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  1. Population optimization algorithms: Saplings Sowing and Growing up (SSG)

    by , 05-15-2023 at 02:00 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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  2. Category Theory in MQL5 (Part 7): Multi, Relative and Indexed Domains

    by , 05-14-2023 at 02:24 AM
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    In the prior article we looked at how changes to cones and compositions can affect sensitivity-analysis results thus presenting opportunity in system design depending on the indicators and securities one could be trading with. For this one we will take a deeper look at the various types of special/unique domains one can come across and also explore how their relationships can be used to moderate expectations in price volatility.
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  3. How to connect MetaTrader 5 to PostgreSQL

    by , 05-12-2023 at 02:05 AM
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    This article analyses five standard alternatives to connecting a Postgres database to MetaTrader 5, their requirements, Pros, and Cons. Also, we will set up a development environment, install a Postgres database as a remote one, connect to it, and insert and retrieve data to be consumed by an MQL5 script or EA.
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  4. Creating an EA that works automatically (Part 11): Automation (III)

    by , 05-11-2023 at 02:41 AM
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    In the previous article "Creating an EA that works automatically (Part 10): Automation (II)", we looked at a way to add EA operation schedule control. While the entire EA system has been built to prioritize autonomy, before moving on to the last phase where we will get a 100% automated EA, we need to make some minor changes to the code.
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  5. Experiments with neural networks (Part 4): Templates

    by , 05-10-2023 at 02:56 AM
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    In the previous articles (Part 1, Part 2, Part 3), we experimented with shapes and angles whose values were passed to the perceptron and the neural network built on the basis of the DeepNeuralNetwork.mqh library. We also conducted experiments on optimization methods in the strategy tester.
    An important task in the current experiments was to track the influence of the amount of transmitted data and the depth of history we take this data from.
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