Python-MetaTrader 5 Strategy Tester (Part 02): Dealing with Bars, Ticks, and Overloading Built-in Functions in a Simulator
In this article, we introduce functions similar to those provided by the Python-MetaTrader 5 module, providing a simulator with a familiar interface and a custom way of handling bars and ticks internally.
Analyzing binary code of prices on the exchange (Part I): A new look at technical analysis
This article presents an innovative approach to technical analysis based on converting price movements into binary code. The author demonstrates how various aspects of market behavior — from simple price movements to complex patterns — can be encoded in a sequence of zeros and ones.
MQL5 Trading Tools (Part 14): Pixel-Perfect Scrollable Text Canvas with Antialiasing and Rounded Scrollbar
In this article, we enhance the canvas-based price dashboard in MQL5 by adding a pixel-perfect scrollable text panel for usage guides, overcoming native scrolling limitations through custom antialiasing and a rounded scrollbar design with hover-expand functionality. The text panel supports themed backgrounds with opacity, dynamic line wrapping for content like instructions and contacts, and interactive navigation via up/down buttons, slider dragging, and mouse wheel scrolling within the body area.
Neural Networks in Trading: Two-Dimensional Connection Space Models (Chimera)
In this article, we will explore the innovative Chimera framework: a two-dimensional state-space model that uses neural networks to analyze multivariate time series. This method offers high accuracy with low computational cost, outperforming traditional approaches and Transformer architectures.
Arithmetic Optimization Algorithm (AOA): From AOA to SOA (Simple Optimization Algorithm)
In this article, we present the Arithmetic Optimization Algorithm (AOA) based on simple arithmetic operations: addition, subtraction, multiplication and division. These basic mathematical operations serve as the foundation for finding optimal solutions to various problems.
Non-stationary processes and spurious regression
The article demonstrates spurious regression occurring when attempting to apply regression analysis to non-stationary processes using Monte Carlo simulation.
The MQL5 Standard Library Explorer (Part 8) : The Hybrid Trades Journal Logging with CFile
In this article, we explore the File Operations classes of the MQL5 Standard Library to build a robust reporting module that automatically generates Excel-ready CSV files. Along the way, we clearly distinguish between manually executed trades and algorithmically executed orders, laying the groundwork for reliable, auditable trade reporting.
MQL5 Wizard Techniques you should know (Part 10). The Unconventional RBM
Restrictive Boltzmann Machines are at the basic level, a two-layer neural network that is proficient at unsupervised classification through dimensionality reduction. We take its basic principles and examine if we were to re-design and train it unorthodoxly, we could get a useful signal filter.
Integrating External Applications with MQL5 Community OAuth
Learn how to add “Sign in with MQL5” to your Android app using the OAuth 2.0 authorization code flow. The guide covers app registration, endpoints, redirect URI, Custom Tabs, deep-link handling, and a PHP backend that exchanges the code for an access token over HTTPS. You will authenticate real MQL5 users and access profile data such as rank and reputation.
From Basic to Intermediate: Union (II)
Today we have a very funny and quite interesting article. We will look at Union and will try to solve the problem discussed earlier. We'll also explore some unusual situations that can arise when using union in applications. The materials presented here are intended for didactic purposes only. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Risk Management (Part 4): Completing the Key Class Methods
This is Part 4 of our series on risk management in MQL5, where we continue exploring advanced methods for protecting and optimizing trading strategies. Having laid important foundations in earlier articles, we will now focus on completing all remaining methods postponed in Part 3, including functions for checking whether specific profit or loss levels have been reached. In addition, we will introduce new key events that enable more accurate and flexible risk management.
Neural networks made easy (Part 70): Closed-Form Policy Improvement Operators (CFPI)
In this article, we will get acquainted with an algorithm that uses closed-form policy improvement operators to optimize Agent actions in offline mode.
Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)
In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.
Developing a Replay System (Part 57): Understanding a Test Service
One point to note: although the service code is not included in this article and will only be provided in the next one, I'll explain it since we'll be using that same code as a springboard for what we're actually developing. So, be attentive and patient. Wait for the next article, because every day everything becomes more interesting.
