History of MQL5.community development

In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.

One of the key problems within reinforcement learning is environmental exploration. Previously, we have already seen the research method based on Intrinsic Curiosity. Today I propose to look at another algorithm: Exploration via Disagreement.

This article is the third part of a series describing our development steps of a native MQL5 client for the MQTT protocol. In this part, we describe in detail how we are using Test-Driven Development to implement the Operational Behavior part of the CONNECT/CONNACK packet exchange. At the end of this step, our client MUST be able to behave appropriately when dealing with any of the possible server outcomes from a connection attempt.

This article, the 21st in our series, continues with a look at Natural Transformations and how they can be implemented using linear discriminant analysis. We present applications of this in a signal class format, like in the previous article.

In this article we delve into the application of boostrapping techniques as a means to estimate the future performance of an automated strategy.