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Indicators and strategies

ArrayOperationsIntLibrary "ArrayOperationsInt" Array Basic Operations for Integers add(sample_a, sample_b) Adds sample_b to sample_a and returns a new array. Parameters: sample_a : values to be added to. sample_b : values to add. Returns: int array with added results. subtract(sample_a, sample_b) subtracts sample_b from sample_a and returns a new array. Parameters: sample_a : values to be subtracted from. sample_b : values to subtract. Returns: int array with subtracted results. multiply(sample_a, sample_b) multiply sample_a with sample_b and returns a new array. Parameters: sample_a : values to multiply. sample_b : values to multiply with. Returns: int array with multiplied results. divide(sample_a, sample_b) divide sample_a with sample_b and returns a new array. Parameters: sample_a : values to divide. sample_b : values to divide with. Returns: int array with divided results. power(sample_a, sample_b) rise sample_a to the power of sample_b and returns a new array. Parameters: sample_a : base values to raise. sample_b : values of exponents. Returns: int array with raised results. remainder(sample_a, sample_b) integer remainder of sample_a under the dividend sample_b and returns a new array. Parameters: sample_a : values of quotients. sample_b : values of dividends. Returns: int array with remainder results.
Pine Script™ library
by RicardoSantos
ArrayOperationsFloatLibrary "ArrayOperationsFloat" Array Basic Operations for Integers add(sample_a, sample_b) Adds sample_b to sample_a and returns a new array. Parameters: sample_a : values to be added to. sample_b : values to add. Returns: float array with added results. subtract(sample_a, sample_b) subtracts sample_b from sample_a and returns a new array. Parameters: sample_a : values to be subtracted from. sample_b : values to subtract. Returns: float array with subtracted results. multiply(sample_a, sample_b) multiply sample_a with sample_b and returns a new array. Parameters: sample_a : values to multiply. sample_b : values to multiply with. Returns: float array with multiplied results. divide(sample_a, sample_b) divide sample_a with sample_b and returns a new array. Parameters: sample_a : values to divide. sample_b : values to divide with. Returns: float array with divided results. power(sample_a, sample_b) rise sample_a to the power of sample_b and returns a new array. Parameters: sample_a : base values to raise. sample_b : values of exponents. Returns: float array with raised results. remainder(sample_a, sample_b) float remainder of sample_a under the dividend sample_b and returns a new array. Parameters: sample_a : values of quotients. sample_b : values of dividends. Returns: float array with remainder results.
Pine Script™ library
by RicardoSantos
3
LineGetPriceOnLogScaleLibrary "LineGetPriceOnLogScale" This library provides a way to calculate the y-coordinate of a line on a specified bar when the chart scale is Log. The built-in `line.get_price()` function only works with linear scale and gives incorrect results when the chart is in Log scale. The library only works with `bar_index` values and `xloc.bar_index`-based lines, `time`-based lines will cause errors to appear. coordGetPriceLog(x1, y1, x2, y2, xi) Calculates the y-coordinate on the specified bar on the logarithmic scale. Only coordinates based on bar index are applicable, bar time will throw an error. Parameters: x1 : First X coordinate of a line, index of the bar where the line starts. y1 : First Y coordinate of a line, price on the price scale. x2 : Second X coordinate of a line, index of the bar where the line ends. y2 : Second Y coordinate of a line, price on the price scale. xi : Index of the bar for which the price should be calculated. Returns: Price of the line on the bar specified in `xi`, on the logarithmic scale. lineGetPriceLog(_line, xi) Calculates the y-coordinate on the specified bar for the logarithmic scale. Takes a line. Only lines drawn based on `xloc.bar_index` are applicable, `xloc.bar_time` will throw and error. Parameters: _line : The line for which the price is calculated. xi : Index of the bar for which the bar should calculate the price. Returns: Price of the line on the bar specified in `xi`, on the logarithmic scale.
Pine Script™ library
by BeeHolder
2
StringEvaluationLibrary "StringEvaluation" Methods to handle evaluation of strings. is_comma(char) Check if char is a comma ".". Parameters: char : string, 1 character string. Returns: bool. is_op(char) Check if char is a operator. Parameters: char : string, 1 character string. Returns: bool. number(char) convert a single char string into valid number. Parameters: char : string, 1 character string. Returns: float. operator(op, left, right) operation between left and right values. Parameters: op : string, operator string character. left : float, left value of operation. right : float, right value of operation. operator_precedence(op) level of precedence of operator. Parameters: op : string, operator 1 char string. Returns: int. cleanup(_str) Evaluate a string to clean up and retrieve only used chars Parameters: _str : string, arithmetic operations in a string. Returns: string array, evaluated array. generate_rpn(tokens) uses Shunting-Yard algorithm to generate a RPN (Reverse Polish notation) array of strings from a array of strings containing arithmetic notation. ex:.. ' ' --> ' ' Parameters: tokens : string array, array with arithmetic notation. Returns: parse_rpn() evaluate a RPN (Reverse Polish notation) array of strings. ex:.. 3 4 2 * 1 5 - 2 3 ^ ^ / + | @param tokens string array, RPN ordered tokens, ex( ). | @returns float, solution. eval() evaluate a string with references to a array of arguments. | @param tokens string, arithmetic operations with references to indices in arguments, ex:"0+1*0+2*2+3" arguments | @param arguments float array, arguments. | @returns float, solution.
