History of MQL5.community development

Embark on the next phase of our MQL5 journey. In this insightful and beginner-friendly article, we'll look into the remaining array functions, demystifying complex concepts to empower you to craft efficient trading strategies. We’ll be discussing ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap, ArrayReverse, and ArraySort. Elevate your algorithmic trading expertise with these essential array functions. Join us on the path to MQL5 mastery!

In this article we describe the implementation of the Multilayered Iterative Algorithm of the Group Method of Data Handling in MQL5.

Learn how you can get ahead of any market you wish to trade, regardless of your current level of skill.

The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.

In this article, we will consider the algorithm that demonstrates the most controversial results of all those discussed previously - the differential evolution (DE) algorithm.

In this article, we will create a random forest model in Python, train the model, and save it as an ONNX pipeline with data preprocessing. After that we will use the model in the MetaTrader 5 terminal.

In this article, we will get acquainted with an interesting algorithm that is built at the intersection of supervised and reinforcement learning methods.

Spatial Temporal Fusion which is using both ‘space’ and time metrics in modelling data is primarily useful in remote-sensing, and a host of other visual based activities in gaining a better understanding of our surroundings. Thanks to a published paper, we take a novel approach in using it by examining its potential to traders.

The article presents an optimization algorithm based on the patterns of constructing spiral trajectories in nature, such as mollusk shells - the spiral dynamics optimization (SDO) algorithm. I have thoroughly revised and modified the algorithm proposed by the authors. The article will consider the necessity of these changes.

Dive into the heart of neural networks as we demystify the optimization algorithms used inside the neural network. In this article, discover the key techniques that unlock the full potential of neural networks, propelling your models to new heights of accuracy and efficiency.