cacheLibrary "cache"
A simple cache library to store key value pairs.
Fed up of injecting and returning so many values all the time?
Want to separate your code and keep it clean?
Need to make an expensive calculation and use the results in numerous places?
Want to throttle calculations or persist random values across bars or ticks?
Then you've come to the right place. Or not! Up to you, I don't mind either way... ;)
Check the helpers and unit tests in the script for further detail.
Detailed Interface
init(persistant) Initialises the syncronised cache key and value arrays
Parameters:
persistant : bool, toggles data persistance between bars and ticks
Returns: [string , float ], a tuple of both arrays
set(keys, values, key, value) Sets a value into the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to create or update
value : float, the value to set
has(keys, values, key) Checks if the cache has a key
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to check
Returns: bool, true only if the key is found
get(keys, values, key) Gets a keys value from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to get
Returns: float, the stored value
remove(keys, values, key) Removes a key and value from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
key : string, the cache key to remove
count() Counts how many key value pairs in the cache
Returns: int, the total number of pairs
loop(keys, values) Returns true for each value in the cache (use as the while loop expression)
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
next(keys, values) Returns each key value pair on successive calls (use in the while loop)
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
Returns: , tuple of each key value pair
clear(keys, values) Clears all key value pairs from the cache
Parameters:
keys : string , the array of cache keys
values : float , the array of cache values
unittest_cache(case) Cache module unit tests, for inclusion in parent script test suite. Usage: log.unittest_cache(__ASSERTS)
Parameters:
case : string , the current test case and array of previous unit tests (__ASSERTS)
unittest(verbose) Run the cache module unit tests as a stand alone. Usage: cache.unittest()
Parameters:
verbose : bool, optionally disable the full report to only display failures
Statistics
FFTLibraryLibrary "FFTLibrary" contains a function for performing Fast Fourier Transform (FFT) along with a few helper functions. In general, FFT is defined for complex inputs and outputs. The real and imaginary parts of formally complex data are treated as separate arrays (denoted as x and y). For real-valued data, the array of imaginary parts should be filled with zeros.
FFT function
fft(x, y, dir) : Computes the one-dimensional discrete Fourier transform using an in-place complex-to-complex FFT algorithm . Note: The transform also produces a mirror copy of the frequency components, which correspond to the signal's negative frequencies.
Parameters:
x : float array, real part of the data, array size must be a power of 2
y : float array, imaginary part of the data, array size must be the same as x ; for real-valued input, y must be an array of zeros
dir : string, options = , defines the direction of the transform: forward" (time-to-frequency) or inverse (frequency-to-time)
Returns: x, y : tuple (float array, float array), real and imaginary parts of the transformed data (original x and y are changed on output)
Helper functions
fftPower(x, y) : Helper function that computes the power of each frequency component (in other words, Fourier amplitudes squared).
Parameters:
x : float array, real part of the Fourier amplitudes
y : float array, imaginary part of the Fourier amplitudes
Returns: power : float array of the same length as x and y , Fourier amplitudes squared
fftFreq(N) : Helper function that returns the FFT sample frequencies defined in cycles per timeframe unit. For example, if the timeframe is 5m, the frequencies are in cycles/(5 minutes).
Parameters:
N : int, window length (number of points in the transformed dataset)
Returns: freq : float array of N, contains the sample frequencies (with zero at the start).
FunctionProbabilityDistributionSamplingLibrary "FunctionProbabilityDistributionSampling"
Methods for probability distribution sampling selection.
sample(probabilities) Computes a random selected index from a probability distribution.
Parameters:
probabilities : float array, probabilities of sample.
Returns: int.
FunctionElementsInArrayLibrary "FunctionElementsInArray"
Methods to count number of elements in arrays
count_float(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : float array, sample data to process.
value : float value to check for equality.
Returns: int.
count_int(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : int array, sample data to process.
value : int value to check for equality.
Returns: int.
count_string(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : string array, sample data to process.
value : string value to check for equality.
Returns: int.
count_bool(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : bool array, sample data to process.
value : bool value to check for equality.
Returns: int.
count_color(sample, value) Counts the number of elements equal to provided value in array.
Parameters:
sample : color array, sample data to process.
value : color value to check for equality.
Returns: int.
extract_indices_float(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : float array, sample data to process.
value : float value to check for equality.
Returns: int.
extract_indices_int(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : int array, sample data to process.
value : int value to check for equality.
Returns: int.
extract_indices_string(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : string array, sample data to process.
value : string value to check for equality.
