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 88): Time-Series Dense Encoder (TiDE)

Neural Networks Made Easy (Part 88): Time-Series Dense Encoder (TiDE)

In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.
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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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Mastering File Operations in MQL5: From Basic I/O to Building a Custom CSV Reader

Mastering File Operations in MQL5: From Basic I/O to Building a Custom CSV Reader

This article focuses on essential MQL5 file-handling techniques, spanning trade logs, CSV processing, and external data integration. It offers both conceptual understanding and hands-on coding guidance. Readers will learn to build a custom CSV importer class step-by-step, gaining practical skills for real-world applications.
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 Tools (Part 7): Informational Dashboard for Multi-Symbol Position and Account Monitoring

MQL5 Trading Tools (Part 7): Informational Dashboard for Multi-Symbol Position and Account Monitoring

In this article, we develop an informational dashboard in MQL5 for monitoring multi-symbol positions and account metrics like balance, equity, and free margin. We implement a sortable grid with real-time updates, CSV export, and a glowing header effect to enhance usability and visual appeal.
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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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How to view deals directly on the chart without weltering in trading history

How to view deals directly on the chart without weltering in trading history

In this article, we will create a simple tool for convenient viewing of positions and deals directly on the chart with key navigation. This will allow traders to visually examine individual deals and receive all the information about trading results right on the spot.
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Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (Final Part)

Neural Networks in Trading: An Ensemble of Agents with Attention Mechanisms (Final Part)

In the previous article, we introduced the multi-agent adaptive framework MASAAT, which uses an ensemble of agents to perform cross-analysis of multimodal time series at different data scales. Today we will continue implementing the approaches of this framework in MQL5 and bring this work to a logical conclusion.
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Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence

Self Optimizing Expert Advisors in MQL5 (Part 17): Ensemble Intelligence

All algorithmic trading strategies are difficult to set up and maintain, regardless of complexity—a challenge shared by beginners and experts alike. This article introduces an ensemble framework where supervised models and human intuition work together to overcome their shared limitations. By aligning a moving average channel strategy with a Ridge Regression model on the same indicators, we achieve centralized control, faster self-correction, and profitability from otherwise unprofitable systems.
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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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Finding custom currency pair patterns in Python using MetaTrader 5

Finding custom currency pair patterns in Python using MetaTrader 5

Are there any repeating patterns and regularities in the Forex market? I decided to create my own pattern analysis system using Python and MetaTrader 5. A kind of symbiosis of math and programming for conquering Forex.
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Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies

Developing a multi-currency Expert Advisor (Part 2): Transition to virtual positions of trading strategies

Let's continue developing a multi-currency EA with several strategies working in parallel. Let's try to move all the work associated with opening market positions from the strategy level to the level of the EA managing the strategies. The strategies themselves will trade only virtually, without opening market positions.
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Neural Networks in Trading: Optimizing the Transformer for Time Series Forecasting (LSEAttention)

Neural Networks in Trading: Optimizing the Transformer for Time Series Forecasting (LSEAttention)

The LSEAttention framework offers improvements to the Transformer architecture. It was designed specifically for long-term multivariate time series forecasting. The approaches proposed by the authors of the method can be applied to solve problems of entropy collapse and learning instability, which are often encountered with vanilla Transformer.
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Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
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Risk Management (Part 2): Implementing Lot Calculation in a Graphical Interface

Risk Management (Part 2): Implementing Lot Calculation in a Graphical Interface

In this article, we will look at how to improve and more effectively apply the concepts presented in the previous article using the powerful MQL5 graphical control libraries. We'll go step by step through the process of creating a fully functional GUI. I'll be explaining the ideas behind it, as well as the purpose and operation of each method used. Additionally, at the end of the article, we will test the panel we created to ensure it functions correctly and meets its stated goals.
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MQL5 Wizard Techniques you should know (Part 71): Using Patterns of MACD and the OBV

MQL5 Wizard Techniques you should know (Part 71): Using Patterns of MACD and the OBV

The Moving-Average-Convergence-Divergence (MACD) oscillator and the On-Balance-Volume (OBV) oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This pairing, as is practice in these article series, is complementary with the MACD affirming trends while OBV checks volume. As usual, we use the MQL5 wizard to build and test any potential these two may possess.
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Manual Backtesting with On-Chart Buttons in the MetaTrader 5 Strategy Tester

Manual Backtesting with On-Chart Buttons in the MetaTrader 5 Strategy Tester

Learn how to build a manual backtesting EA for MetaTrader 5's visual tester by adding chart buttons with CButton, executing orders through CTrade, and filtering positions with a magic number. The article implements Buy/Sell and Close All controls, configurable lot size and initial SL, and a trailing stop via CPositionInfo. You will also see how to load indicators with tester.tpl to validate ideas faster before automation and narrow optimization ranges.
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Creating 3D bars based on time, price and volume

Creating 3D bars based on time, price and volume

The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.
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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Moving to MQL5 Algo Forge (Part 4): Working with Versions and Releases

Moving to MQL5 Algo Forge (Part 4): Working with Versions and Releases

We'll continue developing the Simple Candles and Adwizard projects, while also describing the finer aspects of using the MQL5 Algo Forge version control system and repository.
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Creating 3D bars based on time, price and volume

Creating 3D bars based on time, price and volume

The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.
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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Creating 3D bars based on time, price and volume

