MQL4 and MQL5 Programming Articles
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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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.
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.
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!
Considering Orders in a Large Program
General principles of considering orders in a large and complex program are discussed.
The MQL5 Standard Library Explorer (Part 5): Multiple Signal Expert
In this session, we will build a sophisticated, multi-signal Expert Advisor using the MQL5 Standard Library. This approach allows us to seamlessly blend built-in signals with our own custom logic, demonstrating how to construct a powerful and flexible trading algorithm. For more, click to read further.
Introduction to MQL5 (Part 27): Mastering API and WebRequest Function in MQL5
This article introduces how to use the WebRequest() function and APIs in MQL5 to communicate with external platforms. You’ll learn how to create a Telegram bot, obtain chat and group IDs, and send, edit, and delete messages directly from MT5, building a strong foundation for mastering API integration in your future MQL5 projects.
Employing Game Theory Approaches in Trading Algorithms
We are creating an adaptive self-learning trading expert advisor based on DQN machine learning, with multidimensional causal inference. The EA will successfully trade simultaneously on 7 currency pairs. And agents of different pairs will exchange information with each other.
A Trader's Assistant Based on Extended MACD Analysis
Script 'Trader's Assistant' helps you to make a decision on opening positions, based on the extended analysis of the MACD status for the last three bars in the real-time trading on any timeframe. It can also be used for back testing.
Introduction to MQL5 (Part 21): Automating Harmonic Pattern Detection
Learn how to detect and display the Gartley harmonic pattern in MetaTrader 5 using MQL5. This article explains each step of the process, from identifying swing points to applying Fibonacci ratios and plotting the full pattern on the chart for clear visual confirmation.
Object Approach in MQL
This article will be interesting first of all for programmers both beginners and professionals working in MQL environment. Also it would be useful if this article were read by MQL environment developers and ideologists, because questions that are analyzed here may become projects for future implementation of MetaTrader and MQL.
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.
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.
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.
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.
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.
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.
Expert Advisors Based on Popular Trading Systems and Alchemy of Trading Robot Optimization (Part IV)
In this article the author continues to analyze implementation algorithms of simplest trading systems and introduces recording of optimization results in backtesting into one html file in the form of a table. The article will be useful for beginning traders and EA writers.
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.
Automating Trading Strategies in MQL5 (Part 49): The Quasimodo (QM) Reversal Pattern
In this article, we build an automated trading program in MQL5 that detects the Quasimodo reversal pattern from a zig-zag of confirmed swing pivots. We work through swing detection, pattern arming, retrace entries at the QM line, and structural stop placement with risk-based sizing. We also add trade management with breakeven, trailing, and partial closing to handle open positions.
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.
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.
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.
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.
Data Science and ML (Part 31): Using CatBoost AI Models for Trading
CatBoost AI models have gained massive popularity recently among machine learning communities due to their predictive accuracy, efficiency, and robustness to scattered and difficult datasets. In this article, we are going to discuss in detail how to implement these types of models in an attempt to beat the forex market.
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.
Master MQL5 from beginner to pro (Part V): Fundamental control flow operators
This article explores the key operators used to modify the program's execution flow: conditional statements, loops, and switch statements. Utilizing these operators will allow the functions we create to behave more "intelligently".
Quantum computing and trading: A fresh approach to price forecasts
The article describes an innovative approach to forecasting price movements in financial markets using quantum computing. The main focus is on the application of the Quantum Phase Estimation (QPE) algorithm to find prototypes of price patterns allowing traders to significantly speed up the market data analysis.
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.
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.
How to Cut an EA Code for an Easier Life and Fewer Errors
A simple concept described in the article allows those developing automated trading systems in MQL4 to simplify existing trading systems, as well as reduce time needed for development of new systems due to shorter codes.
From Novice to Expert: Detecting Liquidity Zone Flips Using MQL5
This article presents an MQL5 indicator that detects and manages liquidity zone flips. It identifies supply and demand zones from higher timeframes using a base–impulse pattern, applies objective breakout and impulse thresholds, and flips zones automatically when structure changes. The result is a dynamic support‑resistance map that reduces manual redraws and gives you clear, actionable context for signals and retests.
MetaTrader 5 Machine Learning Blueprint (Part 6): Engineering a Production-Grade Caching System
Tired of watching progress bars instead of testing trading strategies? Traditional caching fails financial ML, leaving you with lost computations and frustrating restarts. We've engineered a sophisticated caching architecture that understands the unique challenges of financial data—temporal dependencies, complex data structures, and the constant threat of look-ahead bias. Our three-layer system delivers dramatic speed improvements while automatically invalidating stale results and preventing costly data leaks. Stop waiting for computations and start iterating at the pace the markets demand.
Larry Williams Market Secrets (Part 9): Patterns to Profit
An empirical study of Larry Williams' short-term trading patterns, showing how classic setups can be automated in MQL5, tested on real market data, and evaluated for consistency, profitability, and practical trading value.
Implementing the SHA-256 Cryptographic Algorithm from Scratch in MQL5
Building DLL-free cryptocurrency exchange integrations has long been a challenge, but this solution provides a complete framework for direct market connectivity.
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.
Market Simulation (Part 06): Transferring Information from MetaTrader 5 to Excel
Many people, especially non=programmers, find it very difficult to transfer information between MetaTrader 5 and other programs. One such program is Excel. Many use Excel as a way to manage and maintain their risk control. It is an excellent program and easy to learn, even for those who are not VBA programmers. Here we will look at how to establish a connection between MetaTrader 5 and Excel (a very simple method).
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.
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.
MQL5 Trading Tools (Part 13): Creating a Canvas-Based Price Dashboard with Graph and Stats Panels
In this article, we develop a canvas-based price dashboard in MQL5 using the CCanvas class to create interactive panels for visualizing recent price graphs and account statistics, with support for background images, fog effects, and gradient fills. The system includes draggable and resizable features via mouse event handling, theme toggling between dark and light modes with dynamic color adjustments, and minimize/maximize controls for efficient chart space management.
News Trading Made Easy (Part 5): Performing Trades (II)
This article will expand on the trade management class to include buy-stop and sell-stop orders to trade news events and implement an expiration constraint on these orders to prevent any overnight trading. A slippage function will be embedded into the expert to try and prevent or minimize possible slippage that may occur when using stop orders in trading, especially during news events.