Returns: int.
extract_indices_bool(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : bool array, sample data to process.
value : bool value to check for equality.
Returns: int.
extract_indices_color(sample, value) Counts the number of elements equal to provided value in array, and returns its indices.
Parameters:
sample : color array, sample data to process.
value : color value to check for equality.
Returns: int.
LinearRegressionLibraryLibrary "LinearRegressionLibrary" contains functions for fitting a regression line to the time series by means of different models, as well as functions for estimating the accuracy of the fit.
Linear regression algorithms:
RepeatedMedian(y, n, lastBar) applies repeated median regression (robust linear regression algorithm) to the input time series within the selected interval.
Parameters:
y :: float series, source time series (e.g. close)
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
Output:
mSlope :: float, slope of the regression line
mInter :: float, intercept of the regression line
TheilSen(y, n, lastBar) applies the Theil-Sen estimator (robust linear regression algorithm) to the input time series within the selected interval.
Parameters:
y :: float series, source time series
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
Output:
tsSlope :: float, slope of the regression line
tsInter :: float, intercept of the regression line
OrdinaryLeastSquares(y, n, lastBar) applies the ordinary least squares regression (non-robust) to the input time series within the selected interval.
Parameters:
y :: float series, source time series
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
Output:
olsSlope :: float, slope of the regression line
olsInter :: float, intercept of the regression line
Model performance metrics:
metricRMSE(y, n, lastBar, slope, intercept) returns the Root-Mean-Square Error (RMSE) of the regression. The better the model, the lower the RMSE.
Parameters:
y :: float series, source time series (e.g. close)
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
slope :: float, slope of the evaluated linear regression line
intercept :: float, intercept of the evaluated linear regression line
Output:
rmse :: float, RMSE value
metricMAE(y, n, lastBar, slope, intercept) returns the Mean Absolute Error (MAE) of the regression. MAE is is similar to RMSE but is less sensitive to outliers. The better the model, the lower the MAE.
Parameters:
y :: float series, source time series
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
slope :: float, slope of the evaluated linear regression line
intercept :: float, intercept of the evaluated linear regression line
Output:
mae :: float, MAE value
metricR2(y, n, lastBar, slope, intercept) returns the coefficient of determination (R squared) of the regression. The better the linear regression fits the data (compared to the sample mean), the closer the value of the R squared is to 1.
Parameters:
y :: float series, source time series
n :: integer, the length of the selected time interval
lastBar :: integer, index of the last bar of the selected time interval (defines the position of the interval)
slope :: float, slope of the evaluated linear regression line
intercept :: float, intercept of the evaluated linear regression line
Output:
Rsq :: float, R-sqared score
Usage example:
//@version=5
indicator('ExampleLinReg', overlay=true)
// import the library
import tbiktag/LinearRegressionLibrary/1 as linreg
// define the studied interval: last 100 bars
int Npoints = 100
int lastBar = bar_index
int firstBar = bar_index - Npoints
// apply repeated median regression to the closing price time series within the specified interval
{square bracket}slope, intercept{square bracket} = linreg.RepeatedMedian(close, Npoints, lastBar)
// calculate the root-mean-square error of the obtained linear fit
rmse = linreg.metricRMSE(close, Npoints, lastBar, slope, intercept)
// plot the line and print the RMSE value
float y1 = intercept
float y2 = intercept + slope * (Npoints - 1)
if barstate.islast
{indent} line.new(firstBar,y1, lastBar,y2)
{indent} label.new(lastBar,y2,text='RMSE = '+str.format("{0,number,#.#}", rmse))
FunctionCompoundInterestLibrary "FunctionCompoundInterest"
Method for compound interest.
simple_compound(principal, rate, duration) Computes compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rate : float, the rate of interest.
duration : float, the period of growth.
Returns: float.
variable_compound(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
simple_compound_array(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
variable_compound_array(principal, rates, duration) Computes variable compound interest for given duration.
Parameters:
principal : float, the principal or starting value.
rates : float array, the rates of interest.
duration : int, the period of growth.
Returns: float array.
LibraryPrivateUsage001This is a public library that include the functions explained below. The libraries are considered public domain code and permission is not required from the author if you reuse these functions in your open-source scripts
FunctionDecisionTreeLibrary "FunctionDecisionTree"
Method to generate decision tree based on weights.
decision_tree(weights, depth) Method to generate decision tree based on weights.
Parameters:
weights : float array, weights for decision consideration.
depth : int, depth of the tree.
