History of MQL5.community development

This article simplifies triangular arbitrage, showing you how to use predictions and specialized software to trade currencies smarter, even if you're new to the market. Ready to trade with expertise?

In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.

This article discusses the implementation of automatic moves in the tic-tac-toe game in Python, integrated with MQL5 functions and unit tests. The goal is to improve the interactivity of the game and ensure the reliability of the system through testing in MQL5. The presentation covers game logic development, integration, and hands-on testing, and concludes with the creation of a dynamic game environment and a robust integrated system.

In this article, we will explore various methods used in binary genetic and other population algorithms. We will look at the main components of the algorithm, such as selection, crossover and mutation, and their impact on the optimization. In addition, we will study data presentation methods and their impact on optimization results.

Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.

We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.

In this article we present the implementation of a feature selection algorithm described in an academic paper titled,"FREL: A stable feature selection algorithm", called Feature weighting as regularized energy based learning.