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  1. Testing and optimization of binary options strategies in MetaTrader 5

    by , 04-09-2023 at 02:50 AM
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    High/Low is the simplest type of binary options as traders only have to determine, in which direction the price will go. In a bullish trend, it is advisable to buy High (Call). While Down (Put) is purchased if a downward movement of the asset is expected. The profit of High/Low options varies from 10% to 80% of the bet.
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  2. Neural networks made easy (Part 34): Fully Parameterized Quantile Function

    by , 04-08-2023 at 02:37 AM
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    We continue studying distributed Q-learning algorithms. Earlier we have already considered two algorithms. In the first one [4], our model learned the probabilities of receiving a reward in a given range of values. In the second algorithm [5], we used a different approach to solving the problem. We trained the model to predict the reward level with a given probability.
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  3. Creating an EA that works automatically (Part 07): Account types (II)

    by , 04-06-2023 at 02:49 AM
    In the previous article Creating an EA that works automatically (Part 06): Account types (I), we started developing a way to ensure that the automated EA works correctly and within its intended purpose. In that article, we created the C_Manager class, which acts as an administrator, so that in case of strange or incorrect EA behavior the EA will be removed from the chart.
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  4. Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps

    by , 04-03-2023 at 04:09 AM
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    Kohonen Maps or Self-Organizing maps(SOM) or Self-Organizing Feature Map(SOFM). Is an unsupervised machine learning technique used to produce a low-dimensional(typically tow-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.
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  5. Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)

    by , 04-01-2023 at 02:47 AM
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    Principal Component Analysis (PCA) is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
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