Weighted Harrell-Davis Quantile Estimator with AbsoluteDeviation — Indicator by xel_arjona — TradingView

Weighted Harrell-Davis Quantile Estimator with Absolute Deviation Fences.


The Following indicator/code IS NOT intended to be a formal investment advice or recommendation by the author, nor should be construed as such. Users will be fully responsible by their use regarding their own trading vehicles/assets.

The following indicator was made for NON LUCRATIVE ACTIVITIES and must remain as is, following TradingView’s regulations. Use of indicator and their code are published for work and knowledge sharing. All access granted over it, their use, copy or re-use should mention authorship(s) and origin(s).


THE INCLUDED FUNCTION MUST BE CONSIDERED FOR TESTING. The models included in the indicator have been taken from open sources on the web and some of them has been modified by the author, problems could occur at diverse data sceneries, compiler version, or any other externality.


Weighted Quantiles or <<Percentile Ranking>> are quite difficult to find on must systems, also it’s non-weighted approach are rarely used to estimate the location parameter of price distribution WICH IS NOT NORMAL, all this in favour of it’s non-robust counterpart, the Arithmetic rolling Mean or <<Moving Average>> and it’s weighted variants like the WMA , VWAP , etc.

Also, a big drawback from this is that must statistics derived from Normal-Distribution parameter location (the Mean) definitely will not fit for an efficient, nor robust estimation for price distributions, so their moments like the standard deviation, kurtosis , skewness, etc. will not be the better tools to build derived algorithms or technical indicators among price/ volume .

In an effort searching better statistical tools for price distributions, I found the excellent work of Andrey Akinshin that took me to port some of their Math research contributions for the compute benchmarking field, and bring it here at the TradingView ecosystem to take a shot at the price distribution crazy fields. For a better detail of what the weighted Harrell-Davis Quantile Estimator can do, who better than drink directly from the source at References:



This work is licensed under a Attribution-NonCommercial-ShareAlike 4.0 International Copyright (c) 2021 ( CC BY-NC-SA 4.0)

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