History of MQL5.community development

We continue our dive into chaos theory in financial markets. This time I will consider its applicability to the analysis of currencies and other assets.

In this article we are creating a synthetic symbol using a Generative Adversarial Network (GAN) involves generating realistic Financial data that mimics the behavior of actual market instruments, such as EURUSD. The GAN model learns patterns and volatility from historical market data and creates synthetic price data with similar characteristics.

The Market Facilitation Index is another Bill Williams Indicator that is intended to measure the efficiency of price movement in tandem with volume. As always, we look at the various patterns of this indicator within the confines of a wizard assembly signal class, and present a variety of test reports and analyses for the various patterns.

When working with machine learning models, it’s essential to ensure consistency in the data used for training, validation, and testing. In this article, we will create our own version of the Pandas library in MQL5 to ensure a unified approach for handling machine learning data, for ensuring the same data is applied inside and outside MQL5, where most of the training occurs.

Master the power of VWAP with our comprehensive guide! Learn how to integrate VWAP analysis into your trading strategy using MQL5 and Python. Maximize your market insights and improve your trading decisions today.

The EA under development is expected to show good results when trading with different brokers. But for now we have been using quotes from a MetaQuotes demo account to perform tests. Let's see if our EA is ready to work on a trading account with different quotes compared to those used during testing and optimization.

In this article, we develop a Multi-Level Zone Recovery System in MQL5 that utilizes RSI to generate trading signals. Each signal instance is dynamically added to an array structure, allowing the system to manage multiple signals simultaneously within the Zone Recovery logic. Through this approach, we demonstrate how to handle complex trade management scenarios effectively while maintaining a scalable and robust code design.

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.

In this article, I will teach you basic file operations and how to configure a flexible handler for customization. We will update the CLogifyHandlerFile class to write logs directly to the file. We will conduct a performance test by simulating a strategy on EURUSD for a week, generating logs at each tick, with a total time of 5 minutes and 11 seconds. The result will be compared in a future article, where we will implement a caching system to improve performance.