Creating 3D bars based on time, price and volume

The article dwells on multivariate 3D price charts and their creation. We will also consider how 3D bars predict price reversals, and how Python and MetaTrader 5 allow us to plot these volume bars in real time.
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Creating a market making algorithm in MQL5

Creating a market making algorithm in MQL5

How do market makers work? Let's consider this issue and create a primitive market-making algorithm.
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MetaTrader Meets Google Sheets with Pythonanywhere: A Guide to Secure Data Flow

MetaTrader Meets Google Sheets with Pythonanywhere: A Guide to Secure Data Flow

This article demonstrates a secure way to export MetaTrader data to Google Sheets. Google Sheet is the most valuable solution as it is cloud based and the data saved in there can be accessed anytime and from anywhere. So traders can access trading and related data exported to google sheet and do further analysis for future trading anytime and wherever they are at the moment.
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Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++)

Neural Networks in Trading: Hybrid Graph Sequence Models (GSM++)

Hybrid graph sequence models (GSM++) combine the advantages of different architectures to provide high-fidelity data analysis and optimized computational costs. These models adapt effectively to dynamic market data, improving the presentation and processing of financial information.
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Benefiting from Forex market seasonality

Benefiting from Forex market seasonality

We are all familiar with the concept of seasonality, for example, we are all accustomed to rising prices for fresh vegetables in winter or rising fuel prices during severe frosts, but few people know that similar patterns exist in the Forex market.
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Deconstructing examples of trading strategies in the client terminal

Deconstructing examples of trading strategies in the client terminal

The article uses block diagrams to examine the logic of the candlestick-based training EAs located in the Experts\Free Robots folder of the terminal.
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From Novice to Expert: Collaborative Debugging in MQL5

From Novice to Expert: Collaborative Debugging in MQL5

Problem-solving can establish a concise routine for mastering complex skills, such as programming in MQL5. This approach allows you to concentrate on solving problems while simultaneously developing your skills. The more problems you tackle, the more advanced expertise is transferred to your brain. Personally, I believe that debugging is the most effective way to master programming. Today, we will walk through the code-cleaning process and discuss the best techniques for transforming a messy program into a clean, functional one. Read through this article and uncover valuable insights.
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MQL5 Trading Toolkit (Part 8): How to Implement and Use the History Manager EX5 Library in Your Codebase

MQL5 Trading Toolkit (Part 8): How to Implement and Use the History Manager EX5 Library in Your Codebase

Discover how to effortlessly import and utilize the History Manager EX5 library in your MQL5 source code to process trade histories in your MetaTrader 5 account in this series' final article. With simple one-line function calls in MQL5, you can efficiently manage and analyze your trading data. Additionally, you will learn how to create different trade history analytics scripts and develop a price-based Expert Advisor as practical use-case examples. The example EA leverages price data and the History Manager EX5 library to make informed trading decisions, adjust trade volumes, and implement recovery strategies based on previously closed trades.
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Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation

Overcoming The Limitation of Machine Learning (Part 5): A Quick Recap of Time Series Cross Validation

In this series of articles, we look at the challenges faced by algorithmic traders when deploying machine-learning-powered trading strategies. Some challenges within our community remain unseen because they demand deeper technical understanding. Today’s discussion acts as a springboard toward examining the blind spots of cross-validation in machine learning. Although often treated as routine, this step can easily produce misleading or suboptimal results if handled carelessly. This article briefly revisits the essentials of time series cross-validation to prepare us for more in-depth insight into its hidden blind spots.
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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.
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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Experiments with neural networks (Part 4): Templates

Experiments with neural networks (Part 4): Templates

In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading. Simple explanation.
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Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python

Developing a multi-currency Expert Advisor (Part 19): Creating stages implemented in Python

So far we have considered the automation of launching sequential procedures for optimizing EAs exclusively in the standard strategy tester. But what if we would like to perform some handling of the obtained data using other means between such launches? We will attempt to add the ability to create new optimization stages performed by programs written in Python.
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Understand and efficiently use OpenCL API by recreating built-in support as DLL on Linux (Part 1): Motivation and validation

Understand and efficiently use OpenCL API by recreating built-in support as DLL on Linux (Part 1): Motivation and validation

Bulit-in OpenCL support in MetaTrader 5 still has a major problem especially the one about device selection error 5114 resulting from unable to create an OpenCL context using CL_USE_GPU_ONLY, or CL_USE_GPU_DOUBLE_ONLY although it properly detects GPU. It works fine with directly using of ordinal number of GPU device we found in Journal tab, but that's still considered a bug, and users should not hard-code a device. We will solve it by recreating an OpenCL support as DLL with C++ on Linux. Along the journey, we will get to know OpenCL from concept to best practices in its API usage just enough for us to put into great use later when we deal with DLL implementation in C++ and consume it with MQL5.
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Neural Networks Made Easy (Part 87): Time Series Patching

Neural Networks Made Easy (Part 87): Time Series Patching

Forecasting plays an important role in time series analysis. In the new article, we will talk about the benefits of time series patching.
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Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part)

Neural Networks in Trading: A Multimodal, Tool-Augmented Agent for Financial Markets (Final Part)

We continue to develop the algorithms for FinAgent, a multimodal financial trading agent designed to analyze multimodal market dynamics data and historical trading patterns.
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MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library

MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library

Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.
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News Trading Made Easy (Part 3): Performing Trades

News Trading Made Easy (Part 3): Performing Trades

In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.