History of MQL5.community development

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.

The article presents an implementation of the Blue Monkey metaheuristic algorithm, which is based on a model of the social behavior of blue monkeys. The article examines the key mechanisms of the algorithm — the group structure of the population, following local leaders, and generational renewal through the replacement of the worst adults with the best offspring — and analyzes the test results.

This article focuses on EA architecture rather than signal design. Starting with a flawed Moving Average crossover EA, we add new‑bar detection to prevent duplicate entries, Magic Number and position awareness, ATR‑based risk levels, and data and trade result validation, along with basic safeguards. You obtain a practical base to build and test advanced systems.