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Quantopian Lecture Series: Integration, Cointegration, and Stationarity

I have been complaining about non-stationarity and how it can break statistical analysis a lot at in-person events. I finally got around to building a lecture explaining what stationarity and non-stationarity is, and threw in integration and cointegration because why not?

TL:DR Stationarity can be an invisible problem that kills all your analysis.

All lectures can be found here:

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4 responses

This is great!

Awesome work Delaney!

In [9]:


When running the cell, it shows something like "p-value = 0.000452801665171 The series C is likely stationary."
Does anyone have the same problem?
I reran the code multiple times, sometimes "stationary" sometimes "non-stationary"

Hey James, that cell is there to show that the test will be confused if the function is ambiguous enough. In this case we use a mean reverting function with no trend, so it may be very hard to detect that the parameter follows anything other than a stationary random noise pattern. For example, in this modified notebook I ran that cell 1000 times and counted how many times the function returned stationary or non-stationary. In my case I got 587, but it will be a bit different each time. The point here is that the test can't do much better/worse than a coin-flip. Does this answer your question?

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