History of MQL5.community development

Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.

Discover how to develop an Expert Advisor (EA) in MQL5 using multiple indicators like RSI, MA, and Stochastic Oscillator to detect hidden bullish and bearish divergences. Learn to implement effective risk management and automate trades with detailed examples and fully commented source code for educational purposes!

Building DLL-free cryptocurrency exchange integrations has long been a challenge, but this solution provides a complete framework for direct market connectivity.

We continue dwelling on the topic of social behavior of living organisms and its impact on the development of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will dive into the two-phase evolution, test the algorithm and draw conclusions. Just as in nature a group of living organisms join their efforts to survive, ASBO uses principles of collective behavior to solve complex optimization problems.

The liquidity grab trading strategy is a key component of Smart Money Concepts (SMC), which seeks to identify and exploit the actions of institutional players in the market. It involves targeting areas of high liquidity, such as support or resistance zones, where large orders can trigger price movements before the market resumes its trend. This article explains the concept of liquidity grab in detail and outlines the development process of the liquidity grab trading strategy Expert Advisor in MQL5.

In this article we will talk about using space-time transformations to effectively predict upcoming price movement. To improve the numerical prediction accuracy in STNN, a continuous attention mechanism is proposed that allows the model to better consider important aspects of the data.

Since these articles are educational in nature and are not intended to show the implementation of specific functionality, we will do things a little differently in this article. Instead of showing how to apply factorization to obtain the inverse of a matrix, we will focus on factorization of the pseudoinverse. The reason is that there is no point in showing how to get the general coefficient if we can do it in a special way. Even better, the reader can gain a deeper understanding of why things happen the way they do. So, let's now figure out why hardware is replacing software over time.

In this article, we will discuss the implementation of MQL5 in partnership with Python to perform broker-related operations. Imagine having a continuously running Expert Advisor (EA) hosted on a VPS, executing trades on your behalf. At some point, the ability of the EA to manage funds becomes paramount. This includes operations such as topping up your trading account and initiating withdrawals. In this discussion, we will shed light on the advantages and practical implementation of these features, ensuring seamless integration of fund management into your trading strategy. Stay tuned!

In this article, we will explore the concept of handlers in the logging library, understand how they work, and create three initial implementations: Console, Database, and File. We will cover everything from the basic structure of handlers to practical testing, preparing the ground for their full functionality in future articles.

We continue the discussion about the use of piecewise linear representation of time series, which was started in the previous article. Today we will see how to combine this method with other approaches to time series analysis to improve the price trend prediction quality.