Returns: int array
FunctionForecastLinearLibrary "FunctionForecastLinear"
Method for linear Forecast, same as found in excel and other sheet packages.
forecast(sample_x, sample_y, target_x) linear forecast method.
Parameters:
sample_x : float array, sample data X value.
sample_y : float array, sample data Y value.
target_x : float, target X to get Y forecast value.
Returns: float
FunctionBoxCoxTransformLibrary "FunctionBoxCoxTransform"
Methods to compute the Box-Cox Transformer.
regular(sample, lambda) Regular transform.
Parameters:
sample : float array, sample data values.
lambda : float, scaling factor.
Returns: float array.
inverse(sample, lambda) Regular transform.
Parameters:
sample : float array, sample data values.
lambda : float, scaling factor.
Returns: float array.
FunctionBestFitFrequencyLibrary "FunctionBestFitFrequency"
TODO: add library description here
array_moving_average(sample, length, ommit_initial, fillna) Moving Average values for selected data.
Parameters:
sample : float array, sample data values.
length : int, length to smooth the data.
ommit_initial : bool, default=true, ommit values at the start of the data under the length.
fillna : string, default='na', options='na', '0', 'avg'
Returns: float array
errors:
length > sample size "Canot call array methods when id of array is na."
best_fit_frequency(sample, start, end) Search a frequency range for the fairest moving average frequency.
Parameters:
sample : float array, sample data to based the moving averages.
start : int lowest frequency.
end : int highest frequency.
Returns: tuple with (int frequency, float percentage)
ArrayStatisticsLibrary "ArrayStatistics"
Statistic Functions using arrays.
rms(sample) Root Mean Squared
Parameters:
sample : float array, data sample points.
Returns: float
skewness_pearson1(sample) Pearson's 1st Coefficient of Skewness.
Parameters:
sample : float array, data sample.
Returns: float
skewness_pearson2(sample) Pearson's 2nd Coefficient of Skewness.
Parameters:
sample : float array, data sample.
Returns: float
pearsonr(sample_a, sample_b) Pearson correlation coefficient measures the linear relationship between two datasets.
Parameters:
sample_a : float array, sample with data.
sample_b : float array, sample with data.
Returns: float p
kurtosis(sample) Kurtosis of distribution.
Parameters:
sample : float array, data sample.
Returns: float
range_int(sample, percent) Get range around median containing specified percentage of values.
Parameters:
sample : int array, Histogram array.
percent : float, Values percentage around median.
Returns: tuple with , Returns the range which containes specifies percentage of values.
ProbabilityLibrary "Probability"
erf(value) Complementary error function
Parameters:
value : float, value to test.
Returns: float
ierf_mcgiles(value) Computes the inverse error function using the Mc Giles method, sacrifices accuracy for speed.
Parameters:
value : float, -1.0 >= _value >= 1.0 range, value to test.
Returns: float
ierf_double(value) computes the inverse error function using the Newton method with double refinement.
Parameters:
value : float, -1. > _value > 1. range, _value to test.
Returns: float
ierf(value) computes the inverse error function using the Newton method.
Parameters:
value : float, -1. > _value > 1. range, _value to test.
Returns: float
complement(probability) probability that the event will not occur.
Parameters:
probability : float, 0 >=_p >= 1, probability of event.
Returns: float
entropy_gini_impurity_single(probability) Gini Inbalance or Gini index for a given probability.
Parameters:
probability : float, 0>=x>=1, probability of event.
Returns: float
entropy_gini_impurity(events) Gini Inbalance or Gini index for a series of events.
Parameters:
events : float , 0>=x>=1, array with event probability's.
Returns: float
entropy_shannon_single(probability) Entropy information value of the probability of a single event.
Parameters:
probability : float, 0>=x>=1, probability value.
Returns: float, value as bits of information.
entropy_shannon(events) Entropy information value of a distribution of events.
Parameters:
events : float , 0>=x>=1, array with probability's.
Returns: float
inequality_chebyshev(n_stdeviations) Calculates Chebyshev Inequality.
Parameters:
n_stdeviations : float, positive over or equal to 1.0
Returns: float
inequality_chebyshev_distribution(mean, std) Calculates Chebyshev Inequality.
Parameters:
mean : float, mean of a distribution
std : float, standard deviation of a distribution
Returns: float
inequality_chebyshev_sample(data_sample) Calculates Chebyshev Inequality for a array of values.
Parameters:
data_sample : float , array of numbers.
