PINE LIBRARY
Updated stats

Library "stats"
stats
factorial(x)
factorial
Parameters:
x (int)
standardize(x, length, lengthSmooth)
standardize
description Moving Standardization of a time series.
Parameters:
x (float)
length (int)
lengthSmooth (int)
dnorm(x, mean, sd)
dnorm
description Approximation for Normal Density Function.
Parameters:
x (float)
mean (float)
sd (float)
pnorm(x, mean, sd, log)
pnorm
description Approximation for Normal Cumulative Distribution Function.
Parameters:
x (float)
mean (float)
sd (float)
log (bool)
ewma(x, length, tau_hl)
ewma
description Exponentially Weighted Moving Average.
Parameters:
x (float)
length (int)
tau_hl (float)
ewm_sd(x, length, tau_hl)
Exponentially Weighted Moving Standard Deviation.
Parameters:
x (float)
length (int)
tau_hl (float)
ewm_scoring(x, length, tau_hl)
ewm_scoring
description Exponentially Weighted Moving Standardization:
Parameters:
x (float)
length (int)
tau_hl (float)
stats
factorial(x)
factorial
Parameters:
x (int)
standardize(x, length, lengthSmooth)
standardize
description Moving Standardization of a time series.
Parameters:
x (float)
length (int)
lengthSmooth (int)
dnorm(x, mean, sd)
dnorm
description Approximation for Normal Density Function.
Parameters:
x (float)
mean (float)
sd (float)
pnorm(x, mean, sd, log)
pnorm
description Approximation for Normal Cumulative Distribution Function.
Parameters:
x (float)
mean (float)
sd (float)
log (bool)
ewma(x, length, tau_hl)
ewma
description Exponentially Weighted Moving Average.
Parameters:
x (float)
length (int)
tau_hl (float)
ewm_sd(x, length, tau_hl)
Exponentially Weighted Moving Standard Deviation.
Parameters:
x (float)
length (int)
tau_hl (float)
ewm_scoring(x, length, tau_hl)
ewm_scoring
description Exponentially Weighted Moving Standardization:
Parameters:
x (float)
length (int)
tau_hl (float)
Release Notes
v2Removed:
ewma(x, length, tau_hl)
ewma
description Exponentially Weighted Moving Average.
ewm_sd(x, length, tau_hl)
Exponentially Weighted Moving Standard Deviation.
ewm_scoring(x, length, tau_hl)
ewm_scoring
description Exponentially Weighted Moving Standardization:
Release Notes
v3Added:
rationalQuadratic(_src, _lookback, _relativeWeight, startAtBar)
Rational Quadratic Kernel - An infinite sum of Gaussian Kernels of different length scales.
description from trader jdehorty KernelFunctions v2
Parameters:
_src (float): <float series> The source series.
_lookback (simple int): <simple int> The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.
_relativeWeight (simple float): <simple float> Relative weighting of time frames. Smaller values resut in a more stretched out curve and larger values will result in a more wiggly curve. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel.
startAtBar (simple int)
Returns: yhat <float series> The estimated values according to the Rational Quadratic Kernel.
discreteFourierTransform(source, length, smoothing)
Discrete Fourier transform
description from trader jdehorty
Parameters:
source (float): time series
length (int)
smoothing (simple int)
Returns: a touple [dft, dfts] i.e. [Discrete Fourier Transform, Smoothed Discrete Fourier Transform]
Pine library
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 publications 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.
Pine library
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 publications 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.