Articles on MetaTrader 5 integration using MQL5

In this article, we will continue to connect the new strategy to the created auto optimization system. Let's look at what changes need to be made to the optimization project creation EA, as well as the second and third stage EAs.

This is a continuation of the previous article considering the idea of social groups. The article explores the evolution of social groups using movement and memory algorithms. The results will help to understand the evolution of social systems and apply them in optimization and search for solutions.

This article discusses the transition from procedural coding to object-oriented programming (OOP) in MQL5 with an emphasis on integration with the REST API. Today we will discuss how to organize HTTP request functions (GET and POST) into classes. We will take a closer look at code refactoring and show how to replace isolated functions with class methods. The article contains practical examples and tests.

Soft Actor Critic is a Reinforcement Learning algorithm that we looked at in a previous article, where we also introduced python and ONNX to these series as efficient approaches to training networks. We revisit the algorithm with the aim of exploiting tensors, computational graphs that are often exploited in Python.

In this discussion, we introduce a Higher-to-Lower Timeframe Synchronizer tool designed to solve the problem of analyzing market patterns that span across higher timeframe periods. The built-in period markers in MetaTrader 5 are often limited, rigid, and not easily customizable for non-standard timeframes. Our solution leverages the MQL5 language to develop an indicator that provides a dynamic and visual way to align higher timeframe structures within lower timeframe charts. This tool can be highly valuable for detailed market analysis. To learn more about its features and implementation, I invite you to join the discussion.

This piece follows up ‘Part-84’, where we introduced the pairing of Stochastic and the Fractal Adaptive Moving Average. We now shift focus to Inference Learning, where we look to see if laggard patterns in the last article could have their fortunes turned around. The Stochastic and FrAMA are a momentum-trend complimentary pairing. For our inference learning, we are revisiting the Beta algorithm of a Variational Auto Encoder. We also, as always, do the implementation of a custom signal class designed for integration with the MQL5 Wizard.

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.

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.

This article teaches you how to retrieve and extract price data from external platforms using APIs and the WebRequest function in MQL5. You’ll learn how URLs are structured, how API responses are formatted, how to convert server data into readable strings, and how to identify and extract specific values from JSON responses.

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.

This article offers a step-by-step guide to mastering CRUD (Create, Read, Update, Delete) operations in Firebase, focusing on its Realtime Database and Firestore. Discover how to use Firebase SDK methods to efficiently manage data in web and mobile apps, from adding new records to querying, modifying, and deleting entries. Explore practical code examples and best practices for structuring and handling data in real-time, empowering developers to build dynamic, scalable applications with Firebase’s flexible NoSQL architecture.

In this part, we focus on how to merge real-time market feedback—such as live trade outcomes, volatility changes, and liquidity shifts—with adaptive model learning to maintain a responsive and self-improving trading system.

This piece follows up ‘Part-80’, where we examined the pairing of Ichimoku and the ADX under a Reinforcement Learning framework. We now shift focus to Inference Learning. Ichimoku and ADX are complimentary as already covered, however we are going to revisit the conclusions of the last article related to pipeline use. For our inference learning, we are using the Beta algorithm of a Variational Auto Encoder. We also stick with the implementation of a custom signal class designed for integration with the MQL5 Wizard.

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.

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.

The article presents the Central Force Optimization (CFO) algorithm inspired by the laws of gravity. It explores how principles of physical attraction can solve optimization problems where "heavier" solutions attract less successful counterparts.

This is a specialized trend-following EA that makes both short and long-term analyses, trading decisions, and executions based on the overall trend and its strength. This article will explore in detail an EA that is specifically designed for traders who are patient, disciplined, and focused enough to only execute trades and hold their positions only when trading with strength and in the trend direction without changing their bias frequently, especially against the trend, until take-profit targets are hit.

In this discussion, we’ll explore additional advancements as we integrate refined event‑alerting logic for the economic calendar events displayed by the News Headline EA. This enhancement is critical—it ensures users receive timely notifications a short time before key upcoming events. Join this discussion to discover more.

We continue developing the Connexus library. In this chapter, we explore the concept of headers in the HTTP protocol, explaining what they are, what they are for, and how to use them in requests. We cover the main headers used in communications with APIs, and show practical examples of how to configure them in the library.

This article is the sixth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we comment on the main changes in our first refactoring, how we arrived at a viable blueprint for our packet-building classes, how we are building PUBLISH and PUBACK packets, and the semantics behind the PUBACK Reason Codes.

Learn how to complete the creation of the final module in the History Manager EX5 library, focusing on the functions responsible for handling the most recently canceled pending order. This will provide you with the tools to efficiently retrieve and store key details related to canceled pending orders with MQL5.

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.

In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.

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.

Discover how to detect user actions in MetaTrader 5, send requests to an AI API, extract responses, and implement scrolling text in your panel.