Returns: float
intersection_of_independent_events(events) Probability that all arguments will happen when neither outcome
is affected by the other (accepts 1 or more arguments)
Parameters:
events : float , 0 >= _p >= 1, list of event probabilities.
Returns: float
union_of_independent_events(events) Probability that either one of the arguments will happen when neither outcome
is affected by the other (accepts 1 or more arguments)
Parameters:
events : float , 0 >= _p >= 1, list of event probabilities.
Returns: float
mass_function(sample, n_bins) Probabilities for each bin in the range of sample.
Parameters:
sample : float , samples to pool probabilities.
n_bins : int, number of bins to split the range
@return float
cumulative_distribution_function(mean, stdev, value) Use the CDF to determine the probability that a random observation
that is taken from the population will be less than or equal to a certain value.
Or returns the area of probability for a known value in a normal distribution.
Parameters:
mean : float, samples to pool probabilities.
stdev : float, number of bins to split the range
value : float, limit at which to stop.
Returns: float
transition_matrix(distribution) Transition matrix for the suplied distribution.
Parameters:
distribution : float , array with probability distribution. ex:.
Returns: float
diffusion_matrix(transition_matrix, dimension, target_step) Probability of reaching target_state at target_step after starting from start_state
Parameters:
transition_matrix : float , "pseudo2d" probability transition matrix.
dimension : int, size of the matrix dimension.
target_step : number of steps to find probability.
Returns: float
state_at_time(transition_matrix, dimension, start_state, target_state, target_step) Probability of reaching target_state at target_step after starting from start_state
Parameters:
transition_matrix : float , "pseudo2d" probability transition matrix.
dimension : int, size of the matrix dimension.
start_state : state at which to start.
target_state : state to find probability.
target_step : number of steps to find probability.
MathStatisticsKernelDensityEstimationLibrary "MathStatisticsKernelDensityEstimation"
(KDE) Method for Kernel Density Estimation
kde(observations, kernel, bandwidth, nsteps)
Parameters:
observations : float array, sample data.
kernel : string, the kernel to use, default='gaussian', options='uniform', 'triangle', 'epanechnikov', 'quartic', 'triweight', 'gaussian', 'cosine', 'logistic', 'sigmoid'.
bandwidth : float, bandwidth to use in kernel, default=0.5, range=(0, +inf), less will smooth the data.
nsteps : int, number of steps in range of distribution, default=20, this value is connected to how many line objects you can display per script.
Returns: tuple with signature: (float array, float array)
draw_horizontal(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a horizontal distribution at current location on chart.
Parameters:
distribution_x : float array, distribution points x value.
distribution_y : float array, distribution points y value.
distribution_lines : line array, array to append the distribution curve lines.
graph_lines : line array, array to append the graph lines.
graph_labels : label array, array to append the graph labels.
Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels.
draw_vertical(distribution_x, distribution_y, distribution_lines, graph_lines, graph_labels) Draw a vertical distribution at current location on chart.
Parameters:
distribution_x : float array, distribution points x value.
distribution_y : float array, distribution points y value.
distribution_lines : line array, array to append the distribution curve lines.
graph_lines : line array, array to append the graph lines.
graph_labels : label array, array to append the graph labels.
Returns: void, updates arrays: distribution_lines, graph_lines, graph_labels.
style_distribution(lines, horizontal, to_histogram, line_color, line_style, linewidth) Style the distribution lines.
Parameters:
lines : line array, distribution lines to style.
horizontal : bool, default=true, if the display is horizontal(true) or vertical(false).
to_histogram : bool, default=false, if graph style should be switched to histogram.
line_color : color, default=na, if defined will change the color of the lines.
line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both)
linewidth : int, default=na, if defined will change the line width.
Returns: void.
style_graph(lines, lines, horizontal, line_color, line_style, linewidth) Style the graph lines and labels
Parameters:
lines : line array, graph lines to style.
lines : labels array, graph labels to style.
horizontal : bool, default=true, if the display is horizontal(true) or vertical(false).
line_color : color, default=na, if defined will change the color of the lines.
line_style : string, defaul=na, if defined will change the line style, options=('na', line.style_solid, line.style_dotted, line.style_dashed, line.style_arrow_right, line.style_arrow_left, line.style_arrow_both)
linewidth : int, default=na, if defined will change the line width.
Returns: void.
