BrokersMinutes

In this article, we explore the powerful features of the MetaQuotes Language 5 (MQL5) Economic Calendar and how they can be integrated into algorithmic trading. The Economic Calendar, incorporated in the trading terminal, MetaTrader 5, is a crucial tool for traders, providing essential news and data that can significantly impact market movements. By understanding how to retrieve and interpret this information, we can gain an edge in forecasting market reactions to economic events and adjust

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In the previous articles, we discussed the FEDformer method that uses the frequency domain to find patterns in a time series. However, the Transformer used in that method can hardly be referred to as a lightweight model. Instead of complex models that require large computational costs, the paper "FITS: Modeling Time Series with 10k Parameters" proposes a method for the frequency interpolation of time series (Frequency Interpolation Time Series - FITS). It is a compact and efficient solution for

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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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