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mql5

  1. Developing a trading Expert Advisor from scratch (Part 15): Accessing data on the web (I)

    by , 07-17-2022 at 01:30 AM
    There are a lot of websites and places on the web, featuring a huge amount information. What you need to know is where to look and how best to use this information. And the better you are informed during the right period, the better it is for trading. However, if you are going to use a browser, whatever it is: you will find that it is very difficult to filter certain information well, that you are forced to look at many screens and monitors, and in the end, although the information is there, ...
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  2. Developing a trading Expert Advisor from scratch (Part 14): Adding Volume At Price (II)

    by , 07-14-2022 at 01:51 AM
    Our EA already has some resources which assist in trading — we have added them in our previous articles. However, this EA has some issues with visualization and resizing. They do not interfere with trading, but at some points this leads to a mess on the screen until you force it to refresh. In addition, there are still some things missing that would provide valuable information for us. These are specific things, but the information can be necessary.

    So, let's start implementing these
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  3. MQL5 Wizard techniques you should know (Part 02): Kohonen Maps

    by , 07-11-2022 at 09:03 PM
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    Continuing with this series on the MQL5 wizard, we will delve into Kohonen-Maps for this one.
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  4. Indicators with on-chart interactive controls

    by , 07-10-2022 at 01:49 AM
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    The idea behind the interface is to scroll the indicator period with the mouse wheel directly from the chart without going into the settings. We are going to use the button to activate the scrolling mode.
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  5. Neural networks made easy (Part 14): Data clustering

    by , 07-08-2022 at 09:43 PM
    In this series of articles, we have already made a substantial progress in studying various neural network algorithms. But all previously considered algorithms were based on supervised model learning principles. It means that we input some historical data into the model and optimized weights so that the model returned values very close to reference results.
    There is another approach to Artificial Intelligence learning methods — unsupervised learning. This method enables model training only
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