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  1. Neural Networks Made Easy (Part 86): U-Shaped Transformer

    by , 11-19-2024 at 11:55 AM
    Forecasting long-term timeseries is of specifically great importance for trading. The Transformer architecture, which was introduced in 2017, has demonstrated impressive performance in the areas of Natural Language Processing (NLP) and Computer Vision (CV). The use of Self-Attention mechanisms allows the effective capturing of dependencies over long time intervals, extracting key information from the context. Naturally, quite quickly a large number of different algorithms based on this mechanism
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  2. Data Science and ML (Part 31): Using CatBoost AI Models for Trading

    by , 11-09-2024 at 03:41 PM
    CatBoost is an open-source software library with gradient-boosting algorithms on decision trees, it was designed specifically to address the challenges of handling categorical features and data in machine learning.

    It was developed by Yandex and was made open-source in the year of 2017, read more.
    Despite being introduced recently compared to machine learning techniques such as Linear regression or SVM's, CatBoost gained massive popularity among AI communities and rose to
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  3. Creating an MQL5 Expert Advisor Based on the Daily Range Breakout Strategy

    by , 10-26-2024 at 08:14 AM
    In this article, we will explore how to create an Expert Advisor (EA) in MetaQuotes Language 5 (MQL5) based on the Daily Range Breakout strategy. As traders continuously seek effective automated trading solutions, the Daily Range Breakout strategy offers a systematic approach that capitalizes on price movements beyond a defined range, making it an attractive option for Forex traders in MetaTrader 5.
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  4. Developing a multi-currency Expert Advisor (Part 9): Collecting optimization results for single trading strategy instances

    by , 10-24-2024 at 01:24 PM
    We have already implemented a lot of interesting things in the previous articles. We have a trading strategy or several trading strategies that we can implement in the EA. Besides, we have developed a structure for connecting many instances of trading strategies in a single EA, added tools for managing the maximum allowable drawdown, looked at possible ways of automated selection of sets of strategy parameters for their best work in a group, learned how to assemble an EA from groups of strategy
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  5. Data Science and ML(Part 30): The Power Couple for Predicting the Stock Market, Convolutional Neural Networks(CNNs) and Recurrent Neural Networks(RNNs)

    by , 10-24-2024 at 08:11 AM
    In the previous articles, we have seen how powerful both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are and how they can be deployed to help beat the market by providing us with valuable trading signals.

    In this one we are going to attempt combining two of the most powerful techniques CNN and RNN and observe their predictive impact in the stock market. But before that let us briefly understand what CNN and RNN are all about.
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