History of MQL5.community development

The Triple Exponential Moving Average Oscillator (TRIX) and the Williams Percentage Range Oscillator are another pair of indicators that could be used in conjunction within an MQL5 Expert Advisor. This indicator pair, like those we’ve covered recently, is also complementary given that TRIX defines the trend while Williams Percent Range affirms support and Resistance levels. As always, we use the MQL5 wizard to prototype any potential these two may have.

In this article, we will look at how to use the CustomBookAdd function correctly and effectively. Despite its apparent simplicity, it has many nuances. For example, it allows you to tell the mouse indicator whether a custom symbol is on auction, being traded, or the market is closed. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.

In this article, we will look at what a dynamic array and a static array are. Is there a difference between using one or the other? Or are they always the same? When should you use one and when the other type? And what about constant arrays? We will try to understand what they are designed for and consider the risks of not initializing all the values in the array.

In this article, we enhance the MQL5 Economic Calendar by introducing a draggable dashboard that allows us to reposition the interface for better chart visibility. We implement hover effects for buttons to improve interactivity and ensure seamless navigation with a dynamically positioned scrollbar.

MetaTrader 5 python package provides an easy way to build trading applications for the MetaTrader 5 platform in the Python language, while being a powerful and useful tool, this module isn't as easy as MQL5 programming language when it comes to making an algorithmic trading solution. In this article, we are going to build trade classes similar to the one offered in MQL5 to create a similar syntax and make it easier to make trading robots in Python as in MQL5.

Self-supervised learning can be an effective way to analyze large amounts of unlabeled data. The efficiency is provided by the adaptation of models to the specific features of financial markets, which helps improve the effectiveness of traditional methods. This article introduces an alternative attention mechanism that takes into account the relative dependencies and relationships between inputs.

Geometric patterns offer traders a concise way to interpret price action. Many analysts draw trend lines, rectangles, and other shapes by hand, and then base trading decisions on the formations they see. In this article, we explore an automated alternative: harnessing MQL5 to detect and analyze the most popular geometric patterns. We’ll break down the methodology, discuss implementation details, and highlight how automated pattern recognition can sharpen a trader's market insights.

This article is a transition between what has been discussed so far and a new stage of research. To understand this article, you need to read the previous ones. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.