History of MQL5.community development

In this article, we develop a trendline trader program that uses least squares fit to detect support and resistance trendlines, generating dynamic buy and sell signals based on price touches and open positions based on generated signals.

This part focuses on building a flexible, adaptive trading model trained on historical XAUUSD data, preparing it for ONNX export and potential integration into live trading systems.

SAMformer offers a solution to the key drawbacks of Transformer models in long-term time series forecasting, such as training complexity and poor generalization on small datasets. Its shallow architecture and sharpness-aware optimization help avoid suboptimal local minima. In this article, we will continue to implement approaches using MQL5 and evaluate their practical value.

This phase fine-tunes your multi-pair EA to adapt trade size and risk in real time using volatility metrics like ATR boosting consistency, protection, and performance across diverse market conditions.

We created a log suppression system in the Logify library. It details how the CLogifySuppression class reduces console noise by applying configurable rules to avoid repetitive or irrelevant messages. We also cover the external configuration framework, validation mechanisms, and comprehensive testing to ensure robustness and flexibility in log capture during bot or indicator development.

In this article, we develop an enhanced informational dashboard that upgrades the previous part by adding draggable and minimizable features for improved user interaction, while maintaining real-time monitoring of multi-symbol positions and account metrics.

The MetaTrader 5 module offered in Python provides a convenient way of opening trades in the MetaTrader 5 app using Python, but it has a huge problem, it doesn't have the strategy tester capability present in the MetaTrader 5 app, In this article series, we will build a framework for back testing your trading strategies in Python environments.

Even with a positive-expectancy system, position sizing determines whether you thrive or collapse. It’s the pivot of risk management—translating statistical edges into real-world results while safeguarding your capital.

This article presents a sample Expert Advisor implementation for trading a basket of four Nasdaq stocks. The stocks were initially filtered based on Pearson correlation tests. The filtered group was then tested for cointegration with Johansen tests. Finally, the cointegrated spread was tested for stationarity with the ADF and KPSS tests. Here we will see some notes about this process and the results of the backtests after a small optimization.