Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview

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 subgroups are often referred to as feature maps.

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We have now expanded signal accessibility on our model, making it beneficial for all users. Additionally, we have motivated numerous upcoming developers on how to seamlessly integrate social networks for signals on our well-known MetaTrader 5 trading platform. Let's conclude by delving into WhatsApp integration details. Our goal is to automatically send signals generated by our custom MetaTrader 5 indicators to a WhatsApp number or group. Meta has introduced a new channel feature on WhatsApp, which can help signals reach a wider audience. As stated by Porter Gale in her book, " Your Network Is Your Net Worth," it is crucial to be part of a community to thrive. Through advancements like these, we can impact a large community, especially on popular platforms like Telegram and WhatsApp.

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Welcome to the first installment of our comprehensive guide on building custom graphical user interface (GUI) panels in MetaQuotes Language 5 (MQL5)! As traders and developers, we understand the importance of efficient and user-friendly interfaces for our trading tools. In this series, we’ll dive into the world of MQL5 and explore how to create powerful GUI panels that enhance your trading experience.

In this initial part, we’ll cover the basics: setting up the project, designing the panel layout, and adding essential controls. In the next part, we will make the panel live, interactive, and responsive.

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We as always use an instance of a custom signal class to test our hypotheses on how to measure the attack angle, and we measure this angle not off of raw price but a moving average. We use the decaying moving average as our indicator for measuring and tracking the significance of the attack angle. Raw prices can also be used to monitor attack angles, however since they are bound to have more volatile values than an indicator buffer, we adopt the former. Any moving average could have been used as well, but we adopted the decaying moving average because it is a bit novel and may not be familiar to most traders.

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In our previous article, we laid the foundation by assembling the graphical elements of our MetaQuotes Language 5 (MQL5) graphical user interface (GUI) panel. If you recall, the iteration was a static assembly of GUI elements - a mere snapshot frozen in time, lacking responsiveness. It was static and unyielding. Now, let’s unfreeze that snapshot and infuse it with life. In this eagerly anticipated continuation, we’re taking our panel to the next level.

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We revisit a form of neural network we had considered in an earlier article by dwelling on one specific hyperparameter. The learning-rate. The Generative Adversarial Network is a neural network that operates in pairs, where one network is trained traditionally to discern the truth, while another is trained to discern the former’s projections from real occurrences. This duality does imply that the traditionally trained network (the former) is trying to fool the latter and this is true, however the two networks are on the ‘same team’ and the simultaneous training of both ultimately makes the generator network more useful to the trader. For this article, we dwell on the training process by focusing on the learning rate.

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Let's get acquainted with a new model family: Ordinary Differential Equations. Instead of specifying a discrete sequence of hidden layers, they parameterize the derivative of the hidden state using a neural network. The results of the model are calculated using a "black box", that is, the Differential Equation Solver. These continuous-depth models use a constant amount of memory and adapt their estimation strategy to each input signal. Such models were first introduced in the paper "Neural Ordinary Differential Equations". In this paper, the authors of the method demonstrate the ability to scale backpropagation using any Ordinary Differential Equation (ODE) solver without access to its internal operations. This enables end-to-end training of ODEs within larger models.

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Gaussian Process Kernels are covariance functions used in Gaussian processes to measure the relationships among data points, such as in a time series. These kernels generate matrices that capture the intra-data relationship, allowing the Gaussian Process to make projections or forecasts by assuming the data follows a normal distribution. As these series look to explore new ideas while also examining how these ideas can be exploited, Gaussian Process (GP) Kernels are serving as our subject in building a custom signal.

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We continue these series that explore various trade setups and ideas thanks to MetaTrade-5’s rapid development and prototyping environment with the MQL5 wizard. These articles, in principle, seek to explore how else traders can set themselves apart from the pack by exploring ideas that may not be so common and could deliver an edge to the interested trader, depending on how he chooses to exploit them. So, we are into exploring here, not necessarily exploiting and the reason why an edge matters a lot is many working trade ideas that are available tend to correlate too positively with each other.

This is super when the trends are bullish and everyone is in the green however, as many would agree, diversification is what would mitigate drawdowns when trends reverse and yet simply finding inversely correlated securities is a lot harder than it seems on paper. That’s why trade entries and exits that are specific to a trader could be a better haven than simply relying on commonly used setups. With that, this article looks at the Restricted Boltzmann Machines (RBMs) when implemented with Backpropagation, as opposed to their traditional implementations of Gibbs Sampling and Contrastive Divergence.

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Support Vector Regression (SVR) is a form of regression derived from Support Vector Machines. At its core, SVR uses kernel methods to map input data into higher-dimensional spaces, allowing for more complex relationships to be captured, which contrasts with dimensionality reduction. For this article though we are exploring strictly its loss function role when used with a multi-layer perceptron. A related but different form of regression we looked at in an earlier article was Gaussian Process Regression. So perhaps it is key we start by drawing distinctions between the two.

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