History of MQL5.community development

With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.

This article outlines the steps to implement the Deus EA based on the RSI and Moving Average indicators for guiding automated trading.

In this article, we create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.

In this article, we dive deep into the crucial aspects of choosing the most relevant and high-quality Forex data to enhance the performance of AI models.

In this article, we will continue to look at price patterns in the DoEasy library. We will also create the Inside Bar pattern class of the Price Action formations.

A step-by-step guide to implementing an automated trading algorithm in MQL5 based on the Bollinger Bands trading strategy. A detailed tutorial based on creating an Expert Advisor that can be useful for traders.

Clustering algorithms in machine learning are important unsupervised learning algorithms that can divide the original data into groups with similar observations. By using these groups, you can analyze the market for a specific cluster, search for the most stable clusters using new data, and make causal inferences. The article proposes an original method for time series clustering in Python.

In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.

Batch normalization is the pre-processing of data before it is fed into a machine learning algorithm, like a neural network. This is always done while being mindful of the type of Activation to be used by the algorithm. We therefore explore the different approaches that one can take in reaping the benefits of this, with the help of a wizard assembled Expert Advisor.

In this article, we will explore the development of a fully customizable Price Data export script using MQL5, marking new advancements in the simulation of the Price Man CGI Model. We have implemented advanced refinement techniques to ensure that the data is user-friendly and optimized for animation purposes. Additionally, we will uncover the capabilities of Blender 3D in effectively working with and visualizing price data, demonstrating its potential for creating dynamic and engaging animations.

Most people who want or dream of learning to program don't actually have a clue what they're doing. Their activity consists of trying to create things in a certain way. However, programming is not about tailoring suitable solutions. Doing it this way can create more problems than solutions. Here we will be doing something more advanced and therefore different.

Integration enables seamless workflow where raw financial data from MQL5 can be imported into data processing packages like Jupyter Lab for advanced analysis including statistical testing.

Machine learning models come with various adjustable parameters. In this series of articles, we will explore how to customize your AI models to fit your specific market using the SciPy library.

In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.

It is a common practice for many Artificial Intelligence models to predict a single future value. However, in this article, we will delve into the powerful technique of using machine learning models to predict multiple future values. This approach, known as multistep forecasting, allows us to predict not only tomorrow's closing price but also the day after tomorrow's and beyond. By mastering multistep forecasting, traders and data scientists can gain deeper insights and make more informed decisions, significantly enhancing their predictive capabilities and strategic planning.