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  1. Category Theory in MQL5 (Part 4): Spans, Experiments, and Compositions

    by , 04-12-2023 at 12:59 PM
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    In the previous article, we saw how category theory can be potent in complex systems via its concepts of products, coproducts, and the universal property, examples of applications of which in finance and algorithmic trading were shared. Here, we will delve deeper into spans, experiments, and compositions.
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  2. iOS 16.4 on Your iPhone: Every New Feature to Try Now

    by , 04-11-2023 at 01:43 AM
    Below is some of what your iPhone gains with iOS 16.4. And here's what you need to know before installing the update to help avoid running into issues with the download.

    31 new emoji

    The iOS 16.4 update brings 31 new emoji to your iOS device. The new emoji include a new smiley; new animals, like a moose and a goose; and new heart colors, including pink and light blue.

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  3. Creating an EA that works automatically (Part 08): OnTradeTransaction

    by , 04-10-2023 at 01:31 AM
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    In previous articles: Creating an EA that works automatically (Part 06): Account types (I) and Creating an EA that works automatically (Part 07): Account types (II), we were focusing on the importance of being careful when designing an EA that trades automatically.

    Before we can really understand how the EA code for automation should work, we need to understand how it interacts with the trade server.
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  4. Testing and optimization of binary options strategies in MetaTrader 5

    by , 04-09-2023 at 01:50 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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  5. Neural networks made easy (Part 34): Fully Parameterized Quantile Function

    by , 04-08-2023 at 01:37 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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