Articles on the MQL5 programming and use of trading robots

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Expert Advisors created for the MetaTrader platform perform a variety of functions implemented by their developers. Trading robots can track financial symbols 24 hours a day, copy deals, create and send reports, analyze news and even provide specific custom graphical interface.

The articles describe programming techniques, mathematical ideas for data processing, tips on creating and ordering of trading robots.

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MQL5 Wizard Techniques you should know (Part 48): Bill Williams Alligator

MQL5 Wizard Techniques you should know (Part 48): Bill Williams Alligator

The Alligator Indicator, which was the brain child of Bill Williams, is a versatile trend identification indicator that yields clear signals and is often combined with other indicators. The MQL5 wizard classes and assembly allow us to test a variety of signals on a pattern basis, and so we consider this indicator as well.
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Developing a trading Expert Advisor from scratch (Part 29): The talking platform

Developing a trading Expert Advisor from scratch (Part 29): The talking platform

In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.
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Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?

Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?

Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
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Creating an EA that works automatically (Part 14): Automation (VI)

Creating an EA that works automatically (Part 14): Automation (VI)

In this article, we will put into practice all the knowledge from this series. We will finally build a 100% automated and functional system. But before that, we still have to learn one last detail.
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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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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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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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Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap

Developing a trading Expert Advisor from scratch (Part 8): A conceptual leap

What is the easiest way to implement new functionality? In this article, we will take one step back and then two steps forward.
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Neural networks made easy (Part 24): Improving the tool for Transfer Learning

Neural networks made easy (Part 24): Improving the tool for Transfer Learning

In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.
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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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Neural networks made easy (Part 19): Association rules using MQL5

Neural networks made easy (Part 19): Association rules using MQL5

We continue considering association rules. In the previous article, we have discussed theoretical aspect of this type of problem. In this article, I will show the implementation of the FP Growth method using MQL5. We will also test the implemented solution using real data.
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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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How to create a custom True Strength Index indicator using MQL5

How to create a custom True Strength Index indicator using MQL5

Here is a new article about how to create a custom indicator. This time we will work with the True Strength Index (TSI) and will create an Expert Advisor based on it.
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Price Movement: Mathematical Models and Technical Analysis

Price Movement: Mathematical Models and Technical Analysis

Forecasting the movements of currency pairs is an important factor in trading success. This article explores various price movement models, analyzes their advantages and disadvantages, and explores their practical application in trading strategies. We will consider approaches that allow us to identify hidden patterns and improve the accuracy of forecasts.
Prices in DoEasy library (part 59): Object to store data of one tick
Prices in DoEasy library (part 59): Object to store data of one tick

Prices in DoEasy library (part 59): Object to store data of one tick

From this article on, start creating library functionality to work with price data. Today, create an object class which will store all price data which arrived with yet another tick.
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Creating an EA that works automatically (Part 05): Manual triggers (II)

Creating an EA that works automatically (Part 05): Manual triggers (II)

Today we'll see how to create an Expert Advisor that simply and safely works in automatic mode. At the end of the previous article, I suggested that it would be appropriate to allow manual use of the EA, at least for a while.
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Build Self Optimizing Expert Advisors in MQL5 (Part 5): Self Adapting Trading Rules

Build Self Optimizing Expert Advisors in MQL5 (Part 5): Self Adapting Trading Rules

The best practices, defining how to safely us an indicator, are not always easy to follow. Quiet market conditions may surprisingly produce readings on the indicator that do not qualify as a trading signal, leading to missed opportunities for algorithmic traders. This article will suggest a potential solution to this problem, as we discuss how to build trading applications capable of adapting their trading rules to the available market data.
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Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)

Multiple indicators on one chart (Part 05): Turning MetaTrader 5 into a RAD system (I)

There are a lot of people who do not know how to program but they are quite creative and have great ideas. However, the lack of programming knowledge prevents them from implementing these ideas. Let's see together how to create a Chart Trade using the MetaTrader 5 platform itself, as if it were an IDE.
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Building a Smart Trade Manager in MQL5: Automate Break-Even, Trailing Stop, and Partial Close

