Library "statistics"
General statistics library.
erf(x) The "error function" encountered in integrating the normal
distribution (which is a normalized form of the Gaussian function).
Parameters:
x : The input series.
Returns: The Error Function evaluated for each element of x.
erfc(x)
Parameters:
x : The input series
Returns: The...
█ OVERVIEW
This library is a Pine programmer's tool that provides functions to access Commitment of Traders (COT) data for futures. Four of our scripts use it:
• Commitment of Traders: Legacy Metrics
• Commitment of Traders: Disaggregated Metrics
• Commitment of Traders: Financial Metrics
• Commitment of Traders: Total
If you do not program in...
Library "FunctionBlackScholes"
Some methods for the Black Scholes Options Model, which demonstrates several approaches to the valuation of a European call.
// reference:
// people.math.sc.edu
// people.math.sc.edu
asset_path(s0, mu, sigma, t1, n) Simulates the behavior of an asset price over time.
Parameters:
s0 : float, asset price at...
Library "FunctionMinkowskiDistance"
Method for Minkowski Distance,
The Minkowski distance or Minkowski metric is a metric in a normed vector space
which can be considered as a generalization of both the Euclidean distance and
the Manhattan distance.
It is named after the German mathematician Hermann Minkowski.
reference: en.wikipedia.org
double(point_ax,...
Library "regress"
produces the slope (beta), y-intercept (alpha) and coefficient of determination for a linear regression
regress(x, y, len) regress: computes alpha, beta, and r^2 for a linear regression of y on x
Parameters:
x : the explaining (independent) variable
y : the dependent variable
len : use the most recent "len" values of x and...
Library "FunctionNNPerceptron"
Perceptron Function for Neural networks.
function(inputs, weights, bias, activation_function, alpha, scale) generalized perceptron node for Neural Networks.
Parameters:
inputs : float array, the inputs of the perceptron.
weights : float array, the weights for inputs.
bias : float, default=1.0, the default bias...
Library "MLActivationFunctions"
Activation functions for Neural networks.
binary_step(value) Basic threshold output classifier to activate/deactivate neuron.
Parameters:
value : float, value to process.
Returns: float
linear(value) Input is the same as output.
Parameters:
value : float, value to process.
Returns: float
sigmoid(value) ...
Library "Divergence"
Calculates a divergence between 2 series
bullish(_src, _low, depth) Calculates bullish divergence
Parameters:
_src : Main series
_low : Comparison series (`low` is used if no argument is supplied)
depth : Fractal Depth (`2` is used if no argument is supplied)
Returns: 2 boolean values for regular and hidden...
Library "FunctionPeakDetection"
Method used for peak detection, similar to MATLAB peakdet method
function(sample_x, sample_y, delta) Method for detecting peaks.
Parameters:
sample_x : float array, sample with indices.
sample_y : float array, sample with data.
delta : float, positive threshold value for detecting a peak.
Returns: tuple with...
Library "DailyDeviation"
Helps in determining the relative deviation from the open of the day compared to the high or low values.
hlcDeltaArrays(daysPrior, maxDeviation, spec, res) Retuns a set of arrays representing the daily deviation of price for a given number of days.
Parameters:
daysPrior : Number of days back to get the close from.
...
Library "Volatility"
Functions for determining if volatility (true range) is within or exceeds normal.
The "True Range" (ta.tr) is used for measuring volatility.
Values are normalized by the volume adjusted weighted moving average (VAWMA) to be more like percent moves than price.
current(len) Returns the current price adjusted volatitlity...
Library "DataCleaner"
Functions for acquiring outlier levels and acquiring a cleaned version of a series.
outlierLevel(src, len, level) Gets the (standard deviation) outlier level for a given series.
Parameters:
src : The series to average and add a multiple of the standard deviation to.
len : The The number of bars to measure.
level : The...
Library "bench"
A simple banchmark library to analyse script performance and bottlenecks.
Very useful if you are developing an overly complex application in Pine Script, or trying to optimise a library / function / algorithm...
Supports artificial looping benchmarks (of fast functions)
Supports integrated linear benchmarks (of expensive scripts)
One...
Library "HurstExponent"
Library to calculate Hurst Exponent refactored from Hurst Exponent - Detrended Fluctuation Analysis
demean(src) Calculates a series subtracted from the series mean.
Parameters:
src : The series used to calculate the difference from the mean (e.g. log returns).
Returns: The series subtracted from the series mean
...
Library "Moments"
Based on Moments (Mean,Variance,Skewness,Kurtosis) . Rewritten for Pinescript v5.
logReturns(src) Calculates log returns of a series (e.g log percentage change)
Parameters:
src : Source to use for the returns calculation (e.g. close).
Returns: Log percentage returns of a series
mean(src, length) Calculates the mean of a...
Library "pNRTR"
Provides functions for calculating Nick Rypock Trailing Reverse (NRTR) trend values with higher precision offsets for both low, and high points rather than the standard single offset.
pnrtr(float low_offset = 0.2, float high_offset = 0.2, float value = close)
low_offset
Offset used for nrtr low_point calculations. Default is 0.2.
...
