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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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.
Neural Network in Practice: Pseudoinverse (I)
Today we will begin to consider how to implement the calculation of pseudo-inverse in pure MQL5 language. The code we are going to look at will be much more complex for beginners than I expected, and I'm still figuring out how to explain it in a simple way. So for now, consider this an opportunity to learn some unusual code. Calmly and attentively. Although it is not aimed at efficient or quick application, its goal is to be as didactic as possible.
Category Theory in MQL5 (Part 23): A different look at the Double Exponential Moving Average
In this article we continue with our theme in the last of tackling everyday trading indicators viewed in a ‘new’ light. We are handling horizontal composition of natural transformations for this piece and the best indicator for this, that expands on what we just covered, is the double exponential moving average (DEMA).
Creating Custom Indicators in MQL5 (Part 4): Smart WaveTrend Crossover with Dual Oscillators
In this article, we develop a custom indicator in MQL5 called Smart WaveTrend Crossover, utilizing dual WaveTrend oscillators—one for generating crossover signals and another for trend filtering—with customizable parameters for channel, average, and moving average lengths. The indicator plots colored candles based on the trend direction, displays buy and sell arrow signals on crossovers, and includes options to enable trend confirmation and adjust visual elements like colors and offsets.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part II)
Today, we are discussing a working Telegram integration for MetaTrader 5 Indicator notifications using the power of MQL5, in partnership with Python and the Telegram Bot API. We will explain everything in detail so that no one misses any point. By the end of this project, you will have gained valuable insights to apply in your projects.
Neural Network in Practice: Straight Line Function
In this article, we will take a quick look at some methods to get a function that can represent our data in the database. I will not go into detail about how to use statistics and probability studies to interpret the results. Let's leave that for those who really want to delve into the mathematical side of the matter. Exploring these questions will be critical to understanding what is involved in studying neural networks. Here we will consider this issue quite calmly.
Neural networks made easy (Part 62): Using Decision Transformer in hierarchical models
In recent articles, we have seen several options for using the Decision Transformer method. The method allows analyzing not only the current state, but also the trajectory of previous states and actions performed in them. In this article, we will focus on using this method in hierarchical models.
Artificial Bee Hive Algorithm (ABHA): Tests and results
In this article, we will continue exploring the Artificial Bee Hive Algorithm (ABHA) by diving into the code and considering the remaining methods. As you might remember, each bee in the model is represented as an individual agent whose behavior depends on internal and external information, as well as motivational state. We will test the algorithm on various functions and summarize the results by presenting them in the rating table.
Propensity score in causal inference
The article examines the topic of matching in causal inference. Matching is used to compare similar observations in a data set. This is necessary to correctly determine causal effects and get rid of bias. The author explains how this helps in building trading systems based on machine learning, which become more stable on new data they were not trained on. The propensity score plays a central role and is widely used in causal inference.
Introduction to MQL5 (Part 22): Building an Expert Advisor for the 5-0 Harmonic Pattern
This article explains how to detect and trade the 5-0 harmonic pattern in MQL5, validate it using Fibonacci levels, and display it on the chart.
Neural networks made easy (Part 65): Distance Weighted Supervised Learning (DWSL)
In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.
Neural networks made easy (Part 64): ConserWeightive Behavioral Cloning (CWBC) method
As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.
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.
Neural Networks in Trading: Spatio-Temporal Neural Network (STNN)
In this article we will talk about using space-time transformations to effectively predict upcoming price movement. To improve the numerical prediction accuracy in STNN, a continuous attention mechanism is proposed that allows the model to better consider important aspects of the data.
Developing a Replay System (Part 71): Getting the Time Right (IV)
In this article, we will look at how to implement what was shown in the previous article related to our replay/simulation service. As in many other things in life, problems are bound to arise. And this case was no exception. In this article, we continue to improve things. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
How can century-old functions update your trading strategies?
This article considers the Rademacher and Walsh functions. We will explore ways to apply these functions to financial time series analysis and also consider various applications for them in trading.
Developing a Replay System — Market simulation (Part 12): Birth of the SIMULATOR (II)
Developing a simulator can be much more interesting than it seems. Today we'll take a few more steps in this direction because things are getting more interesting.
Implementing Practical Modules from Other Languages in MQL5 (Part 03): Schedule Module from Python, the OnTimer Event on Steroids
The schedule module in Python offers a simple way to schedule repeated tasks. While MQL5 lacks a built-in equivalent, in this article we’ll implement a similar library to make it easier to set up timed events in MetaTrader 5.
Data Science and ML (Part 43): Hidden Patterns Detection in Indicators Data Using Latent Gaussian Mixture Models (LGMM)
Have you ever looked at the chart and felt that strange sensation… that there’s a pattern hidden just beneath the surface? A secret code that might reveal where prices are headed if only you could crack it? Meet LGMM, the Market’s Hidden Pattern Detector. A machine learning model that helps identify those hidden patterns in the market.
Population optimization algorithms: Simulated Annealing (SA) algorithm. Part I
The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.
MQL5 Wizard Techniques you should know (Part 51): Reinforcement Learning with SAC
Soft Actor Critic is a Reinforcement Learning algorithm that utilizes 3 neural networks. An actor network and 2 critic networks. These machine learning models are paired in a master slave partnership where the critics are modelled to improve the forecast accuracy of the actor network. While also introducing ONNX in these series, we explore how these ideas could be put to test as a custom signal of a wizard assembled Expert Advisor.