MathStatisticsKernelFunctionsLibrary "MathStatisticsKernelFunctions"
TODO: add library description here
uniform(distance, bandwidth) Uniform kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
triangular(distance, bandwidth) Triangular kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
epanechnikov(distance, bandwidth) Epanechnikov kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
quartic(distance, bandwidth) Quartic kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
triweight(distance, bandwidth) Triweight kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
tricubic(distance, bandwidth) Tricubic kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
gaussian(distance, bandwidth) Gaussian kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
cosine(distance, bandwidth) Cosine kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
logistic(distance, bandwidth) logistic kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
sigmoid(distance, bandwidth) Sigmoid kernel.
Parameters:
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
select(kernel, distance, bandwidth) Kernel selection method.
Parameters:
kernel : string, kernel to select. (options="uniform", "triangle", "epanechnikov", "quartic", "triweight", "tricubic", "gaussian", "cosine", "logistic", "sigmoid")
distance : float, distance to kernel origin.
bandwidth : float, default=1.0, bandwidth limiter to weight the kernel.
Returns: float.
MathSearchDijkstraLibrary "MathSearchDijkstra"
Shortest Path Tree Search Methods using Dijkstra Algorithm.
min_distance(distances, flagged_vertices) Find the lowest cost/distance.
Parameters:
distances : float array, data set with distance costs to start index.
flagged_vertices : bool array, data set with visited vertices flags.
Returns: int, lowest cost/distance index.
dijkstra(matrix_graph, dim_x, dim_y, start) Dijkstra Algorithm, perform a greedy tree search to calculate the cost/distance to selected start node at each vertex.
Parameters:
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
start : int, the vertex index to start search.
Returns: int array, set with costs/distances to each vertex from start vertexs.
shortest_path(start, end, matrix_graph, dim_x, dim_y) Retrieves the shortest path between 2 vertices in a graph using Dijkstra Algorithm.
Parameters:
start : int, the vertex index to start search.
end : int, the vertex index to end search.
matrix_graph : int array, matrix holding the graph adjacency list and costs/distances.
dim_x : int, x dimension of matrix_graph.
dim_y : int, y dimension of matrix_graph.
Returns: int array, set with vertex indices to the shortest path.
MathFinancialAbsoluteRiskMeasuresLibrary "MathFinancialAbsoluteRiskMeasures"
Financial Absolute Risk Measures.
gain_stdev(sample) Standard deviation of gains in a data sample.
Parameters:
sample : float array, data sample.
Returns: float.
loss_stdev(sample) Standard deviation of losses in a data sample.
Parameters:
sample : float array, data sample.
Returns: float.
downside_stdev(sample, minimal_acceptable_return) Downside standard deviation in a data sample.
Parameters:
sample : float array, data sample.
minimal_acceptable_return : float, minimum gain value.
Returns: float.
semi_stdev(sample) Standard deviation of less than average returns in a data sample.
Parameters:
sample : float array, data sample.
Returns: float.
gain_loss_ratio(sample) ratio of average gains of average losses in a data sample.
Parameters:
sample : float array, data sample.
Returns: float.
compound_risk_score(source, length) Compound Risk Score
Parameters:
source : float, input data, default=close.
length : int, period of observation, default=12)
Returns: float.
SignalProcessingClusteringKMeansLibrary "SignalProcessingClusteringKMeans"
K-Means Clustering Method.
nearest(point_x, point_y, centers_x, centers_y) finds the nearest center to a point and returns its distance and center index.
Parameters:
point_x : float, x coordinate of point.
point_y : float, y coordinate of point.
centers_x : float array, x coordinates of cluster centers.
centers_y : float array, y coordinates of cluster centers.
@ returns tuple of int, float.
bisection_search(samples, value) Bissection Search
Parameters:
samples : float array, weights to compare.
value : float array, weights to compare.
Returns: int.
label_points(points_x, points_y, centers_x, centers_y) labels each point index with cluster index and distance.
Parameters:
points_x : float array, x coordinates of points.
points_y : float array, y coordinates of points.
centers_x : float array, x coordinates of points.
centers_y : float array, y coordinates of points.
Returns: tuple with int array, float array.
kpp(points_x, points_y, n_clusters) K-Means++ Clustering adapted from Andy Allinger.
Parameters:
points_x : float array, x coordinates of the points.
points_y : float array, y coordinates of the points.
n_clusters : int, number of clusters.
Returns: tuple with 2 arrays, float array, int array.
AnalysisInterpolationLoessLibrary "AnalysisInterpolationLoess"
LOESS, local weighted Smoothing function.
loess(sample_x, sample_y, point_span) LOESS, local weighted Smoothing function.
