Articles on the MQL5 programming and use of trading robots

From ChatGPT to Gemini and many model AI tools for text, image, and video generation. Transformers have rocked the AI-world. But, are they applicable in the financial (trading) space? Let's find out.

We wrap up our look at learning rate sensitivity to the performance of Expert Advisors by primarily examining the Adaptive Learning Rates. These learning rates aim to be customized for each parameter in a layer during the training process and so we assess potential benefits vs the expected performance toll.

This article explores the grey model, a promising tool that can expand trader's capabilities. We will look at some options for applying this model to technical analysis and building trading strategies.

In this article, we explored the concept of body in HTTP requests, which is essential for sending data such as JSON and plain text. We discussed and explained how to use it correctly with the appropriate headers. We also introduced the ChttpBody class, part of the Connexus library, which will simplify working with the body of requests.

In this final installment of our Connexus library series, we explored the implementation of the Observer pattern, as well as essential refactorings to file paths and method names. This series covered the entire development of Connexus, designed to simplify HTTP communication in complex applications.

The article presents a V-in-V nested cross-validation pipeline for financial data that breaks leakage at three decision points: hyperparameter search, calibration, and final evaluation. A temporal three‑zone split isolates an inner walk‑forward search with the 1‑SE rule from an outer walk‑forward or CPCV evaluation, while OOF isotonic calibration is fitted independently. The resulting UnifiedValidationCalibrator delivers unbiased out‑of‑sample scores and well‑calibrated probabilities for deployment.

In this article, we expand our butterfly animation program with a four-stage animation pipeline: sequential curve drawing, smooth wing fill fading, detailed body rendering, and continuous flight. We implement a timer-driven state machine, four oscillators for wing flapping, vertical bobbing, horizontal sway, and tilt, as well as a neon glow around the wing outlines and a cyclical color change based on hue. You will learn how to structure these effects on the MetaTrader 5 canvas for clean and controlled playback.

We refactor the Tools Palette from a flat, function-based panel into a modular, class-driven sidebar in MQL5. The design introduces supersampled canvas rendering for anti-aliased shapes, theme control, a category registry, snap alignment, and selective corner rounding. The result is a reusable, scalable sidebar foundation that you can extend with tool selection, dragging, and fly-out menus in future steps.

In this article, you will learn how to create an interactive control panel in MetaTrader 5. We cover the basics of adding input fields, action buttons, and labels to display text. Using a project-based approach, you will see how to set up a panel where users can type messages and eventually display server responses from an API.

This article presents a compact MQL5 utility layer for routine trade operations. It includes position existence checkers, position counters, bulk close helpers, and functions to retrieve the most recent or oldest position by symbol, magic, or type. A simple SMA crossover Expert Advisor demonstrates integration. The result is cleaner EAs, fewer inconsistencies across projects, and faster maintenance.

This article shows how to run Python-trained models natively in MetaTrader 5 via the terminal's ONNX functions. We build an MQL5 class that encapsulates session creation, fixes input/output tensor shapes, applies min-max feature normalization to mirror training, and executes OnnxRun once per bar to protect the CPU, the result is a reliable, maintainable inference path for live charts and the Strategy Tester without sockets or DLLs.

We rebuild the MQL5 Economic Calendar dashboard from a monolithic object-based panel into a modular canvas-based system split across four files. The update adds a dual light and dark theme, collapsible day groups, a resizable layout with pixel-based scrolling, revised value markers, and a live countdown with toast notifications. A candidate event cache and a fast-path timer that repaints only changed cells improve responsiveness and make the codebase easier to extend.

In this article, we replace the embedded CSV snapshot with a SQLite layer that persists calendar events and triggered trade IDs across restarts. The database lives in the common terminal folder and is shared by live charts and the strategy tester, so both modes read the same data without recompiling. An on-demand downloader with a canvas progress bar fetches history from the calendar API and stores it for offline reuse.

For our next Exploration on notions that are testable with the MQL5 Wizard we examine if Skip Lists and the Hopfield Network can give us a profit-guarding trailing strategy. Trailing Stop Management, as already argued, can be overlooked in most trading systems at the expense of Entry Signals or even Money Management. Trailing stops can make all the difference in certain situations such as trending markets, and thus we test this out with GBP USD.

This article provides a structured MQL5 framework for serializing an Expert Advisor's internal state into local binary files. It prevents data resets during platform restarts by safely storing volatile tracking metrics, such as trade counts and multipliers, directly to disk. This architecture offers a more robust state continuity alternative to terminal Global Variables.