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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Most notable Artificial Cooperative Search algorithm modifications (ACSm)
Here we will consider the evolution of the ACS algorithm: three modifications aimed at improving the convergence characteristics and the algorithm efficiency. Transformation of one of the leading optimization algorithms. From matrix modifications to revolutionary approaches regarding population formation.
Artificial Ecosystem-based Optimization (AEO) algorithm
The article considers a metaheuristic Artificial Ecosystem-based Optimization (AEO) algorithm, which simulates interactions between ecosystem components by creating an initial population of solutions and applying adaptive update strategies, and describes in detail the stages of AEO operation, including the consumption and decomposition phases, as well as different agent behavior strategies. The article introduces the features and advantages of this algorithm.
Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)
Most modern multimodal time series forecasting methods use the independent channels approach. This ignores the natural dependence of different channels of the same time series. Smart use of two approaches (independent and mixed channels) is the key to improving the performance of the models.
Permuting price bars in MQL5
In this article we present an algorithm for permuting price bars and detail how permutation tests can be used to recognize instances where strategy performance has been fabricated to deceive potential buyers of Expert Advisors.
Neural Networks in Trading: Exploring the Local Structure of Data
Effective identification and preservation of the local structure of market data in noisy conditions is a critical task in trading. The use of the Self-Attention mechanism has shown promising results in processing such data; however, the classical approach does not account for the local characteristics of the underlying structure. In this article, I introduce an algorithm capable of incorporating these structural dependencies.
MQL5 Wizard Techniques you should know (Part 69): Using Patterns of SAR and the RVI
The Parabolic-SAR (SAR) and the Relative Vigour Index (RVI) are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered in the past, is also complementary since SAR defines the trend while RVI checks momentum. As usual, we use the MQL5 wizard to build and test any potential this indicator pairing may have.
Build Self Optimizing Expert Advisors in MQL5 (Part 7): Trading With Multiple Periods At Once
In this series of articles, we have considered multiple different ways of identifying the best period to use our technical indicators with. Today, we shall demonstrate to the reader how they can instead perform the opposite logic, that is to say, instead of picking the single best period to use, we will demonstrate to the reader how to employ all available periods effectively. This approach reduces the amount of data discarded, and offers alternative use cases for machine learning algorithms beyond ordinary price prediction.
Neural Networks in Trading: Mask-Attention-Free Approach to Price Movement Forecasting
In this article, we will discuss the Mask-Attention-Free Transformer (MAFT) method and its application in the field of trading. Unlike traditional Transformers that require data masking when processing sequences, MAFT optimizes the attention process by eliminating the need for masking, significantly improving computational efficiency.
MQL5 Wizard Techniques you should know (Part 62): Using Patterns of ADX and CCI with Reinforcement-Learning TRPO
The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We continue where we left off in the last article by examining how in-use training, and updating of our developed model, can be made thanks to reinforcement-learning. We are using an algorithm we are yet to cover in these series, known as Trusted Region Policy Optimization. And, as always, Expert Advisor assembly by the MQL5 Wizard allows us to set up our model(s) for testing much quicker and also in a way where it can be distributed and tested with different signal types.
Installing MetaTrader 5 and Other MetaQuotes Apps on HarmonyOS NEXT
Easily install MetaTrader 5 and other MetaQuotes apps on HarmonyOS NEXT devices using DroiTong. A detailed step-by-step guide for your phone or laptop.
Neural Networks in Trading: Directional Diffusion Models (DDM)
In this article, we discuss Directional Diffusion Models that exploit data-dependent anisotropic and directed noise in a forward diffusion process to capture meaningful graph representations.
Mastering Log Records (Part 7): How to Show Logs on Chart
Learn how to display logs directly on the MetaTrader chart in an organized way, with frames, titles and automatic scrolling. In this article, we show you how to create a visual log system using MQL5, ideal for monitoring what your robot is doing in real time.
