History of MQL5.community development

Can we benefit from seasonality when creating models for Deep Learning with python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.

This article is an introduction to a series of articles about programming. It is assumed here that the reader has never dealt with programming before. So, this series starts from the very basics. Programming knowledge level: Absolute Beginner.

The article considers an interesting algorithm derived from inanimate nature - intelligent water drops (IWD) simulating the process of river bed formation. The ideas of this algorithm made it possible to significantly improve the previous leader of the rating - SDS. As usual, the new leader (modified SDSm) can be found in the attachment.

As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.

The article proposes the method of creating bots using machine learning.

Stop loss and take profit can have a significant impact on trading results. In this article, we will look at several ways to find optimal stop order values.

Density Based Spatial Clustering for Applications with Noise is an unsupervised form of grouping data that hardly requires any input parameters, save for just 2, which when compared to other approaches like k-means, is a boon. We delve into how this could be constructive for testing and eventually trading with Wizard assembled Expert Advisers

The multi-currency expert advisor in this article is an expert advisor or automated trading that uses ZigZag indicator which are filtered with the Awesome Oscillator or filter each other's signals.

This article is the sixth part of a series describing our development steps of a native MQL5 client for the MQTT 5.0 protocol. In this part we comment on the main changes in our first refactoring, how we arrived at a viable blueprint for our packet-building classes, how we are building PUBLISH and PUBACK packets, and the semantics behind the PUBACK Reason Codes.