Library "Feature_Scaling" FS: This library helps you scale your data to certain ranges or standarize, normalize, unit scale or min-max scale your data in your prefered way. Mostly used for normalization purposes.
minmaxscale(source, min, max, length) minmaxscale: Min-max normalization scales your data to set minimum and maximum range Parameters: source min max length Returns: res: Data scaled to the set minimum and maximum range
meanscale(source, length) meanscale: Mean normalization of your data Parameters: source length Returns: res: Mean normalization result of the source
standarize(source, length, biased) standarize: Standarization of your data Parameters: source length biased Returns: res: Standarized data
unitlength(source, length) unitlength: Scales your data into overall unit length Parameters: source length Returns: res: Your data scaled to the unit length
Release Notes
v2
Updated: Fixed Descriptions minmaxscale(source, min, max, length) minmaxscale Min-max normalization scales your data to set minimum and maximum range Parameters: source: Source data you want to use min: Minimum value you want max: Maximum value you want length: Length of the data you want taken into account Returns: res Data scaled to the set minimum and maximum range
meanscale(source, length) meanscale Mean normalization of your data Parameters: source: Source data you want to use length: Length of the data you want taken into account Returns: res Mean normalization result of the source
standarize(source, length, biased) standarize Standarization of your data Parameters: source: Source data you want to use length: Length of the data you want taken into account biased: Whether to do biased calculation while taking standard deviation, default is true Returns: res Standarized data
unitlength(source, length) unitlength Scales your data into overall unit length Parameters: source: Source data you want to use length: Length of the data you want taken into account Returns: res Your data scaled to the unit length
In true TradingView spirit, the author has published this Pine code as an open-source library so that other Pine programmers from our community can reuse it. Cheers to the author! You may use this library privately or in other open-source publications, but reuse of this code in a publication is governed by House rules.
Disclaimer
The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations supplied or endorsed by TradingView. Read more in the Terms of Use.