History of MQL5.community development

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.

The article examines two practical aspects of the Adwizard-based optimization pipeline: diagnostics and recovery after failures when generating the final Expert Advisor database, as well as preliminary selection of strategy parameter ranges before project creation. It is shown how analyzing the stages/jobs/tasks tables in SQLite and restarting stages based on their statuses help restore the process, while trial optimization narrows the search space, eliminates redundant parameters, and reduces the risk of getting stuck at local maxima.

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.

In this article, I have shown—in the clearest possible way—how to modify and improve code capable of handling specific tasks while making as few changes as possible to the existing code. We will add a volume display and, at the same time, ensure that users or traders cannot effectively remove objects created by the position indicator.

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.

We consider a BAS+PSO (BSO) hybrid, where BAS provides a local direction signal and PSO facilitates the exchange of best solutions within the swarm. The article presents a mathematical model, pseudocode, an implementation of the class in MQL5, and test results from a standard test bench. This material allows reproducing the algorithm, configuring its parameters, and understanding how three objective-function evaluations per iteration affect efficiency.

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.

In the previous article, we configured the indicator to display the financial result. However, not everyone likes using this display mode. The reasons differ from one trader to another, although in some cases they seem quite reasonable and justified to me. Adapting the code to provide this capability is by no means one of the most difficult tasks. It's actually pretty simple. In this article, we'll look at how to do this.

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.

We are pleased to present the final stage of the TQNet framework’s development and testing, where theory meets real-world trading practice. We will move from historical training to a stress test using recent market data, evaluating the model's robustness and accuracy. The final results are not just dry statistics, but also a clear demonstration of the practical value of the proposed approach.

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.

A step-by-step guide to a native Isolation Forest in MQL5 focused on execution metrics rather than price. It details five features, tree construction and path‑length scoring, rolling‑window training, CSV logging, and FILE_COMMON persistence, all integrated into OnTradeTransaction(). The resulting circuit breaker flags unusual fills in real time and applies controlled responses to stabilize live trading under changing execution conditions.

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.

This article explains Monte Carlo simulation and analysis for trading and guides you through a Python tool that ingests MetaTrader 5 HTML reports. It generates many randomized equity paths, then summarizes them with max drawdown, bust/profit rates, and percentile envelopes around the mean curve. The workflow helps you assess uncertainty, separate normal behavior from outliers, and size positions accordingly.

A hybrid architecture based on Llama 3.2 and SEAL is being tested on eight currency pairs (M15), with forward-period data isolation and information leakage control. The methodology combines adversarial self-play, curriculum learning, and class balancing to ensure stable training. The experiments confirm the gap between forecast accuracy and actual returns, providing readers with practical guidelines for testing strategies and accurately assessing their generalizability.