Visualizing deals on a chart (Part 1): Selecting a period for analysis
Here we are going to develop a script from scratch that simplifies unloading print screens of deals for analyzing trading entries. All the necessary information on a single deal is to be conveniently displayed on one chart with the ability to draw different timeframes.
Developing a Replay System (Part 33): Order System (II)
Today we will continue to develop the order system. As you will see, we will be massively reusing what has already been shown in other articles. Nevertheless, you will receive a small reward in this article. First, we will develop a system that can be used with a real trading server, both from a demo account or from a real one. We will make extensive use of the MetaTrader 5 platform, which will provide us with all the necessary support from the beginning.
Data label for time series mining (Part 5)：Apply and Test in EA Using Socket
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!
Neural Networks in Trading: Point Cloud Analysis (PointNet)
Direct point cloud analysis avoids unnecessary data growth and improves the performance of models in classification and segmentation tasks. Such approaches demonstrate high performance and robustness to perturbations in the original data.
Price Action Analysis Toolkit Development (Part 18): Introducing Quarters Theory (III) — Quarters Board
In this article, we enhance the original Quarters Script by introducing the Quarters Board, a tool that lets you toggle quarter levels directly on the chart without needing to revisit the code. You can easily activate or deactivate specific levels, and the EA also provides trend direction commentary to help you better understand market movements.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (V): AnalyticsPanel Class
In this discussion, we explore how to retrieve real-time market data and trading account information, perform various calculations, and display the results on a custom panel. To achieve this, we will dive deeper into developing an AnalyticsPanel class that encapsulates all these features, including panel creation. This effort is part of our ongoing expansion of the New Admin Panel EA, introducing advanced functionalities using modular design principles and best practices for code organization.
Neural Networks in Trading: Market Analysis Using a Pattern Transformer
When we use models to analyze the market situation, we mainly focus on the candlestick. However, it has long been known that candlestick patterns can help in predicting future price movements. In this article, we will get acquainted with a method that allows us to integrate both of these approaches.
Quantitative approach to risk management: Applying VaR model to optimize multi-currency portfolio using Python and MetaTrader 5
This article explores the potential of the Value at Risk (VaR) model for multi-currency portfolio optimization. Using the power of Python and the functionality of MetaTrader 5, we demonstrate how to implement VaR analysis for efficient capital allocation and position management. From theoretical foundations to practical implementation, the article covers all aspects of applying one of the most robust risk calculation systems – VaR – in algorithmic trading.
Combinatorially Symmetric Cross Validation In MQL5
In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.
MQL5 Wizard Techniques you should know (Part 36): Q-Learning with Markov Chains
Reinforcement Learning is one of the three main tenets in machine learning, alongside supervised learning and unsupervised learning. It is therefore concerned with optimal control, or learning the best long-term policy that will best suit the objective function. It is with this back-drop, that we explore its possible role in informing the learning-process to an MLP of a wizard assembled Expert Advisor.
Data Science and ML (Part 39): News + Artificial Intelligence, Would You Bet on it?
News drives the financial markets, especially major releases like Non-Farm Payrolls (NFPs). We've all witnessed how a single headline can trigger sharp price movements. In this article, we dive into the powerful intersection of news data and Artificial Intelligence.
MQL5 Wizard Techniques you should know (Part 67): Using Patterns of TRIX and the Williams Percent Range
The Triple Exponential Moving Average Oscillator (TRIX) and the Williams Percentage Range Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered recently, is also complementary given that TRIX defines the trend while Williams Percent Range affirms support and Resistance levels. As always, we use the MQL5 wizard to prototype any potential these two may have.
Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)
In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.
HTTP and Connexus (Part 2): Understanding HTTP Architecture and Library Design
This article explores the fundamentals of the HTTP protocol, covering the main methods (GET, POST, PUT, DELETE), status codes and the structure of URLs. In addition, it presents the beginning of the construction of the Conexus library with the CQueryParam and CURL classes, which facilitate the manipulation of URLs and query parameters in HTTP requests.
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.
