Articles with examples of trading robots developed in MQL5

In this article, we take our second attempt to convert the changes in price levels on any market, into a corresponding change in angle. This time around, we selected a more mathematically sophisticated approach than we selected in our first attempt, and the results we obtained suggest that our change in approach may have been the right decision. Join us today, as we discuss how we can use Polar coordinates to calculate the angle formed by changes in price levels, in a meaningful way, regardless of which market you are analyzing.

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.

In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.

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.

Today, we are harnessing the capabilities of MQL5 to utilize external resources—such as images in the BMP format—to create a uniquely styled home interface for the Trading Administrator Panel. The strategy demonstrated here is particularly useful when packaging multiple resources, including images, sounds, and more, for streamlined distribution. Join us in this discussion as we explore how these features are implemented to deliver a modern and visually appealing interface for our New_Admin_Panel EA.

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.

The article introduces a restart-safe storage model for news-time stop removal. Suspension state and original SL/TP per position are written to terminal global variables, reconstructed on OnInit, and cleaned after restoration. This lets the EA resume an active suspension window after recompiles or restarts and restore stops only when the news window ends.

By studying the FEDformer method, we opened the door to the frequency domain of time series representation. In this new article, we will continue the topic we started. We will consider a method with which we can not only conduct an analysis, but also predict subsequent states in a particular area.

While analyzing the market situation, we divide it into separate segments, identifying key trends. However, traditional analysis methods often focus on one aspect and thus limit the proper perception. In this article, we will learn about a method that enables the selection of multiple objects to ensure a more comprehensive and multi-layered understanding of the situation.

In the previous article, we got acquainted with the Decision Transformer. But the complex stochastic environment of the foreign exchange market did not allow us to fully implement the potential of the presented method. In this article, I will introduce an algorithm that is aimed at improving the performance of algorithms in stochastic environments.

In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.

We continue to build the Hidformer hierarchical dual-tower transformer model designed for analyzing and forecasting complex multivariate time series. In this article, we will bring the work we started earlier to its logical conclusion — we will test the model on real historical data.

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.

We continue exploring a multi-task learning framework based on ResNeXt, which is characterized by modularity, high computational efficiency, and the ability to identify stable patterns in data. Using a single encoder and specialized "heads" reduces the risk of model overfitting and improves the quality of forecasts.

The article discusses the practical implementation of the HiSSD framework in algorithmic trading tasks. It explains how the skill hierarchy and adaptive architecture can be used to build sustainable trading strategies.

Automated trading systems often focus heavily on signal generation while neglecting the mechanisms required to protect capital during periods of stress. This article presents an Equity Governance Framework in MQL5 that monitors drawdown conditions, evaluates equity pressure, and dynamically controls trading activity through a state-driven risk management model. By combining drawdown analysis, cooldown logic, trade authorization, and execution restrictions, the framework demonstrates how trading discipline can be engineered directly into code using a modular and extensible architecture.

This article presents a comprehensive guide to implementing a sophisticated trading system using Causality Network Analysis (CNA) and Vector Autoregression (VAR) in MQL5. It covers the theoretical background of these methods, provides detailed explanations of key functions in the trading algorithm, and includes example code for implementation.

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.

Let's discuss how we can make our Expert Advisors speech‑capable using text‑to‑speech technology, partnering Python and MQL5. After reading this article, you will walk away with a working example of an EA that speaks dynamic market information. You will master the application of TTS, the WebRequest function, and learn how Python libraries integrate with the MQL5 language to create a truly voice‑aware trading tool.

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.

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.

Security prompts, such as those triggered every time you refresh the chart, add a new pair to the chat with the Admin Panel EA, or restart the terminal, can become tedious. In this discussion, we will explore and implement a feature that tracks the number of login attempts to identify a trusted user. After a set number of failed attempts, the application will transition to an advanced login procedure, which also facilitates passcode recovery for users who may have forgotten it. Additionally, we will cover how cryptography can be effectively integrated into the Admin Panel to enhance security.

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.

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.

This article shows how to build a local stop-loss system in an MQL5 Expert Advisor that keeps stop levels on the terminal side. It walks through the execution logic, event handlers, inputs, and an OOP design using CTrade, CPositionInfo, CHashMap/CHashSet, and chart objects. You will implement multi-position tracking, draggable stops, visual spacers and labels, plus cleanup and disconnection behavior to create a practical risk-control utility.

This article demonstrates how to build the persistence foundation of a self-healing Expert Advisor in MQL5 using SQLite. Readers will learn how to create a permanent trade-state storage layer capable of surviving terminal restarts, shutdowns, and unexpected interruptions. The article covers SQLite integration in MetaTrader 5, database lifecycle management, persistent trade-state structures, and runtime state recovery using practical MQL5 implementations.

An empirical study of Larry Williams’ Trade Day of the Week concept, showing how time-based market bias can be measured, tested, and applied using MQL5. This article presents a practical framework for analyzing win rates and performance across trading days to improve short-term trading systems.

This article introduces the basic concepts behind HMAC-SHA256 and API signatures in MQL5, explaining how messages and secret keys are combined to securely authenticate requests. It lays the foundation for signing API calls without exposing sensitive data.

In this discussion, we follow up on the previously developed multi-signal Expert Advisor with the objective of exploring and applying available optimization methods. The aim is to determine whether the trading performance of the EA can be meaningfully improved through systematic optimization based on historical data.

We invite you to explore the Actor-Director-Critic framework, which combines hierarchical learning and a multi-component architecture for creating adaptive trading strategies. In this article, we take a detailed look at how using the Director to classify the Actor's actions helps to effectively optimize trading decisions and improve the robustness of models in financial market conditions.

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.