Parameters:
sample_x : int array, x values.
sample_y : float array, y values.
point_span : int, local point interval span.
aloess(sample_x, sample_y, point_span) aLOESS, adaptive local weighted Smoothing function.
Parameters:
sample_x : int array, x values.
sample_y : float array, y values.
point_span : int, local point interval span.
Matrix_Functions_Lib_JDLibrary "Matrix_Functions_Lib_JD"
This is a library to add matrix / 2D array functionality to Pinescript.
once you import the library at the beginning of your script, you can add all the functions described below just by calling them like you do any other built'in function.
Enjoy,
Gr, JD.
PS. if you find functionality or calculation errors in the functions, please let me know, so I can fix them.
There are quite a lot of functions, so little mishaps may have slipped in! ;-)
get_nr_of_rows() Returns the number of rows from a 2D matrix
get_nr_of_columns() Returns the number of columns from a 2D matrix
get_size() Returns a tuple with the total number of rows and columns from a 2D matrix
init() 2D matrix init function, builds a 2D matrix with dimensional metadata in first two values and fills it with a default value, the body of the actual matrix data starts at index 2.
from_list() 2D matrix init function, builds a 2D matrix from an existing array by adding dimensional metadata in first two values, the body of the actual matrix data consists of the data of the source array and starts at index 2.
set() Sets values in 2D matrix with (row index, column index) (index for rows and columns both starts at 0 !!)
fill_val() Fills all elements in a 2D matrix with a value
randomize() Fills a 2D matrix with random values//
get() Gets values from 2D matrix with (row index, column index) (index for rows and columns both starts at 0 !!)
copy_slice_body() Cuts off the metadata header and returns the array body, WITHOUT THE DIMENSIONAL METADATA!!
do_slice This variable should be set as: - 'false' to only make a copy, changes to the new array copy will NOT ALTER the ORIGINAL - 'true' to make a slice, changes to the new array slice WILL(!) ALTER the ORIGINAL
get_record() Gets /retrieve the values from a ROW/RECORD from a certain row/lookback period, the values are returned as an array
get_row_index() Gets the row nr. in a 2D matrix from 1D index (index for rows and columns both starts at 0 !!)
get_column_index() Gets the column nr. in a 2D matrix from 1D index (index for rows and columns both starts at 0 !!)
get_row_column_index() Gets a tuple with the (row, column) coordinates in 2D matrix from 1D index (index starts at 0 and does not include the header!!)
get_array_index() Gets the 1D index from (row, column) coordinates in 2D matrix (index for row and column both starts at 0 !! Index starts at 0 and does not include the header!!)
remove_rows() Removes one or more rows/records from a 2D matrix (if from_row = to_row, only this row is removed)
remove_columns() Remove one or more columns from a 2D matrix (if from_column = to_column, only this column is removed)
insert_array_of_rows() Insert an array of rows/records at a certain row number in a 2D matrix
add_row() ADDS a ROW/RECORD on the TOP of a sheet, shift the whole list one down and gives the option to REMOVE the OLDEST row/record. (2D version of "unshift" + "pop" but with a whole row at once)
insert_array_of_columns() Insert an array of columns at a certain column number in a 2D matrix
append_array_of_rows() Appends/adds an array of rows/records to the bottom of a 2D matrix
append_array_of_columns() Appends/adds an array of columns to the right side of a 2D matrix
pop_row() Removes / pops and returns the last row/record from a 2D matrix.
pop_column() Removes / pops and returns the last (most right) column from a 2D matrix.
replace()
abs()
add_value() Returns a new matrix with the same value added to all the elements of the source matrix.
addition() Returns a new matrix with the of the elements of one 2D matrix added to every corresponding element of a source 2D matrix.
subtract_value() Returns a new matrix with the same value subtracted from every element of a 2D matrix
subtraction() Returns a new matrix with the values of the elements of one 2D matrix subtracted from every corresponding element of a source 2D matrix.
scalar_multipy() Returns a new matrix with all the elements of the source matrix scaled/multiplied by a scalar value.
transpose() Returns a new matrix with the elements of the source matrix transposed.
multiply_elem() Performs ELEMENT WISE MULTIPLICATION of 2D matrices, returns a new matrix c.
multiply() Performs DOT PROCUCT MULTIPLICATION of 2D matrices, returns a new matrix c.
determinant_2x2() Calculates the determinant of 2x2 matrices.
determinant_3x3() Calculates the determinant of 3x3 matrices.
determinant_4x4() Calculates the determinant of 4x4 matrices.
print() displays a 2D matrix in a table layout.
