Metatrader 5 / Metatrader 4 for MQL5 / MQL4 articles preview

Piecewise linear representation of time series is a method of approximating a time series using linear functions over small intervals. In this article, we will discuss the algorithm of Bidirectional Piecewise Linear Representation of time series (BPLR), which was presented in the paper "Bidirectional piecewise linear representation of time series with application to collective anomaly detection". This method was proposed to solve problems related to finding anomalies in time series.

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Volatility tends to peak around high-impact news events, creating significant breakout opportunities. In this article, we will outline the implementation process of a calendar-based breakout strategy in MQL5. We'll cover everything from creating a class to interpret and store calendar data, developing realistic backtests using this data, and finally, implementing execution code for live trading.

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Using a project-based approach, we will divide the process into two main parts. First, without utilizing the iMA function, we will build a Moving Average indicator entirely from scratch. Next, we'll go one step further and transform the Moving Average from the conventional line shape into a candle-style indication. In addition, this practical method will open up new avenues for developing trading tools that are specifically suited to your requirements.

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In the previous article, "From Basic to Intermediate: Operator Precedence", we talked a little about the precautions we should take when using factorization in our codes. It's not uncommon to come across code that appears correct at first glance but ends up producing completely unexpected results in certain situations. This type of issue is often directly related to how the factorizations are implemented. Getting a piece of code to deliver consistent results may seem like a trivial task. However, without the proper knowledge (as demonstrated in that article), the likelihood of catastrophic failures increases as more and more poorly implemented factorizations are introduced into the code.

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The proposed Object Cluster Module plays a crucial role in enabling the model to achieve a deeper, more holistic understanding of both linguistic and visual information.

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