History of MQL5.community development

Continued from the part 1 in the series, now we proceed to implement as a simple DLL then test with MetaTrader 5. This will prepare us well before developing a full-fledge OpenCL as DLL support in the following part to come.

Alan Andrews is one of the most famous "educators" of the modern world in the field of trading. His "pitchfork" is included in almost all modern quote analysis programs. But most traders do not use even a fraction of the opportunities that this tool provides. Besides, Andrews' original training course includes a description not only of the pitchfork (although it remains the main tool), but also of some other useful constructions. The article provides an insight into the marvelous chart analysis methods that Andrews taught in his original course. Beware, there will be a lot of images.

ONNX (Open Neural Network Exchange) is an open format built to represent machine learning models. In this article, we will consider how to create a CNN-LSTM model to forecast financial timeseries. We will also show how to use the created ONNX model in an MQL5 Expert Advisor.

Gary Anderson developed a method of market analysis based on a theory he dubbed the Janus Factor. The theory describes a set of indicators that can be used to reveal trends and assess market risk. In this article we will implement these tools in mql5.

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.

A new article to learn how to detect candlesticks patterns on prices automatically by MQL5.

This article is about moral expectation. We will look at several examples of its use in trading, as well as the results that can be achieved with its help.

In this article I'll introduce a method for creating custom indicators whose drawings are made using the class CCanvas from standard library and see charts properties for coordinates conversion. I'll approach specially indicators which need to fill the area between two lines using transparency.

We continue to study reinforcement learning algorithms. All the algorithms we have considered so far required the creation of a reward policy to enable the agent to evaluate each of its actions at each transition from one system state to another. However, this approach is rather artificial. In practice, there is some time lag between an action and a reward. In this article, we will get acquainted with a model training algorithm which can work with various time delays from the action to the reward.