History of MQL5.community development

In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.

In our series on integrating MQL5 with data processing packages, we delve in to the powerful combination of machine learning and predictive analysis. We will explore how to seamlessly connect MQL5 with popular machine learning libraries, to enable sophisticated predictive models for financial markets.

The article demonstrates spurious regression occurring when attempting to apply regression analysis to non-stationary processes using Monte Carlo simulation.

Gaussian Process Kernels are the covariance function of the Normal Distribution that could play a role in forecasting. We explore this unique algorithm in a custom signal class of MQL5 to see if it could be put to use as a prime entry and exit signal.

The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.

Regularization is a form of penalizing the loss function in proportion to the discrete weighting applied throughout the various layers of a neural network. We look at the significance, for some of the various regularization forms, this can have in test runs with a wizard assembled Expert Advisor.

In this article, we discuss the concept of dynamic time warping as a means of identifying predictive patterns in financial time series. We will look into how it works as well as present its implementation in pure MQL5.

Python is a well-known and popular programming language with many features, especially in the fields of finance, data science, Artificial Intelligence, and Machine Learning. Python is a powerful tool that can be useful in trading as well. MQL5 allows us to use this powerful language as an integration to get our objectives done effectively. In this article, we will share how we can use Python as an integration in MQL5 after learning some basic information about Python.

In this discussion, we will create our first Expert Advisor in MQL5 based on the indicator we made in the prior article. We will cover all the features required to make the process automatic, including risk management. This will extensively benefit the users to advance from manual execution of trades to automated systems.

In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.

The article considers Boids algorithm based on unique examples of animal flocking behavior. In turn, the Boids algorithm serves as the basis for the creation of the whole class of algorithms united under the name "Swarm Intelligence".