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.
News Trading Made Easy (Part 4): Performance Enhancement
This article will dive into methods to improve the expert's runtime in the strategy tester, the code will be written to divide news event times into hourly categories. These news event times will be accessed within their specified hour. This ensures that the EA can efficiently manage event-driven trades in both high and low-volatility environments.
Developing a Replay System (Part 52): Things Get Complicated (IV)
In this article, we will change the mouse pointer to enable the interaction with the control indicator to ensure reliable and stable operation.
MQL5 Trading Toolkit (Part 5): Expanding the History Management EX5 Library with Position Functions
Discover how to create exportable EX5 functions to efficiently query and save historical position data. In this step-by-step guide, we will expand the History Management EX5 library by developing modules that retrieve key properties of the most recently closed position. These include net profit, trade duration, pip-based stop loss, take profit, profit values, and various other important details.
3D Visualization Without External Libraries: How MetaTrader 5 Reveals Optimization Results via MQL5 + DX11
The article describes the practical application of DirectX 11 and built-in MQL5 tools for creating 3D visualizations and interactive interfaces in MetaTrader 5. The focus is on cognitive efficiency - the ability of 3D charts and guided scenes to help in understanding optimization data, liquidity clusters, and multi-dimensional trading scenarios. The basics of the DX pipeline, working with shaders, binding mouse and keyboard events, and objective technological limitations are discussed in detail. The article is intended for MQL5 developers and algorithmic traders who are ready to transform strategy metrics into understandable 3D analytical landscapes, where the visual layer accelerates decision-making.
MQL5 Wizard Techniques you should know (Part 60): Inference Learning (Wasserstein-VAE) with Moving Average and Stochastic Oscillator Patterns
We wrap our look into the complementary pairing of the MA & Stochastic oscillator by examining what role inference-learning can play in a post supervised-learning & reinforcement-learning situation. There are clearly a multitude of ways one can choose to go about inference learning in this case, our approach, however, is to use variational auto encoders. We explore this in python before exporting our trained model by ONNX for use in a wizard assembled Expert Advisor in MetaTrader.
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.
Category Theory in MQL5 (Part 11): Graphs
This article is a continuation in a series that look at Category Theory implementation in MQL5. In here we examine how Graph-Theory could be integrated with monoids and other data structures when developing a close-out strategy to a trading system.
Neural networks made easy (Part 63): Unsupervised Pretraining for Decision Transformer (PDT)
We continue to discuss the family of Decision Transformer methods. From previous article, we have already noticed that training the transformer underlying the architecture of these methods is a rather complex task and requires a large labeled dataset for training. In this article we will look at an algorithm for using unlabeled trajectories for preliminary model training.
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.
MQL5 Wizard Techniques you should know (Part 35): Support Vector Regression
Support Vector Regression is an idealistic way of finding a function or ‘hyper-plane’ that best describes the relationship between two sets of data. We attempt to exploit this in time series forecasting within custom classes of the MQL5 wizard.
Overcoming Accessibility Problems in MQL5 Trading Tools (Part III): Bidirectional Speech Communication Between a Trader and an Expert Advisor
Build a local, bidirectional voice interface for MetaTrader 5 using MQL5 WebRequest and two Python services. The article implements offline speech recognition with Vosk, wake‑word detection, an HTTP command endpoint, and a text‑to‑speech server on localhost. You will wire an Expert Advisor that fetches commands, executes trades, and returns spoken confirmations for hands‑free operation.
Artificial Showering Algorithm (ASHA)
The article presents the Artificial Showering Algorithm (ASHA), a new metaheuristic method developed for solving general optimization problems. Based on simulation of water flow and accumulation processes, this algorithm constructs the concept of an ideal field, in which each unit of resource (water) is called upon to find an optimal solution. We will find out how ASHA adapts flow and accumulation principles to efficiently allocate resources in a search space, and see its implementation and test results.
Developing a Replay System (Part 30): Expert Advisor project — C_Mouse class (IV)
Today we will learn a technique that can help us a lot in different stages of our professional life as a programmer. Often it is not the platform itself that is limited, but the knowledge of the person who talks about the limitations. This article will tell you that with common sense and creativity you can make the MetaTrader 5 platform much more interesting and versatile without resorting to creating crazy programs or anything like that, and create simple yet safe and reliable code. We will use our creativity to modify existing code without deleting or adding a single line to the source code.
The MQL5 Standard Library Explorer (Part 11): How to Build a Matrix-Based Market Structure Indicator in MQL5
Learn to engineer an MQL5 indicator that converts trend, momentum, and volatility into a single raw score using a matrix.mqh (ALGLIB). The article covers a separate‑window oscillator to validate the core mathematics, then a main‑chart indicator that plots non‑repainting buy/sell arrows when the score crosses user‑defined thresholds. An optional long‑term EMA filter, a minimum‑bar cooldown, and built‑in alerts make the tool practical for live trading.
MQL5 Trading Tools (Part 20): Canvas Graphing with Statistical Correlation and Regression Analysis
In this article, we create a canvas-based graphing tool in MQL5 for statistical correlation and linear regression analysis between two symbols, with draggable and resizable features. We incorporate ALGLIB for regression calculations, dynamic tick labels, data points, and a stats panel displaying slope, intercept, correlation, and R-squared. This interactive visualization aids in pair trading insights, supporting customizable themes, borders, and real-time updates on new bars
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.
