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Make a fast and accurate python version of the fisher transform for price data



The fisher transform appears easy to do but has proven difficult for me to get working with accurate precision. I have found conflicting information on it and have found some of the details to be confusing.

Specifically, someone mentions using arctan-h from numpy but I have found this to produce bizarre results that are inconsistent with the classic fisher transform indicator. If you can show me that arctan-h works accurately, then I am fine with that. It think the issue is due to the way the bounds are calculated on values at the upper and lower limits of the function.

What I need is a function that will calculate the fisher transform in a live trading environment. It needs to be able to continue calculating the transform for new data. Whatever way most effectively achieves this is what I need, but I would prefer a simple solution where a list of prices can be supplied to the function and the modified list is returned after the transform. Since it appears to be calculated iteratively, I am a little confused on the most efficient way to do this (since I don't want to have to re-calculate every value when the list rolls over with a new price)

I am an intermediate python programmer who happens to be stumped on this silly thing, and maybe you can help?

I will be testing code to make sure it is accurate and will discuss any needed changes (if any).







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