BrokersMinutes

In this article series, we'll explore how MQL5 indicators are created, customized, and utilized to enhance trading strategies in MetaTrader 5. From basic indicator logic to advanced customization options, we'll cover the basics and gradually move deeper into the more advanced concepts of indicator development as we progress with the article series. The main aim of this article series is to empower you to create your own MQL5 custom indicators tailored to your trading preferences and goals.

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In the previous article, we went through the processes of implementing trades based on the news event's impact. We were successful in this mission, but a key disadvantage to the article's last code was its back-testing speed which is relatively slow. This is mainly due to frequently accessing the database in memory while back-testing the strategy, to resolve this issue we will reduce the number of times the database is accessed during the back-testing procedure. We will get all the information

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We will analyze all of Deriv’s synthetic markets individually, starting with their best known synthetic market, the Boom 1000. The Boom 1000 is notorious for its volatile and unpredictable behavior. The market is characterized by slow, short and equally sized bear candles that are randomly followed by violent, skyscraper sized bull candles. The bull candles are especially challenging to mitigate because the ticks associated with the candle normally aren’t sent to the client terminal, meaning

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Stochastic modeling and control optimization are mathematical methods that help solve problems under uncertainty. They are used in finance, engineering, artificial intelligence, and many other areas.

Stochastic modeling is used to describe systems with random elements, such as stock market price movements or a queue at a restaurant. It is based on random variables, probability distributions, and stochastic processes. Methods such as Monte Carlo and Markov chains can model these

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In the current article, we will talk in more detail about the importance of passing meaningful data, the so-called time series, in a neural network. In particular, we will pass our favorite indicators. To achieve this, I will introduce some new concepts that I use while working with neural networks. Although, I think this is not the limit, and over time I will have a new vision in understanding what and how exactly needs to be passed.

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