MQL4 and MQL5 Programming Articles

In this article, I will present our system for simulating market operations. Although everything is practically finished, there are still a few things to implement and a few changes to make. However, I have to admit that, after everything we've already developed, I'm tired of still being stuck on implementing this system.

Code and build Python-based trading robots just like MQL5 Expert Advisors (EAs). In this article, we develop a Python-based replica of the MetaTrader 5 Python package, providing methods that closely resemble those of MetaTrader 5 during simulation. This allows us to backtest Python EAs in a simplified environment, using an approach similar to developing and testing Expert Advisors in MQL5.

We replace the embedded bitmap icons in our MQL5 canvas interface with a resolution-independent vector icon system. A small set of anti-aliased primitives (strokes, discs, rings, rounded rectangles, and polygons) draws every logo and sidebar glyph procedurally, then plugs into the header, sidebar, and theme toggle. You get smaller builds, theme-aware recoloring, and icons that stay sharp at any size.

In this article, we will take a first step toward showing how to implement operator overloading for the index operator and the assignment operator, while striving to offer a practical and interesting approach for everyone. What we will see here is only part of what I still intend to show, and it is directly related to the overloading of these operators.

In this article, we explore the architecture of SAGDFN — a modern framework capable of transforming the approach to processing spatiotemporal data. It preserves key information even in complex graphs while reducing computational costs.

This article builds an MQL5 dashboard that classifies closed deals into Sydney, Tokyo, London, and New York trading sessions by UTC close time. It computes per-session P&L, win rate, and average hold time, then renders the results as a persistent CCanvas bar chart with an account-wide summary row, alongside an Experts tab summary table. A verification script confirms the classification and metrics logic behaves correctly.

Article revisits Trading Robot that merged Discrete Fourier Transform with Leaky Integrate-and-Fire Spiking Neural Network. Evaluation is made over the seven operating modes with different symbols, timeframes, and test windows. We use two-thirds of the test window to optimize while the one-third does a forward walk run. This study tries to detail parameter interactions, input scaling, gating effects, and outlines practical checks that include rolling windows, frozen inputs, and others. The goal remains identifying settings worth further testing or paper trading.

At first, this article may seem rather confusing because of the material I'm going to cover in it. Nevertheless, I've tried to explain everything as simply and clearly as possible. I hope you'll understand what I'm about to show you here, and that it will come in handy someday.

One of the issues that bothered me the most was that the `C_ElementsTrade` class contains code for accessing positions. Don't take this as a mistake, because it really isn't one. However, this increases the risk of errors in some of the tasks we will need to handle later. All work on implementing the position indicator was carried out with a view to its use in the replay/simulation service. However, when running in this environment, we will have no access to actual positions. Consequently, any call to the MQL5 library intended to retrieve position data will have no effect in this environment.

The article describes a practical implementation of the HimNet framework based on MQL5, ready for integration into automated trading. We demonstrate how heterogeneity-adapted meta-parameters transform the model into a universal tool capable of handling fluctuating volatility.

In this article, we take a detailed look at the algorithms used to implement the key components of the HimNet framework. We demonstrate how, with a minimal number of trainable components, a high degree of consistency and controllability can be achieved throughout the entire system. The presented implementation is compact and transparent, which makes it easier to adapt to real-world market tasks.

A file-based news filter for MQL5 reads a pre-downloaded Forex Factory CSV from MQL5/Files, avoiding fragile web scraping and paid APIs. It provides a modular CNewsFilter with a quote-aware CSV parser, suffix-robust currency extraction, an inclusive time-window checker with clear block reasons, and chart zones for today's events. A demo EA and assertion tests help you integrate and verify offline filtering around scheduled releases.

The article presents a full MQL5 implementation of a multi-symbol trend system: dual EMA crossovers for entries, ADX to avoid ranges, ATR to normalize position size, and a heat monitor to cap total portfolio risk. We explain the architecture, calculation details, and entry/exit logic on daily bars. The result is a practical EA template for systematic, risk-aware portfolio trading.

In this article, we will examine how to implement a tree balancing algorithm. Here, I will present my own version of an implementation of this algorithm. There are many other algorithms that serve the same purpose. Nevertheless, each of them has its own advantages and disadvantages. You, my dear reader, will need to explore them and find the one that best suits your needs.

We invite you to explore the HimNet framework, which combines the flexibility of spatio-temporal adaptation with high computational efficiency, enabling accurate and stable forecasts for financial time series. The article explains in detail how its key components interact with one another, transforming complex algorithms into a manageable architecture.

The series develops machine learning in 100% native MQL5 with no external dependencies. Part 1 delivers logistic regression from first principles: a CLogReg class with standardization, a stable sigmoid, SGD training, and model persistence, plus a script that builds ATR-normalized features, labels the next bar, and tests out-of-sample against a baseline. Readers get a compact include file and a clear template for leakage-free evaluation.

This article implements the Darvas Box method as a complete MQL5 Expert Advisor. We code box detection with a three-session hold, volume contraction during consolidation, and volume-confirmed breakouts, plus a staircase pyramid with a shared, rolling stop at the latest box floor. The EA uses a state machine to run box scanning and trade management in parallel, providing a ready-to-compile system with configurable inputs and clear on-chart diagnostics.

In this article, we will clearly and simply demonstrate and explain how to remove a node from a tree. This process usually confuses beginners rather than helping them understand how it's done and why it needs to be done that way.

This article implements a typed message bus over Windows named pipes to replace MetaTrader's untyped GlobalVariables for inter‑EA communication. A broker EA manages the server and registry, serves multiple slave EAs, and responds with a live, per‑symbol‑attributed portfolio risk measure. It also explains the non-blocking accept pattern that preserves terminal responsiveness, and includes a dashboard and a test script.

We merge GARCH(1,1) variance projections with ATR plus Bollinger-Bands patterns to form an algorithm that could optionally be used with volatility-scaled LSTM within LSTM Wizard-ready signal class. We cover feature scaling, mode scoring, thresholds, and safety checks. Readers can replicate backtest/forward test results to verify if the recurrent layer gives incremental discrimination over our deterministic baseline.

This article presents a native MQL5 implementation of the ZeroMQ Message Transfer Protocol (ZMTP) built on raw MQL5 sockets. It explains the REQ/REP pattern via the CZmqReqSocket class, including framing, handshake, and strict send/receive alternation. A practical pipeline shows an MQL5 script streaming returns to a Python/R server running MS‑GARCH and receiving regime probabilities, enabling integration without DLLs.

We convert the Part 15 decision‑forest classifier into a regime‑adaptive Expert Advisor that decouples statistical inference from trading authority. The EA trains on completed bars, scores each new completed bar, and confirms stable bullish, neutral, or bearish regimes before acting. It then applies spread, ownership, risk, and execution checks to authorize opening, holding, closing, or blocking a position.

The article details a native MQL5 Kafka producer that speaks the wire protocol over raw TCP. It implements RecordBatch v2 encoding, varints, and CRC32C, and adds batching, acks, and retry logic, all without a sidecar or DLL. Use it to publish JSON-structured trading signals from a single terminal to Kafka, where dashboards and other services subscribe independently.