Category Theory in MQL5 (Part 12): Orders
This article which is part of a series that follows Category Theory implementation of Graphs in MQL5, delves in Orders. We examine how concepts of Order-Theory can support monoid sets in informing trade decisions by considering two major ordering types.
Developing a Replay System (Part 67): Refining the Control Indicator
In this article, we'll look at what can be achieved with a little code refinement. This refinement is aimed at simplifying our code, making more use of MQL5 library calls and, above all, making it much more stable, secure and easy to use in other projects that we may develop in the future.
Data Science and ML (Part 35): NumPy in MQL5 – The Art of Making Complex Algorithms with Less Code
NumPy library is powering almost all the machine learning algorithms to the core in Python programming language, In this article we are going to implement a similar module which has a collection of all the complex code to aid us in building sophisticated models and algorithms of any kind.
Feature Engineering With Python And MQL5 (Part II): Angle Of Price
There are many posts in the MQL5 Forum asking for help calculating the slope of price changes. This article will demonstrate one possible way of calculating the angle formed by the changes in price in any market you wish to trade. Additionally, we will answer if engineering this new feature is worth the extra effort and time invested. We will explore if the slope of the price can improve any of our AI model's accuracy when forecasting the USDZAR pair on the M1.
Neural Networks in Trading: Transformer for the Point Cloud (Pointformer)
In this article, we will talk about algorithms for using attention methods in solving problems of detecting objects in a point cloud. Object detection in point clouds is important for many real-world applications.
Creating a Trading Administrator Panel in MQL5 (Part III): Extending Built-in Classes for Theme Management (II)
In this discussion, we will carefully extend the existing Dialog library to incorporate theme management logic. Furthermore, we will integrate methods for theme switching into the CDialog, CEdit, and CButton classes utilized in our Admin Panel project. Continue reading for more insightful perspectives.
MQL5 Wizard Techniques you should know (Part 66): Using Patterns of FrAMA and the Force Index with the Dot Product Kernel
The FrAMA Indicator and the Force Index Oscillator are trend and volume tools that could be paired when developing an Expert Advisor. We continue from our last article that introduced this pair by considering machine learning applicability to the pair. We are using a convolution neural network that uses the dot-product kernel in making forecasts with these indicators’ inputs. This is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Creating a Trading Administrator Panel in MQL5 (Part IX): Code Organization (III): Communication Module
Join us for an in-depth discussion on the latest advancements in MQL5 interface design as we unveil the redesigned Communications Panel and continue our series on building the New Admin Panel using modularization principles. We'll develop the CommunicationsDialog class step by step, thoroughly explaining how to inherit it from the Dialog class. Additionally, we'll leverage arrays and ListView class in our development. Gain actionable insights to elevate your MQL5 development skills—read through the article and join the discussion in the comments section!
Developing A Swing Entries Monitoring (EA)
As the year approaches its end, long-term traders often reflect on market history to analyze its behavior and trends, aiming to project potential future movements. In this article, we will explore the development of a long-term entry monitoring Expert Advisor (EA) using MQL5. The objective is to address the challenge of missed long-term trading opportunities caused by manual trading and the absence of automated monitoring systems. We'll use one of the most prominently traded pairs as an example to strategize and develop our solution effectively.
Resampling techniques for prediction and classification assessment in MQL5
In this article, we will explore and implement, methods for assessing model quality that utilize a single dataset as both training and validation sets.
Developing an MQTT client for MetaTrader 5: a TDD approach — Part 2
This article is part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part we describe our code organization, the first header files and classes, and how we are writing our tests. This article also includes brief notes about the Test-Driven-Development practice and how we are applying it to this project.
MQL5 Wizard Techniques you should know (Part 59): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.
MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks
The Deep-Q-Network is a reinforcement learning algorithm that engages neural networks in projecting the next Q-value and ideal action during the training process of a machine learning module. We have already considered an alternative reinforcement learning algorithm, Q-Learning. This article therefore presents another example of how an MLP trained with reinforcement learning, can be used within a custom signal class.