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From the beginning of this article series, our emphasis has been on aligning our experts with the prevailing sentiment of the daily (D1) candles. The shape of the daily candle has served as the primary guiding feature. However, we needed to scale down to lower timeframes to identify entry levels within the D1 market. For example, at the M1 timeframe, we wanted the market to reach extreme levels on the Relative Strength Index (RSI) to signal potential trades for the Expert Advisor. At this

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This is a unique research, the idea for which came to me while answering questions that arose during the discussion of one of my articles. I am hopeful that readers will appreciate the value and originality of this work.

To carry out the experiment, we need to first initialize the coordinates of the agents forcibly outside the algorithm, using the coordinates of the global minimum, before measuring the fitness function at the first epoch.

Such an experiment will

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This project aims to familiarize you with the useful applications of chart objects in MQL5. This practical approach will teach you how to manage trades, improve visual analysis, and display important trading data right on your charts by efficiently integrating and modifying chart objects within your Expert Advisor (EA). To help you better understand how to use these tools for trading decisions and performance tracking, this project will walk you through the process of creating, modifying,

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In our previous article, we explored creating an expert advisor (EA) using the Trend Constraint V1.09 indicator, complemented by the manually executed Trend Constraint R-R script for placing risk and reward rectangles. While this setup provided insightful trading signals and enhanced visualization, it required manual intervention that could be streamlined. With the fast-paced nature of trading environments, the need for a more efficient solution becomes apparent. Many traders seek integrated

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CNNs are typically complex neural networks whose main applications are in video and image processing, like we saw with GANs in the previous article. However, unlike GANs that are trained in identifying real images and or subjects in the images from fakes, CNNs tend to work more like a classifier in that they split the input data (which is often image pixels) into various subgroups of data whereby each subgroup is meant to capture a key or very important property of the input data. These produced

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