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  1. Bid/Ask spread analysis in MetaTrader 5

    by , 07-08-2023 at 02:47 AM
    An indicator to report your brokers Bid/Ask spread levels. Now we can use MT5s tick data to analyze what the historic true average Bid/Ask spread actually have recently been. You shouldn't need to look at the current spread because that is available if you show both bid and ask price lines.

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  2. How to connect MetaTrader 5 to PostgreSQL

    by , 07-07-2023 at 02:46 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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  3. Multilayer perceptron and backpropagation algorithm (Part 3): Integration with the Strategy Tester - Overview (I).

    by , 07-06-2023 at 02:46 AM
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    In previous articles, we discussed the construction and use of machine learning models in a simplified way using a client-server connection. However, these models only work in production environments because the tester does not execute network functions.
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  4. Understanding MQL5 Object-Oriented Programming (OOP)

    by , 07-04-2023 at 06:59 AM
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    In this article, we will share one of the most important topics in programming which lead to coding smoothly and easily, and help us to apply the concept of (DRY) which is the shortcut of (Do Not Repeat Yourself) as developers or programmers. In addition to raising the security of any created software, and other of features. We will talk about Object-Oriented Programming (OOP) and how we can use this concept and code it in MQL5 (MetaQuotes Language) by
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  5. An example of how to ensemble ONNX models in MQL5

    by , 07-02-2023 at 11:14 AM
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    For stable trading, it is usually recommended to diversify both the traded instruments and the trading strategies. The same refers to machine learning models: it is easier to create several simpler models that one complex one. But it can be difficult to assemble these models into one ONNX model. In this article, we will consider one of the ensembles called the voting classifier. We will show you how easy it is to implement such an ensemble.
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