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I have a model specifying a cointegration relationship on a number of transaction-level timeseries.

I would like to specify entry and exit points for trades where these points ideally would be just before the turning points of the time series (bottoms for exit and tops for entry). The issue I am having is that the timeseries itself are very short -- usually around 1600 observations/seconds. Thus the usual +- 1/2 standard deviation entry points do not work because I do not know the standard deviation reliably before the window of opportunity is over.

I see a number of solutions but perhaps there's better ones in the literature?

  • Rolling standard deviation: very unreliable at the beginning, and losing out on trading opportunities before having reliable results
  • Rolling quantile: same problem like above
  • Fixed level entry/exit points: could be very wrong since the series vary in magnitude
  • High watermark entry/exit points

Here are some example series. You might notice the distortions at the beginning of the series, this is because I'm removing a quadratic trend.

first example second example

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This sounds like a case where you will need to apply some good old-fashioned judgment to determine what the standard deviation "should be" before you have enough data to measure it. Surely this process repeats with some frequency, and perhaps given some attributes and more details you could make an educated guess as to the standard deviation (or quantiles, or whatever you want to use for your entry/exit points) before you have enough data for the specific series at hand.

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Following on from Tal Fishman's idea of using "some good old-fashioned judgment," you might find the idea of applying a Tukey chart and its related upper and lower control limits more useful than standard deviation.

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