Spread measures:

- Standard deviation (for most cases);

- Average deviation (if there are extreme values);

- GstDev - Geometric Standard Deviation (exclusively for Geometric Mean);

- HstDev - Harmonic Deviation (exclusively for Harmonic Mean).

These modified functions will calculate everything right, they will take source, length, AND basis of your choice, unlike the ones from TW.

Central tendency measures:

- Mean (if everything's cool & equal);

- Median (values clustering towards low/high part of the rolling window);

- Trimean (3/more distinguishable clusters of data);

- Midhinhe (2 distinguishable clusters of data);

- Geometric Mean ( |low.. ... ... .. .... ... . . . . . . . . . . . .high| this kinda data); <- Exp law

- Harmonic Mean { |low. . . . . . . . . . . . . . .. . . .high| kinda data). <- Reciprocal law

Listen:

1) Don't hesitate using Standard Deviation with non-mean, like "Midhinge Standard Devition", despite what ol' stats gurus gonna say, it works when it's appropriate;

2) Don't check log space while using Geometric Mean & Geometric Standard Deviation, these 2 implement log stuff by design, I mean unless u wanna make it double xd

3) You can use this script, modify it how you want, ask me questions whatever, just make money using it;

4) Use Midrange & Midpoints in tandem when data follows ~addition law (like this . . . . . . . . . . . . . . . . . . . . .). <- just addition law

Look at the data, choose spread measure first, then choose central tendency measure, not vice versa.

!!!

Ain't gonna place ® sign on standard deviations like one B guy did in 1980s lmao, but if your wanna use Harmonic Deviations in science/write about/cite it/whatever, pls give me a lil credit at least, I've never seen it anywhere and unfortunately had to develop it by myself. it's useful when your data develops by reciprocals law (opposite to exponential).

Peace TW

Pine Script manual is the king

...

trimmed L∞ norm statistics: for example, the midhinge (average of first and third quartiles)

*which minimizes the median absolute deviation of the whole distribution*, also minimizes the maximum absolute deviation of the distribution after the top and bottom 25% have been trimmed off.

Source, "Minimization" paragraph

I still haven't figured out yet completely what's the real deal about mid-hidnge vs median, but seems like it has something to do with low kurtosis (big variance) value/bimodality.

Man I wish I know for real, if some1 reads it now and knows exactly when to use mid-hinge, pls hit me up.

Peace

Release Notes:
And the final touch...