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  1. Developing a trading Expert Advisor from scratch (Part 18): New order system (I)

    by , 08-03-2022 at 01:10 AM
    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
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  2. Learn how to design a trading system by Chaikin Oscillator

    by , 08-02-2022 at 01:00 AM
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    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:
    1. Chaikin Oscillator definition
    2. Chaikin Oscillator strategy
    3. Chaikin Oscillator strategy blueprint
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  3. Developing a trading Expert Advisor from scratch (Part 17): Accessing data on the web (III)

    by , 08-01-2022 at 01:33 AM
    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
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  4. Data Science and Machine Learning (Part 06): Gradient Descent

    by , 07-31-2022 at 01:53 AM
    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.
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  5. Neural networks made easy (Part 16): Practical use of clustering

    by , 07-30-2022 at 01:48 AM
    The previous two articles were devoted to data clustering. But our main goal is to learn how to use all the considered methods to solve specific practical problems. In particular, trading related cases. When we started considering unsupervised learning methods, we talked about the possibility of using the results obtained both independently and as input data for other models. In this article, we will consider possible use cases for clustering results.[/QUOTE]
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