MQL5 Wizard Techniques you should know (Part 23): CNNs
Convolutional Neural Networks are another machine learning algorithm that tend to specialize in decomposing multi-dimensioned data sets into key constituent parts. We look at how this is typically achieved and explore a possible application for traders in another MQL5 wizard signal class.
Developing a Replay System (Part 60): Playing the Service (I)
We have been working on just the indicators for a long time now, but now it's time to get the service working again and see how the chart is built based on the data provided. However, since the whole thing is not that simple, we will have to be attentive to understand what awaits us ahead.
Implementing Practical Modules from Other Languages in MQL5 (Part 06): Python-Like File IO operations in MQL5
This article shows how to simplify complex MQL5 file operations by building a Python-style interface for effortless reading and writing. It explains how to recreate Python’s intuitive file-handling patterns through custom functions and classes. The result is a cleaner, more reliable approach to MQL5 file I/O.
Atmosphere Clouds Model Optimization (ACMO): Theory
The article is devoted to the metaheuristic Atmosphere Clouds Model Optimization (ACMO) algorithm, which simulates the behavior of clouds to solve optimization problems. The algorithm uses the principles of cloud generation, movement and propagation, adapting to the "weather conditions" in the solution space. The article reveals how the algorithm's meteorological simulation finds optimal solutions in a complex possibility space and describes in detail the stages of ACMO operation, including "sky" preparation, cloud birth, cloud movement, and rain concentration.
Self Optimizing Expert Advisors in MQL5 (Part 14): Viewing Data Transformations as Tuning Parameters of Our Feedback Controller
Preprocessing is a powerful yet quickly overlooked tuning parameter. It lives in the shadows of its bigger brothers: optimizers and shiny model architectures. Small percentage improvements here can have disproportionately large, compounding effects on profitability and risk. Too often, this largely unexplored science is boiled down to a simple routine, seen only as a means to an end, when in reality it is where signal can be directly amplified, or just as easily destroyed.
MQL5 Trading Tools (Part 16): Improved Super-Sampling Anti-Aliasing (SSAA) and High-Resolution Rendering
We add supersampling‑driven anti‑aliasing and high‑resolution rendering to the MQL5 canvas dashboard, then downsample to the target size. The article implements rounded rectangle fills and borders, rounded triangle arrows, and a custom scrollbar with theming for the stats and text panels. These tools help you build smoother, more legible UI components in MetaTrader 5.
Reimagining Classic Strategies in MQL5 (Part II): FTSE100 and UK Gilts
In this series of articles, we explore popular trading strategies and try to improve them using AI. In today's article, we revisit the classical trading strategy built on the relationship between the stock market and the bond market.
A feature selection algorithm using energy based learning in pure MQL5
In this article we present the implementation of a feature selection algorithm described in an academic paper titled,"FREL: A stable feature selection algorithm", called Feature weighting as regularized energy based learning.
MQL5 Trading Toolkit (Part 6): Expanding the History Management EX5 Library with the Last Filled Pending Order Functions
Learn how to create an EX5 module of exportable functions that seamlessly query and save data for the most recently filled pending order. In this comprehensive step-by-step guide, we will enhance the History Management EX5 library by developing dedicated and compartmentalized functions to retrieve essential properties of the last filled pending order. These properties include the order type, setup time, execution time, filling type, and other critical details necessary for effective pending orders trade history management and analysis.
All about Automated Trading Championship: Reporting the Championship 2007
The present article contains Weekly Reports of the ATC 2007. These materials are like snapshots, they are interesting-to-read not only during the Championship, but years later as well.
Database Is Easy (Part 1): A Lightweight ORM Framework for MQL5 Using SQLite
This article presents a structured way to manage SQLite data in MQL5 through an ORM layer for MetaTrader 5. It introduces core classes for entity modeling and database access, a fluent CRUD API, reflection hooks for OnGet/OnSet, and macros to define models quickly. Practical code shows creating tables, binding fields, inserting, updating, querying, and deleting records. Developers gain reusable, type-safe components that minimize repetitive SQL.