Pine Script™ library
by RicardoSantos
2
SignificantFiguresLibrary "SignificantFigures" sigFig(float _float, int _figures) @description Takes a floating-point number - one that can, but doesn't have to, include a decimal point - and converts it to a floating-point number with only a certain number of digits left. For example, say you want to display a variable from your script to the user and it comes out to something like 45.366666666666666666666667 or whatever. That looks awful when you, for example, print it in a label. Now you could round it up to the nearest integer easily using a built-in function, or even to a certain number of decimal places using a reasonably simple custom function. But that's a bit arbitrary. Suppose you don't know what asset the script will be used on, and so you can't predict what the price is, and what the value will turn out to be. It could be 0.00045366666666666666666666667 instead. Now if you round it up to 3 decimal places it comes out as 0.000, which is useless. My function will round that number to 0.0004536 instead, if told to do it to 4 significant digits. I think this is more friendly. @function Converts float with arbitrary number of digits to one with a specified number of significant figures. @param float _float is the floating-point number to manipulate. @param int _figures is the number of significant figures you want. @returns Returns a float with the specified number of significant figures
Pine Script™ library
by SimpleCryptoLife
7
MathSpecialFunctionsGammaLibrary "MathSpecialFunctionsGamma" Gamma Functions. GammaQ(index) Enumeration of the polynomial coefficients for the "GammaLn" approximation. Parameters: index : int, 0 => index => 10, index of coeficient. Returns: float GammaLn(z) Computes the logarithm of the Gamma function. Parameters: z : The argument of the gamma function. Returns: The logarithm of the gamma function. Gamma(z) Computes the Gamma function. Parameters: z : The argument of the gamma function. Returns: float, The logarithm of the gamma function. GammaLowerRegularized(a, x) Parameters: a : float, The argument for the gamma function. x : float, The upper integral limit. Returns: float, The lower incomplete gamma function. GammaUpperRegularized(a, x) Returns the upper incomplete regularized gamma function Parameters: a : float, The argument for the gamma function. x : float, The lower integral limit. Returns: float, The upper incomplete regularized gamma function. GammaUpperIncomplete(a, x) Returns the upper incomplete gamma function. Parameters: a : float, The argument for the gamma function. x : float, The lower integral limit. Returns: float, The upper incomplete gamma function. GammaLowerIncomplete(a, x) Parameters: a : float, The argument for the gamma function. x : float, The upper integral limit. Returns: float, The lower incomplete gamma function.
Pine Script™ library
by RicardoSantos
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.
Pine Script™ library
by RicardoSantos
1
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.
Pine Script™ library
by RicardoSantos
10
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.
Pine Script™ library
by RicardoSantos
3
MathTransformsHartleyLibrary "MathTransformsHartley" implementation of the Fast Discrete Hartley Transform(DHT). naive(samples) Generic naive transform for the (DHT). Parameters: samples : float array, 1d data. Returns: float array. fdht(samples) Fast Discrete Hartley Transform (DHT). Parameters: samples : float array, data samples. Returns: float array. idht(samples, asymmetric_scaling) Inverse Discrete Hartley Transform (DHT). Parameters: samples : float array, data samples. asymmetric_scaling : bool, default=true, scaling option. Returns: float array.
