If you have ever tried creating or modifying a complex indicator, you may know some of the problems that arise when increasing the amount of buffers: you'll need to declare tons of double arrays for the buffers, set them as buffers, configure all of them... You can set as buffers arrays that are contained inside of objects In this article, we'll explore the options that this trick will give us and come with solutions to be able to apply this trick in any other situation using ...
Since we started documenting this EA in this series from the very first article Developing a trading Expert Advisor from scratch, it has undergone various changes and improvements while maintaining the same on-chart order system model. It is very simple and functional. However, in many situations, it is not suitable for real trading. Of course, we could add some things to the original system so that we have some information about orders, both open and pending. But this would turn our code into ...
Here is a new article that completes our series about learning how to design a trading system by the most popular technical indicators because we will learn how to do that using a new technical indicator which is the Chaikin Oscillator. We will learn about this technical indicator in detail through the following topics: Chaikin Oscillator definitionChaikin Oscillator strategyChaikin Oscillator strategy blueprint ...
In the previous article Developing a trading Expert Advisor from scratch (Part 16): Accessing data on the web (II), we talked about the problems and consequences of data capturing from the web. We also considered how to use it in an Expert Advisor and discussed three possible solutions each having their pros and cons. However, we have a new problem: the system of terminal's global variables only allows the use of the double type. Many do not know how to avoid this, and therefore they pass various ...
The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the function at the current point, because this is the direction of steepest descent. Conversely, stepping in the direction of the gradient will lead to a local maximum of that function; the procedure is then known as gradient ascent. ---------------- Data Science and Machine Learning (Part 01): Linear RegressionData Science and Machine Learning (Part 02): Logistic ...
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