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  1. Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer

    by , 11-17-2024 at 10:17 AM
    Security is paramount in any field of expertise, and we cannot afford to overlook its importance. With the persistent threat of unauthorized access, it’s crucial to safeguard our Admin Panel from potential intruders. If unauthorized individuals gain access, they could easily manipulate the panel and jeopardize our communication efforts with the broadcast community. The primary purpose of this system is to facilitate reliable communication, and while we can enhance functionality at the Expert
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  2. From Python to MQL5: A Journey into Quantum-Inspired Trading Systems

    by , 11-15-2024 at 05:04 PM
    This article explores the application of quantum-inspired concepts in trading systems, bridging theoretical quantum computing with practical implementation in MQL5. We’ll introduce essential quantum principles and guide you from Python prototyping to MQL5 integration, with real-world performance data.
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  3. Neural Network in Practice: Least Squares

    by , 11-14-2024 at 03:45 PM
    In the previous article Neural Network in Practice: Secant Line, we began to discuss applied mathematics in practice. However, this was only a short and quick introduction to the topic. We have seen that the basic mathematical operation to be used is the trigonometric function. And, contrary to what many think, this is not a tangent function but a secant function. Although this may all seem quite confusing at first, you will soon find that everything is much simpler than it seems. Unlike
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  4. Portfolio Optimization in Python and MQL5

    by , 11-13-2024 at 09:49 AM
    Introducing two innovative portfolio optimization programs designed to revolutionize trading strategies and maximize returns while minimizing risk The first a Python-based solution leverages the power of MetaTrader 5 integration alongside advanced libraries such as pandas Numpy and cvxpy to analyze historical data optimize asset allocation and visualize results with Matplotlib. The second a similar implementation crafted in MQL5 harnesses the native capabilities of the MetaTrader 5 platform offering
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  5. Applying Localized Feature Selection in Python and MQL5

    by , 11-13-2024 at 06:52 AM
    In this article, we explore a feature selection algorithm introduced in the paper 'Local Feature Selection for Data Classification' by Narges Armanfard, James P. Reilly, and Majid Komeili. This method aims to identify predictive features that are often overlooked by traditional selection techniques due to their limited global utility. We will begin with a general overview of the algorithm, followed by its implementation in Python to create classifier models suitable for export to MetaTrader
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