MQL5 Programming Articles

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.

The article covers the process of developing a base graphical element for the View component as part of the implementation of tables in the MVC (Model-View-Controller) paradigm in MQL5. This is the first article on the View component and the third one in a series of articles on creating tables for the MetaTrader 5 client terminal.

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.

This article delivers Bayesian Online Change-Point Detection as a single, dependency-free MQL5 class that maintains a per-bar, causal probability of a regime break. We use it three ways: a live monitor, a moving average that flushes on breaks, and a risk overlay with a matched-frequency random control. Readers get a reusable primitive to watch structural change, adapt indicators, and gate exposure after detected shifts.

The article presents the Competitive Learning Algorithm (CLA), a new metaheuristic optimization method based on simulating the educational process. The algorithm organizes the population of solutions into classes with students and teachers, where agents learn through three mechanisms: following the best in the class, using personal experience, and sharing knowledge between classes.

We port Daniel Bloch's Relative Moving Average framework into a complete MetaTrader 5 system. Instead of smoothing price, the RMA measures where price sits inside its own recent distribution on a [0,1] fractile scale, and drives four cross-strategies with a regime-adaptive exit. Includes the engine, indicators, and a backtested Expert Advisor.

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.

Kronos is a pretrained transformer that models OHLCV bars the way a language model predicts words. We reimplement its tokenizer/encoder and transformer block in native MQL5, export weights to flat .bin files, and remove Python from runtime entirely. Part 1 delivers preprocessing and BSQ tokenization plus a bit-for-bit verification harness against PyTorch, so you can run the encoder inside MetaTrader 5 with confidence.

For this article we switch to a custom MQL5 Wizard class implementation that explores Money Management. We are labelling our custom class ‘CMoneySuffixAE’ that we derive by combining the Suffix Automaton algorithm with an Autoencoder neural network. As always, this formulation is testable with MQL5 Wizard Assembled Expert Advisors that can be tuned with various entry signals and trailing stop approaches.

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.

The article explores the possibility of launching a terminal with a configuration file to perform automated routine tasks, programmatically handling such launches, and creating a fully-fledged system for auto-optimizing an EA using Windows OS tools.

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.

Most retail traders ignore overnight swap rates, but for long-term positions, these interest payments can make or break your strategy. This article shows you how to build a dynamic MQL5 module that retrieves real-time swap data and converts it into actual profit or loss in your account currency. You will learn how to program an Expert Advisor that automatically calculates if a trade is worth holding based on carry income and adjusts your position size to account for expected interest. It is a practical guide to turning a hidden cost into a mathematical advantage for your trading systems.

An MQL5 analyzer reconstructs completed trades, records acceptance labels, and measures the accepted-minus-rejected mean net-profit difference. It benchmarks that statistic against individual permutations, equal-block permutations, and circular shifts while preserving the accepted count. Block-size sensitivity, CSV exports, and coordinated base/filtered passes separate statistical selection evidence from operational effects on profit, drawdown, and efficiency metrics.

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.

In this article, we will explore how to declare various graphical representation indicators, such as DRAW_COLOR_LINE and DRAW_FILLING. Additionally, of course, we will learn how to plot graphs using multiple indicators in a simple, practical, and fast way. This can truly change your perspective on MetaTrader 5 and the market as a whole.

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.