Table and Header Classes based on a table model in MQL5: Applying the MVC concept
This is the second part of the article devoted to the implementation of the table model in MQL5 using the MVC (Model-View-Controller) architectural paradigm. The article discusses the development of table classes and the table header based on a previously created table model. The developed classes will form the basis for further implementation of View and Controller components, which will be discussed in the following articles.
Adaptive Social Behavior Optimization (ASBO): Two-phase evolution
We continue dwelling on the topic of social behavior of living organisms and its impact on the development of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will dive into the two-phase evolution, test the algorithm and draw conclusions. Just as in nature a group of living organisms join their efforts to survive, ASBO uses principles of collective behavior to solve complex optimization problems.
Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)
In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.
Neural Networks in Trading: Hyperbolic Latent Diffusion Model (HypDiff)
The article considers methods of encoding initial data in hyperbolic latent space through anisotropic diffusion processes. This helps to more accurately preserve the topological characteristics of the current market situation and improves the quality of its analysis.
All about Automated Trading Championship: Reporting the Championship 2006
This article contains Weekly Reports of the ATC 2006. These materials are like snapshots, they are interesting-to-read not only during the Championship, but years later as well.
Developing a Replay System (Part 45): Chart Trade Project (IV)
The main purpose of this article is to introduce and explain the C_ChartFloatingRAD class. We have a Chart Trade indicator that works in a rather interesting way. As you may have noticed, we still have a fairly small number of objects on the chart, and yet we get the expected functionality. The values present in the indicator can be edited. The question is, how is this possible? This article will start to make things clearer.
Introduction to MQL5 (Part 33): Mastering API and WebRequest Function in MQL5 (VII)
This article demonstrates how to integrate the Google Generative AI API with MetaTrader 5 using MQL5. You will learn how to structure API requests, handle server responses, extract AI-generated content, manage rate limits, and save the results to a text file for easy access.
The case for using Hospital-Performance Data with Perceptrons, this Q4, in weighing SPDR XLV's next Performance
XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.
Developing a Replay System (Part 55): Control Module
In this article, we will implement a control indicator so that it can be integrated into the message system we are developing. Although it is not very difficult, there are some details that need to be understood about the initialization of this module. The material presented here is for educational purposes only. In no way should it be considered as an application for any purpose other than learning and mastering the concepts shown.
Atmosphere Clouds Model Optimization (ACMO): Practice
In this article, we will continue diving into the implementation of the ACMO (Atmospheric Cloud Model Optimization) algorithm. In particular, we will discuss two key aspects: the movement of clouds into low-pressure regions and the rain simulation, including the initialization of droplets and their distribution among clouds. We will also look at other methods that play an important role in managing the state of clouds and ensuring their interaction with the environment.
Developing a Replay System (Part 35): Making Adjustments (I)
Before we can move forward, we need to fix a few things. These are not actually the necessary fixes but rather improvements to the way the class is managed and used. The reason is that failures occurred due to some interaction within the system. Despite attempts to find out the cause of such failures in order to eliminate them, all these attempts were unsuccessful. Some of these cases make no sense, for example, when we use pointers or recursion in C/C++, the program crashes.
Overcoming The Limitation of Machine Learning (Part 8): Nonparametric Strategy Selection
This article shows how to configure a black-box model to automatically uncover strong trading strategies using a data-driven approach. By using Mutual Information to prioritize the most learnable signals, we can build smarter and more adaptive models that outperform conventional methods. Readers will also learn to avoid common pitfalls like overreliance on surface-level metrics, and instead develop strategies rooted in meaningful statistical insight.
Population optimization algorithms: Bacterial Foraging Optimization - Genetic Algorithm (BFO-GA)
The article presents a new approach to solving optimization problems by combining ideas from bacterial foraging optimization (BFO) algorithms and techniques used in the genetic algorithm (GA) into a hybrid BFO-GA algorithm. It uses bacterial swarming to globally search for an optimal solution and genetic operators to refine local optima. Unlike the original BFO, bacteria can now mutate and inherit genes.
Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester
This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.
Feature selection and dimensionality reduction using principal components
The article delves into the implementation of a modified Forward Selection Component Analysis algorithm, drawing inspiration from the research presented in “Forward Selection Component Analysis: Algorithms and Applications” by Luca Puggini and Sean McLoone.