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  1. Gold Dust EA

    by , 07-22-2023 at 01:35 AM
    Gold Dust - expert for MetaTrader 5

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    The trading system is based on the future price direction forecast, which is implemented by selecting weight coefficients of an elementary single-layer neural network.
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  2. Forecasting with ARIMA models in MQL5

    by , 07-21-2023 at 01:34 AM
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    The article Implementing an ARIMA training algorithm in MQL5, describes the CArima class for building ARIMA models. Although it is technically possible to use the class as it is to apply a model and make predictions, it is not intuitive. In this article we will address this shortcomming and extend the class to enable easier to use methods for applying models to make predictions. We will discuss some of the complications related to implementing predictions
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  3. Develop a Proof-of-Concept DLL with C++ multi-threading support for MetaTrader 5 on Linux

    by , 07-20-2023 at 01:02 AM
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    Linux has a vibrant development ecosystem and a good ergonomic for software development.
    Normally MetaTrader 5 developers just use MQL5 programming language to develop their indicators/experts or related products then publish on the market to end-users without any concern about which OS to base on.
    But when developers need to involve developing a custom solution as a shared library (DLL) to further expand and provide additional services
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  4. Population optimization algorithms: Gravitational Search Algorithm (GSA)

    by , 07-19-2023 at 01:02 AM
    Gravitational Search Algorithm (GSA) was proposed by E. Rashedi to solve the optimization problem, especially non-linear problems, following the principles of Newton's law of gravitation. In the proposed algorithm, particles are considered as objects and their performance is estimated taking into account their masses.
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  5. Population optimization algorithms: Saplings Sowing and Growing up (SSG)

    by , 07-18-2023 at 01:02 AM
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    There are many optimization methods inspired by processes occurring in nature, such as evolutionary computation, artificial immunology, population methods and others. SSG is basically defined as iterative generation and combination processes working with a garden of potential solutions called seedlings. The Saplings Sowing and Growing (SSG) algorithm was proposed by A. Karci with co-authors in 2002. The algorithm is inspired by the evolution of
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