History of MQL5.community development

This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.

This is a unique optimization algorithm inspired by the evolution of the turtle shell. The TSEA algorithm emulates the gradual formation of keratinized skin areas, which represent optimal solutions to a problem. The best solutions become "harder" and are located closer to the outer surface, while the less successful solutions remain "softer" and are located inside. The algorithm uses clustering of solutions by quality and distance, allowing to preserve less successful options and providing flexibility and adaptability.

We will add Deep Learning to those three examples that were published in previous articles and compare results with previous. The aim is to learn how to add DL to other EA.

In this article, we will focus on visually styling the graphical user interface (GUI) of our Trading Administrator Panel using MQL5. We’ll explore various techniques and features available in MQL5 that allow for customization and optimization of the interface, ensuring it meets the needs of traders while maintaining an attractive aesthetic.

The EA development plan includes several stages with intermediate results being saved in the database. They can only be retrieved from there again as strings or numbers, not objects. So we need a way to recreate the desired objects in the EA from the strings read from the database.

Step by step guide for auto optimization in MQL5 for Expert Advisors. We will cover robust optimization logic, best practices for parameter selection, and how to reconstruct strategies with back-testing. Additionally, higher-level methods like walk-forward optimization will be discussed to enhance your trading approach.

In this article, we integrate interactive inline buttons into an MQL5 Expert Advisor, allowing real-time control via Telegram. Each button press triggers specific actions and sends responses back to the user. We also modularize functions for handling Telegram messages and callback queries efficiently.

Bollinger Bands are a very common Envelope Indicator used by a lot of traders to manually place and close trades. We examine this indicator by considering as many of the different possible signals it does generate, and see how they could be put to use in a wizard assembled Expert Advisor.

The article considers one of the most famous non-parametric homogeneity tests – the two-sample Kolmogorov-Smirnov test. Both model data and real quotes are analyzed. The article also provides an example of constructing a non-stationarity indicator (iSmirnovDistance).

This article explores a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard et al. The algorithm is implemented in Python to build binary classifier models that can be integrated with MetaTrader 5 applications for inference.