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  1. Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5

    by , 06-08-2024 at 10:01 AM
    Now, we'll explore the remaining array functions in Part 6, which will guarantee that you have a thorough understanding of these useful tools. Our objective is still to cover the basic ideas required for automating trading strategies, regardless of your experience level as a developer or level of familiarity with algorithmic trading. Our goal in delving into the nuances of these functions is to promote a comprehensive comprehension so that each reader can competently traverse the ever-changing terrain
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  2. News Trading Made Easy (Part 1): Creating a Database

    by , 06-07-2024 at 05:35 AM
    In this article, we will learn to create a database in which we will store data from the MQL5 Economic Calendar. This data can be used later, in upcoming articles, to trade the news. We will also explore how to execute basic SQL queries to retrieve certain organized information from this database. The entire process will be done in the MQL5 IDE.

    Traders keep a close watch on news sources for information that might impact the markets. This includes geopolitical events, corporate
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  3. Snag Apple Accessories for as Low as $10 at Woot

    by , 06-07-2024 at 04:47 AM


    Apple makes some of the most sought-after tech products out there, but they're also some of the priciest. With as much as you pay for your Apple devices, it only makes sense to want to protect your investment. But cases, wristbands and other accouterments can also add up. If you've been ...
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  4. Neural networks made easy (Part 72): Trajectory prediction in noisy environments

    by , 06-06-2024 at 10:09 AM
    The noise prediction module solves the auxiliary problem of identifying noise in the analyzed trajectories. This helps the movement prediction model better model potential spatial diversity and improves understanding of the underlying representation in movement prediction, thereby improving future predictions.
    The authors of the method conducted additional experiments to empirically demonstrate the critical importance of the spatial consistency and noise prediction modules for SSWNP.
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  5. Indicator of historical positions on the chart as their profit/loss diagram

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