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  1. 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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  2. 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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  3. 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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  4. Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models

    by , 10-22-2024 at 11:30 AM
    In our previous discussion on Markov Chains, linked here, we demonstrated how to use a transition matrix to understand the probabilistic behavior of the market. Our transition matrix summarized a lot of information for us. It not only guided us on when to buy and sell, it also informed us whether our market had strong trends or was mostly mean reverting. In today's discussion, we shall change our definition of the system state from the moving averages we used in our first discussion to the
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  5. Quantization in machine learning (Part 1): Theory, sample code, analysis of implementation in CatBoost

    by , 10-20-2024 at 04:29 PM
    The article considers the theoretical application of quantization in the construction of tree models No complex mathematical equations are used. While writing the article, I discovered the absence of established unified terminology in the scientific works of different authors, so I will choose the terminology options that, in my opinion, best reflect the meaning. Besides, I will use the terms of my own in the matters left unattended by other researchers. This article will use terms and concepts
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