MQL5 Programming Articles

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.

In this article, we will see that not everything always needs to be implemented in a certain specific way. There are alternative approaches to problem-solving. To properly understand this article, it is necessary to grasp the concepts described in the previous articles. The materials presented here are for educational purposes only. Do not regard it as a finished application. Its purpose is to study the concepts presented here.

This article is the fifth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we describe the structure of PUBLISH packets, how we are setting their Publish Flags, encoding Topic Name(s) strings, and setting Packet Identifier(s) when required.

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.

Create a CSV trading journal in MQL5 by reading account history over a defined period and writing structured records to file. The article explains deal counting, ticket retrieval, symbol and order type decoding, and capturing entry (lot, time, price, SL/TP) and exit (time, price, profit, result) data with dynamic arrays. The result is an organized, persistent log suitable for analysis and reporting.

We add SQLite persistence to the canvas tools, saving every drawing and the entire UI session per symbol, then restoring them on startup so the workspace resumes exactly where you left it. The article builds versioned object serialization, a load/save lifecycle with dirty writes, and a timeframe-visibility editor that drives render-time filtering. The toolkit also runs as an indicator, so it can sit alongside other indicators or an Expert Advisor.

This article presents a unique experiment that aims to examine the behavior of population optimization algorithms in the context of their ability to efficiently escape local minima when population diversity is low and reach global maxima. Working in this direction will provide further insight into which specific algorithms can successfully continue their search using coordinates set by the user as a starting point, and what factors influence their success.

We build an adaptive Fibonacci volatility band indicator in MQL5 that centers on a smoothed price (SMMA) and scales band width with a smoothed ATR. The article covers inputs, buffer mapping, ATR handling, and SMMA formulas, then projects configurable Fibonacci ratios with filled zones. Readers get a ready workflow for visualizing volatility expansion/contraction and outlining dynamic support and resistance.

In this article, we will explore the concept of handlers in the logging library, understand how they work, and create three initial implementations: Console, Database, and File. We will cover everything from the basic structure of handlers to practical testing, preparing the ground for their full functionality in future articles.

This article implements recursive least squares in native MQL5 with a constant O(1) update per bar, avoiding the per‑bar O(n) rebuild of a rolling OLS. It derives and codes the Sherman–Morrison rank‑1 update, explains the forgetting factor through its effective window, and provides a reusable class. Two coordinated indicators plot a 1‑step‑ahead price forecast on the chart and the signed slope in a correctly scaled subwindow for practical trend tracking.

If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.

This article describes a live data export framework for MetaTrader 5 built around a decoupled, three‑layer design. The MQL5 component batches bar and tick records via a write buffer and rotates CSV files daily; a Python daemon tails the stream, renders a live dashboard, and flags anomaly thresholds. The demo indicator illustrates integration points, enabling real‑time monitoring and auditability during trading sessions.

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.

We implement tick-, volume-, and dollar-runs bars in Python and MQL5 and align them with the existing bar‑building framework. The article details the dual‑accumulator update, offline calibration with per‑side seeds, state persistence for EAs, and parity verification to match Python and MQL5 outputs. Runs bars expose one‑sided bursts that net imbalance can hide, improving coverage during quiet sessions and for mean‑reversion models.

This article shows how to build a synthetic custom symbol in MQL5 by averaging OHLC data from multiple instruments into a single derived price series. It covers symbol collection and validation, custom symbol creation and configuration, timestamp alignment, historical reconstruction, and lightweight live updates. The result is a reusable method for creating synthetic instruments suitable for correlation analysis, index-style modeling, indicator development, and strategy testing.