MQL5 Trading Tools (Part 17): Exploring Vector-Based Rounded Rectangles and Triangles
In this article, we explore vector-based methods for drawing rounded rectangles and triangles in MQL5 using canvas, with supersampling for anti-aliased rendering. We implement scanline filling, geometric precomputations for arcs and tangents, and border drawing to create smooth, customizable shapes. This approach lays the groundwork for modern UI elements in future trading tools, supporting inputs for sizes, radii, borders, and opacities.
An Introduction to the Study of Fractal Market Structures Using Machine Learning
The article attempts to examine financial time series from the perspective of self-similar fractal structures. Since we have too many analogies that confirm the possibility of considering market quotes as self-similar fractals, this allows us to think about the forecasting horizons of such structures.
Neuro-Structural Trading Engine — NSTE (Part II): Jardine's Gate Six-Gate Quantum Filter
This article introduces Jardine's Gate, a six-gate orthogonal signal filter for MetaTrader 5 that validates LSTM predictions across entropy, expert interference, confidence, regime-adjusted probability, trend direction, and consecutive-loss kill switch dimensions. Out of 43,200 raw signals per month, only 127 pass all six gates. Readers get the complete QuantumEdgeFilter MQL5 class, threshold calibration logic, and gate performance analytics.
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.
Introduction to MQL5 (Part 43): Beginner Guide to File Handling in MQL5 (V)
The article explains how to use MQL5 structures with binary files to persist Expert Advisor parameters. It covers defining structures, accessing members, and distinguishing simple from complex layouts, then writing and reading entire records using FileWriteStruct and FileReadStruct in FILE BIN mode. You will learn safe patterns for fixed-size data and how shared storage (FILE COMMON) enables reuse across sessions and terminals.
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.
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.
Statistical Arbitrage Through Cointegrated Stocks (Part 10): Detecting Structural Breaks
This article presents the Chow test for detecting structural breaks in pair relationships and the application of the Cumulative Sum of Squares - CUSUM - for structural breaks monitoring and early detection. The article uses the Nvidia/Intel partnership announcement and the US Gov foreign trade tariff announcement as examples of slope inversion and intercept shift, respectively. Python scripts for all the tests are provided.
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.
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.
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.
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.
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.
Integrating AI into 3 Smart Money Concepts (SMC): OB, BOS, and FVG
This guide integrates a trained XGBoost model (ONNX) into an SMC EA to evaluate trade setups before execution. The Python pipeline labels historical XAUUSD events and produces a 12-feature representation aligned with the EA. The result is a reproducible method to train, export, and embed the model so the EA can filter OB, FVG, and BOS signals programmatically.
Building a Trade Analytics System (Part 4): Summary Metrics and Dashboard
This article extends the existing Flask backend to compute performance analytics from stored MetaTrader 5 closed trades and deliver them as both JSON and a simple web view. It calculates total trades, total profit, win rate, average profit, and trade duration metrics, returning JSON at /api/v1/analytics/summary and rendering a dashboard at /api/v1. The result provides a quick, consistent way to review trading performance from persisted SQLite records.
MQL5 Wizard Techniques you should know (Part 87): Volatility-Scaled Money Management with Monotonic Queue in MQL5
This article presents a custom MQL5 money management class that adapts position sizing to real-time volatility using a monotonic queue for O(N) sliding-window extremes. The class applies inverse volatility scaling and optionally validates risk with an RBF network. We show implementation details in the Optimize method and compare results with the inbuilt Size-Optimized class to assess latency and risk control benefits.
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.
Formulating Dynamic Multi-Pair EA (Part 9): Market Microstructure Execution Noise Filtering
This article presents a multi-symbol execution filter that scores real-time market quality before any trade is allowed. It measures spread behavior, tick velocity, quote gaps, micro-volatility, and a slippage estimate, then classifies the state to block degraded conditions. Once noise settles, a liquidity sweep continuation model evaluates structure shifts so entries occur only when execution is mechanically stable.
Beyond the Clock (Part 1): Building Activity and Imbalance Bars in Python and MQL5
The article replaces clock-based sampling with López de Prado's alternative bar types and provides two aligned implementations: a unified Python module for batch tick histories and an object‑oriented MQL5 library for live EAs. It covers Parquet/Dask infrastructure, data cleaning, and a single API. Practical issues are solved explicitly: zero‑tick time‑bar filtering, imbalance threshold initialization, EWM state persistence, and parity between Python and MQL5 outputs.
MQL5 Trading Tools (Part 15): Canvas Blur Effects, Shadow Rendering, and Smooth Mouse Wheel Scrolling
In this article, we enhance the MQL5 canvas dashboard with advanced visual effects, including blur gradients for fog overlays, shadow rendering for headers, and antialiased drawing for smoother lines and curves. We add smooth mouse wheel scrolling to the text panel that does not interfere with the chart zoom scale, technically an upgrade.
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.
MQL5 Trading Tools (Part 15): Canvas Blur Effects, Shadow Rendering, and Smooth Mouse Wheel Scrolling
In this article, we enhance the MQL5 canvas dashboard with advanced visual effects, including blur gradients for fog overlays, shadow rendering for headers, and antialiased drawing for smoother lines and curves. We add smooth mouse wheel scrolling to the text panel that does not interfere with the chart zoom scale, technically an upgrade.