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)
Nota Keluaran:
v2
Removed:
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:
Removed:
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:
Nota Keluaran:
v3
Added:
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): The source series.
_lookback (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): 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 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 i.e.
Added:
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): The source series.
_lookback (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): 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 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 i.e.