This study shows the prediction interval as Bollinger Bands using Student's T-distribution. This means that the bands will be wider when the data features higher variation, as well as when the sample size (in the form of length) is smaller. The bands will also be wider when the confidence level is lower. The opposite is also true. Assuming we set a confidence level of 0.99 and a source set to the close price, we could reasonably expect that 99% of the time the close price would fall between the upper and lower bounds. Because this is a general statistical method which requires a lot of math, the script has a tendency to be relatively slow, but should be eligible to be used in a wide variety of situations.
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Updated int types to floats so floor division doesn't cause bands to fail
Bro I use the same algo in my OrderBlox indicator but upper and lower bands doesn’t appear anymore since yesterday. I made a workaround to fix it in my code but I don’t think that’s normal
above-c-level
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@RickSimpson, took me a bit but the script has been updated to fix that issue