Developing a Replay System (Part 75): New Chart Trade (II)
In this article, we will talk about the C_ChartFloatingRAD class. This is what makes Chart Trade work. However, the explanation does not end there. We will complete it in the next article, as the content of this article is quite extensive and requires deep understanding. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Neural networks made easy (Part 52): Research with optimism and distribution correction
As the model is trained based on the experience reproduction buffer, the current Actor policy moves further and further away from the stored examples, which reduces the efficiency of training the model as a whole. In this article, we will look at the algorithm of improving the efficiency of using samples in reinforcement learning algorithms.
MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
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.
Population optimization algorithms: Changing shape, shifting probability distributions and testing on Smart Cephalopod (SC)
The article examines the impact of changing the shape of probability distributions on the performance of optimization algorithms. We will conduct experiments using the Smart Cephalopod (SC) test algorithm to evaluate the efficiency of various probability distributions in the context of optimization problems.
MQL5 Wizard Techniques you should know (Part 11): Number Walls
Number Walls are a variant of Linear Shift Back Registers that prescreen sequences for predictability by checking for convergence. We look at how these ideas could be of use in MQL5.
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)
Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.
Developing a Replay System (Part 61): Playing the service (II)
In this article, we will look at changes that will allow the replay/simulation system to operate more efficiently and securely. I will also not leave without attention those who want to get the most out of using classes. In addition, we will consider a specific problem in MQL5 that reduces code performance when working with classes, and explain how to solve it.
From Basic to Intermediate: FOR Statement
In this article, we will look at the most basic concepts of the FOR statement. It is very important to understand everything that will be shown here. Unlike the other statements we've talked about so far, the FOR statement has some quirks that quickly make it very complex. So don't let stuff like this accumulate. Start studying and practicing as soon as possible.
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
From Basic to Intermediate: WHILE and DO WHILE Statements
In this article, we will take a practical and very visual look at the first loop statement. Although many beginners feel intimidated when faced with the task of creating loops, knowing how to do it correctly and safely can only come with experience and practice. But who knows, maybe I can reduce your troubles and suffering by showing you the main issues and precautions to take when using loops in your code.
From Novice to Expert: Animated News Headline Using MQL5 (VIII) — Quick Trade Buttons for News Trading
While algorithmic trading systems manage automated operations, many news traders and scalpers prefer active control during high-impact news events and fast-paced market conditions, requiring rapid order execution and management. This underscores the need for intuitive front-end tools that integrate real-time news feeds, economic calendar data, indicator insights, AI-driven analytics, and responsive trading controls.
From Novice to Expert: Backend Operations Monitor using MQL5
Using a ready-made solution in trading without concerning yourself with the internal workings of the system may sound comforting, but this is not always the case for developers. Eventually, an upgrade, misperformance, or unexpected error will arise, and it becomes essential to trace exactly where the issue originates to diagnose and resolve it quickly. Today’s discussion focuses on uncovering what normally happens behind the scenes of a trading Expert Advisor, and on developing a custom dedicated class for displaying and logging backend processes using MQL5. This gives both developers and traders the ability to quickly locate errors, monitor behavior, and access diagnostic information specific to each EA.
Evolutionary trading algorithm with reinforcement learning and extinction of feeble individuals (ETARE)
In this article, I introduce an innovative trading algorithm that combines evolutionary algorithms with deep reinforcement learning for Forex trading. The algorithm uses the mechanism of extinction of inefficient individuals to optimize the trading strategy.
Market Simulation (Part 01): Cross Orders (I)
Today we will begin the second stage, where we will look at the market replay/simulation system. First, we will show a possible solution for cross orders. I will show you the solution, but it is not final yet. It will be a possible solution to a problem that we will need to solve in the near future.
DoEasy. Controls (Part 14): New algorithm for naming graphical elements. Continuing work on the TabControl WinForms object
In this article, I will create a new algorithm for naming all graphical elements meant for building custom graphics, as well as continue developing the TabControl WinForms object.
Neural Networks in Trading: Two-Dimensional Connection Space Models (Final Part)
We continue to explore the innovative Chimera framework – a two-dimensional state-space model that uses neural network technologies to analyze multidimensional time series. This method provides high forecasting accuracy with low computational cost.
Developing a Replay System (Part 28): Expert Advisor project — C_Mouse class (II)
When people started creating the first systems capable of computing, everything required the participation of engineers, who had to know the project very well. We are talking about the dawn of computer technology, a time when there were not even terminals for programming. As it developed and more people got interested in being able to create something, new ideas and ways of programming emerged which replaced the previous-style changing of connector positions. This is when the first terminals appeared.
Overcoming The Limitation of Machine Learning (Part 6): Effective Memory Cross Validation
In this discussion, we contrast the classical approach to time series cross-validation with modern alternatives that challenge its core assumptions. We expose key blind spots in the traditional method—especially its failure to account for evolving market conditions. To address these gaps, we introduce Effective Memory Cross-Validation (EMCV), a domain-aware approach that questions the long-held belief that more historical data always improves performance.