Discussing the article: "Implementation of the Augmented Dickey Fuller test in MQL5"

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In this article we demonstrate the implementation of the Augmented Dickey-Fuller test, and apply it to conduct cointegration tests using the Engle-Granger method.

Simply put an ADF test is a hypothesis test, that allows us to determine if a specific characteristic of the observed data is statistically significant. In this instance the characteristic being acertained is the stationarity of a series.  A statistical hypothesis is an assumption made about a data set that is represented by a sample. We can only know the real truth by working with the entire data set. Which is usually not possible for one reason or another. So a sample of a data set is tested to posit an assumption of the entire data set. The important point to remember here is that the truth of a statistical hypothesis is never known with certainty when working with samples. What we get is whether an assumption is likely true or false.

In an ADF test we consider two scenarios:

• The Null hypothesis that a unit root is present in the time series.

• The Alternative hypothesis that the times series does not exhibit a unit root.

Author: Francis Dube

[Bryan Djoufack Nguessong]

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Check out the new article: Implementation of the Augmented Dickey Fuller test in MQL5.

Author: Francis Dube

Hey, thanks a lot for this article. I used the code of this article but I would like to know if you ever updated this code for speed. i did a test but when the size gets above thousand it really takes time. I don’t know if it’s something that can be optimized.