History of MQL5.community development

A metaheuristic algorithm that simulates the growth of coronary arteries in the human heart for optimization problems. It uses the principles of angiogenesis (the growth of new blood vessels), bifurcation (branching), and pruning of weak branches to find optimal solutions in a multidimensional space. Testing its effectiveness across a wide range of tasks yielded unexpected results.

We implement a Hierarchical Risk Parity allocator in MQL5 as a single class, validate each stage against an independent Python reference, and package it in a rebalancing Expert Advisor. The pipeline covers returns, covariance/correlation, clustering, quasi-diagonalization, and recursive bisection, and contrasts HRP with Markowitz on stressed data. You finish with a verified allocator and an EA ready for basket-level testing.

The article delivers a complete, verifiable tick export path from MQL5 to a binary file and into Python. It defines a 64‑byte header, 48‑byte records with millisecond time and flags, an export pipeline using CopyTicksRange(), and a single‑call NumPy loader. Users obtain compact, precision‑preserving files and a reproducible workflow for vectorized analysis.

This part extends the series with a modular, event-driven MQL5 pipeline: swing detection feeds an object placer for trendlines, SR, Fibonacci, channels, and pitchforks; evaluators monitor interactions and generate signals; adaptive logic executes trades with valid stops per instrument. The topology manager synchronizes placement, scanning, and processing. The code is structured into reusable components for easy reuse and scaling.

Mantis is a versatile tool for in-depth time series analysis that can be flexibly scaled to accommodate any financial scenario. Learn how a combination of patching, local convolutions, and cross-attention enables a highly accurate interpretation of market patterns.

What if lunar cycles and seasonal patterns influence the foreign exchange markets? This article shows how to translate astrological concepts into the language of mathematics and machine learning. I built a Python system with 88 features based on astronomical cycles, trained CatBoost on 15 years of EURUSD data, and obtained some intriguing results. The code is open-source, the methods are verifiable, and the conclusions are unexpected — ancient wisdom meets gradient boosting.

We present a native MQL5 implementation of the catch22 feature set: all 22 canonical time-series characteristics in a reusable class validated against pycatch22. Using a leak-free pipeline (chronological split, purging, embargo), we run a three-arm ablation—classic indicators, catch22, and combined—for volatility-regime classification. Finally, we deploy the combined model as a Strategy Tester regime filter to quantify its impact on a simple baseline strategy.

The article shows how to build an MQL5 EA that writes every deal to an SQLite database the moment it appears, using the built-in Database API as the SQLite bridge. It implements an event data model, a prepared INSERT workflow reused across calls, session-safe recovery after restarts, and deal detection via OnTrade(). You can open the resulting file with any SQLite client to run queries for analysis and reporting.

Meet Mantis — a lightweight foundation model for time series classification based on a Transformer architecture, featuring contrastive pre-training and hybrid attention that deliver record-breaking accuracy and scalability.

Close-to-close volatility ignores the high, the low, and overnight gaps. We build a reusable MQL5 library implementing four range-based estimators from Parkinson to the gap-robust Yang-Zhang, and put it to work in a comparison indicator and a set of adaptive volatility bands.

We continue our acquaintance with the Mamba4Cast framework. Today, we will delve into the practical implementation of the proposed approaches. Mamba4Cast was designed not for lengthy warm-up on every new time series, but for immediate deployment. Thanks to the concept of Zero-Shot Forecasting, the model can produce high-quality forecasts on real-world data without additional training or hyperparameter tuning.

Let's move on to using multiple terminal instances on the server by setting up a simple control panel for starting and stopping them. Now it is time to expand the functionality and move on to the next stages — implementing more complex features, such as managing multiple terminal instances, state persistence, integration with the MetaTrader 5 API, and a web interface with comprehensive information about the terminals.

How can we conveniently monitor multiple terminals running Expert Advisors, especially when they are on different computers? Let's try to create a web interface for managing the launch of MetaTrader 5 trading terminals and viewing detailed information about the operation of each instance.