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In the first article of the series, we loaded the dataset, placed labels, enriched the dataset and also performed dataset labeling. The second article was devoted to the creation and training of the model, as well as implementation of cross-validation and bagging.

Now that our model is trained and tested, it is time to start real trading using the MetaTrader 5 library for Python. This powerful library allows us to automate trading directly through Python using the functions

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In the previous article (Part 2 of the series), we demonstrated how to transform the Kumo Breakout Strategy into a fully functional Expert Advisor (EA) using MetaQuotes Language 5 (MQL5). In this article (Part 3), we focus on the Zone Recovery RSI System, an advanced strategy designed to manage trades and recover from losses dynamically. This system combines the Relative Strength Index (RSI) to trigger entry signals with a Zone Recovery mechanism that places counter-trades when the market moves

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A particularly interesting method entitled CCMR was presented in the paper "CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning". It is an approach to optical flow estimation that combines the advantages of attention-oriented methods of motion aggregation concepts and high-resolution multi-scale approaches. The CCMR method consistently integrates context-based motion grouping concepts into a high-resolution coarse-grained estimation framework. This

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The recently published paper "Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation" introduces the algorithm for the graph transformer generative adversarial model (GTGAN), which succinctly combines both of these approaches. The authors of the GTGAN algorithm address the problem of creating a realistic architectural design of a house from an input graph. The generator model they presented consists of three components: a message passing convolutional neural network

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The article presents the results of experiments on eight real data sets, according to which ATFNet shows promising results and outperforms other state-of-the-art time series forecasting methods on many datasets.

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