Building a Smart Trade Manager in MQL5: Automate Break-Even, Trailing Stop, and Partial Close

Learn how to build a Smart Trade Manager Expert Advisor in MQL5 that automates trade management with break-even, trailing stop, and partial close features. A practical, step-by-step guide for traders who want to save time and improve consistency through automation.
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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.
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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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Building a Research-Grounded Grid EA in MQL5: Why Most Grid EAs Fail and What Taranto Proved

Building a Research-Grounded Grid EA in MQL5: Why Most Grid EAs Fail and What Taranto Proved

This article implements a regime-adaptive grid trading EA based on the PhD research of Aldo Taranto. It presents a regime‑adaptive grid trading EA that constrains risk through restartable cycles and equity‑based safeguards. We explain why naive grids fail (variance growth and almost‑sure ruin), derive the loss formula for real‑time exposure, and implement regime‑aware gating, ATR‑dynamic spacing, and a live kill switch. Readers get the mathematical tools and production patterns needed to build, test, and operate a constrained grid safely.
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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.
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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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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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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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MQL5 Trading Tools (Part 19): Building an Interactive Tools Palette for Chart Drawing

MQL5 Trading Tools (Part 19): Building an Interactive Tools Palette for Chart Drawing

In this article, we build an interactive tools palette in MQL5 for chart drawing, with draggable, resizable panels and theme switching. We add buttons for tools like crosshair, trendlines, lines, rectangles, Fibonacci, text, and arrows, handling mouse events for activation and instructions. This system improves trading analysis through a customizable UI, supporting real-time interactions on charts
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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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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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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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Introduction to MQL5 (Part 25): Building an EA that Trades with Chart Objects (II)

Introduction to MQL5 (Part 25): Building an EA that Trades with Chart Objects (II)

This article explains how to build an Expert Advisor (EA) that interacts with chart objects, particularly trend lines, to identify and trade breakout and reversal opportunities. You will learn how the EA confirms valid signals, manages trade frequency, and maintains consistency with user-selected strategies.
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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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Statistical Arbitrage Through Mean Reversion in Pairs Trading: Beating the Market by Math

Statistical Arbitrage Through Mean Reversion in Pairs Trading: Beating the Market by Math

This article describes the fundamentals of portfolio-level statistical arbitrage. Its goal is to facilitate the understanding of the principles of statistical arbitrage to readers without deep math knowledge and propose a starting point conceptual framework. The article includes a working Expert Advisor, some notes about its one-year backtest, and the respective backtest configuration settings (.ini file) for the reproduction of the experiment.
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Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market

Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market

Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.
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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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How to build and optimize a volume-based trading system (Chaikin Money Flow - CMF)

How to build and optimize a volume-based trading system (Chaikin Money Flow - CMF)

In this article, we will provide a volume-based indicator, Chaikin Money Flow (CMF) after identifying how it can be constructed, calculated, and used. We will understand how to build a custom indicator. We will share some simple strategies that can be used and then test them to understand which one is better.
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Neural Networks in Trading: An Agent with Layered Memory

Neural Networks in Trading: An Agent with Layered Memory

Layered memory approaches that mimic human cognitive processes enable the processing of complex financial data and adaptation to new signals, thereby improving the effectiveness of investment decisions in dynamic markets.
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Introduction to MQL5 (Part 24): Building an EA that Trades with Chart Objects

Introduction to MQL5 (Part 24): Building an EA that Trades with Chart Objects

This article teaches you how to create an Expert Advisor that detects support and resistance zones drawn on the chart and executes trades automatically based on them.
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Neural networks made easy (Part 16): Practical use of clustering

Neural networks made easy (Part 16): Practical use of clustering

In the previous article, we have created a class for data clustering. In this article, I want to share variants of the possible application of obtained results in solving practical trading tasks.
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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.