading works. We will also create an EA for automating pair trading and add the ability to automatically optimize this trading algorithm based on historical data. In addition, as part of the project, we will learn how to calculate the differences between two pairs using the z-score.
Electronic Tables in MQL5
Electronic Tables in MQL5

Electronic Tables in MQL5

The article describes a class of dynamic two-dimensional array that contains data of different types in its first dimension. Storing data in the form of a table is convenient for solving a wide range of problems of arrangement, storing and operation with bound information of different types. The source code of the class that implements the functionality of working with tables is attached to the article.
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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 13): Optimizing interaction of WinForms objects with the mouse, starting the development of the TabControl WinForms object

DoEasy. Controls (Part 13): Optimizing interaction of WinForms objects with the mouse, starting the development of the TabControl WinForms object

In this article, I will fix and optimize handling the appearance of WinForms objects after moving the mouse cursor away from the object, as well as start the development of the TabControl WinForms object.
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Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators

Ready-made templates for including indicators to Expert Advisors (Part 2): Volume and Bill Williams indicators

In this article, we will look at standard indicators of the Volume and Bill Williams' indicators category. We will create ready-to-use templates for indicator use in EAs - declaring and setting parameters, indicator initialization and deinitialization, as well as receiving data and signals from indicator buffers in EAs.
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Data Science and Machine Learning (Part 06): Gradient Descent

Data Science and Machine Learning (Part 06): Gradient Descent

The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
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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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Multiple indicators on one chart (Part 03): Developing definitions for users

Multiple indicators on one chart (Part 03): Developing definitions for users

Today we will update the functionality of the indicator system for the first time. In the previous article within the "Multiple indicators on one chart" we considered the basic code which allows using more than one indicator in a chart subwindow. But what was presented was just the starting base of a much larger system.
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Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (FinCon)

Neural Networks in Trading: A Multi-Agent System with Conceptual Reinforcement (FinCon)

We invite you to explore the FinCon framework, which is a a Large Language Model (LLM)-based multi-agent system. The framework uses conceptual verbal reinforcement to improve decision making and risk management, enabling effective performance on a variety of financial tasks.
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Introduction to MQL5 (Part 16): Building Expert Advisors Using Technical Chart Patterns

Introduction to MQL5 (Part 16): Building Expert Advisors Using Technical Chart Patterns

This article introduces beginners to building an MQL5 Expert Advisor that identifies and trades a classic technical chart pattern — the Head and Shoulders. It covers how to detect the pattern using price action, draw it on the chart, set entry, stop loss, and take profit levels, and automate trade execution based on the pattern.
Marvel Your MQL5 Customers with a Usable Cocktail of Technologies!
Marvel Your MQL5 Customers with a Usable Cocktail of Technologies!

Marvel Your MQL5 Customers with a Usable Cocktail of Technologies!

MQL5 provides programmers with a very complete set of functions and object-oriented API thanks to which they can do everything they want within the MetaTrader environment. However, Web Technology is an extremely versatile tool nowadays that may come to the rescue in some situations when you need to do something very specific, want to marvel your customers with something different or simply you do not have enough time to master a specific part of MT5 Standard Library. Today's exercise walks you through a practical example about how you can manage your development time at the same time as you also create an amazing tech cocktail.
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Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

Implementing the Generalized Hurst Exponent and the Variance Ratio test in MQL5

In this article, we investigate how the Generalized Hurst Exponent and the Variance Ratio test can be utilized to analyze the behaviour of price series in MQL5.
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Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5

Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.
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Data Science and Machine Learning (Part 07): Polynomial Regression

Data Science and Machine Learning (Part 07): Polynomial Regression

Unlike linear regression, polynomial regression is a flexible model aimed to perform better at tasks the linear regression model could not handle, Let's find out how to make polynomial models in MQL5 and make something positive out of it.
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Graph Theory: Dijkstra's Algorithm Applied in Trading

Graph Theory: Dijkstra's Algorithm Applied in Trading

Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.
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Larry Williams Market Secrets (Part 11): Detecting Smash Day Reversals with a Custom Indicator

Larry Williams Market Secrets (Part 11): Detecting Smash Day Reversals with a Custom Indicator

We convert Larry Williams’ Smash Day reversal rules into a practical MQL5 indicator that flags confirmed setups with arrows. Step by step, the text shows buffer binding, plot properties, historical mapping, and real‑time updates inside OnCalculate. Adjustable lookback parameters and clean chart rendering help you detect valid reversals quickly while keeping final trade decisions discretionary and context‑driven.
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Optimizing Long-Term Trades: Engulfing Candles and Liquidity Strategies

Optimizing Long-Term Trades: Engulfing Candles and Liquidity Strategies

This is a high-timeframe-based EA that makes long-term analyses, trading decisions, and executions based on higher-timeframe analyses of W1, D1, and MN. This article will explore in detail an EA that is specifically designed for long-term traders who are patient enough to withstand and hold their positions during tumultuous lower time frame price action without changing their bias frequently until take-profit targets are hit.
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Neural networks made easy (Part 67): Using past experience to solve new tasks

Neural networks made easy (Part 67): Using past experience to solve new tasks

In this article, we continue discussing methods for collecting data into a training set. Obviously, the learning process requires constant interaction with the environment. However, situations can be different.
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Larry Williams Market Secrets (Part 14): Detecting Hidden Smash Day Reversals with a Custom Indicator

Larry Williams Market Secrets (Part 14): Detecting Hidden Smash Day Reversals with a Custom Indicator

This article develops a practical MQL5 indicator that identifies Hidden Smash Day bars by strict numeric criteria and optional confirmation on the following session. We cover detection routines, buffer registration, and plot configuration to place arrows at valid bars. The approach delivers stable, non-repainting signals for historical testing and real-time monitoring.
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Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)

Modified Grid-Hedge EA in MQL5 (Part IV): Optimizing Simple Grid Strategy (I)

In this fourth part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Grid EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.
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Neural networks made easy (Part 32): Distributed Q-Learning

Neural networks made easy (Part 32): Distributed Q-Learning

We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.
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Population optimization algorithms: Grey Wolf Optimizer (GWO)

Population optimization algorithms: Grey Wolf Optimizer (GWO)

Let's consider one of the newest modern optimization algorithms - Grey Wolf Optimization. The original behavior on test functions makes this algorithm one of the most interesting among the ones considered earlier. This is one of the top algorithms for use in training neural networks, smooth functions with many variables.
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Developing a Replay System — Market simulation (Part 02): First experiments (II)

Developing a Replay System — Market simulation (Part 02): First experiments (II)

This time, let's try a different approach to achieve the 1 minute goal. However, this task is not as simple as one might think.