Pine Script™ library
by RicardoSantos
MathSpecialFunctionsTestFunctionsLibrary "MathSpecialFunctionsTestFunctions" Methods for test functions. rosenbrock(input_x, input_y) Valley-shaped Rosenbrock function for 2 dimensions: (x,y) -> (1-x)^2 + 100*(y-x^2)^2. Parameters: input_x : float, common range within (-5.0, 10.0) or (-2.048, 2.048). input_y : float, common range within (-5.0, 10.0) or (-2.048, 2.048). Returns: float rosenbrock_mdim(samples) Valley-shaped Rosenbrock function for 2 or more dimensions. Parameters: samples : float array, common range within (-5.0, 10.0) or (-2.048, 2.048). Returns: float himmelblau(input_x, input_y) Himmelblau, a multi-modal function: (x,y) -> (x^2+y-11)^2 + (x+y^2-7)^2 Parameters: input_x : float, common range within (-6.0, 6.0 ). input_y : float, common range within (-6.0, 6.0 ). Returns: float rastrigin(samples) Rastrigin, a highly multi-modal function with many local minima. Parameters: samples : float array, common range within (-5.12, 5.12 ). Returns: float drop_wave(input_x, input_y) Drop-Wave, a multi-modal and highly complex function with many local minima. Parameters: input_x : float, common range within (-5.12, 5.12 ). input_y : float, common range within (-5.12, 5.12 ). Returns: float ackley(input_x) Ackley, a function with many local minima. It is nearly flat in outer regions but has a large hole at the center. Parameters: input_x : float array, common range within (-32.768, 32.768 ). Returns: float bohachevsky1(input_x, input_y) Bowl-shaped first Bohachevsky function. Parameters: input_x : float, common range within (-100.0, 100.0 ). input_y : float, common range within (-100.0, 100.0 ). Returns: float matyas(input_x, input_y) Plate-shaped Matyas function. Parameters: input_x : float, common range within (-10.0, 10.0 ). input_y : float, common range within (-10.0, 10.0 ). Returns: float six_hump_camel(input_x, input_y) Valley-shaped six-hump camel back function. Parameters: input_x : float, common range within (-3.0, 3.0 ). input_y : float, common range within (-2.0, 2.0 ). Returns: float
Pine Script™ library
by RicardoSantos
MathGeometryCurvesChaikinLibrary "MathGeometryCurvesChaikin" Implements the chaikin algorithm to create a curved path, from assigned points. chaikin(points_x, points_y, closed) Chaikin algorithm method, uses provided points to generate a smoothed path. Parameters: points_x : float array, the x value of points. points_y : float array, the y value of points. closed : bool, default=false, is the path closed or not. Returns: tuple with 2 float arrays. smooth(points_x, points_y, iterations, closed) Iterate the chaikin algorithm, to smooth a sample of points into a curve path. Parameters: points_x : float array, the x value of points. points_y : float array, the y value of points. iterations : int, number of iterations to apply the smoothing. closed : bool, default=false, is the path closed or not. Returns: array of lines. draw(path_x, path_y, closed) Draw the path. Parameters: path_x : float array, the x value of the path. path_y : float array, the y value of the path. closed : bool, default=false, is the path closed or not. Returns: array of lines.
Pine Script™ library
by RicardoSantos
2
Double_Triple_EMALibrary "Double_Triple_EMA" Provides the functions to calculate Double and Triple Exponentional Moving Averages (DEMA & TEMA). dema(_source, _length) Calculates Double Exponentional Moving Averages (DEMA) Parameters: _source : -> Open, Close, High, Low, etc ('close' is used if no argument is supplied) _length : -> DEMA length Returns: Double Exponential Moving Average (DEMA) of an input source at the specified input length tema(_source, _length) Calculates Triple Exponentional Moving Averages (TEMA) Parameters: _source : -> Open, Close, High, Low, etc ('close' is used if no argument is supplied) _length : -> TEMA length Returns: Triple Exponential Moving Average (TEMA) of an input source at the specified input length
Pine Script™ library
by KevanoTrades
MathTrigonometryLibrary "MathTrigonometry" Trigonometric methods. sinc(value) Normalized sinc function. Parameters: value : float, value. Returns: float. cot(value) Cotangent of value. Parameters: value : float, value. Returns: float. csc(value) Cosecant of value. Parameters: value : float, value. Returns: float. sec(value) Secant of value. Parameters: value : float, value. Returns: float. acot(value) Arc cotangent of value. Parameters: value : float, adjacent value. Returns: float. asec(value) Arc secant of value. Parameters: value : float, hypotenuse value. Returns: float. acsc(value) Arc cosecant of value. Parameters: value : float, hipotenuse value. Returns: float. sinh(angle) Hyperbolic sine of angle. Parameters: angle : float, value. Returns: float. cosh(angle) Hyperbolic cosine of angle. Parameters: angle : float, value. Returns: float. tanh(angle) Hyperbolic tangent of angle. Parameters: angle : float, value. Returns: float. coth(angle) Hyperbolic cotangent of angle. Parameters: angle : float, value. Returns: float. sech(angle) Hyperbolic secant of angle. Parameters: angle : float, value. Returns: float. csch(angle) Hyperbolic cosecant of angle. Parameters: angle : float, value. Returns: float. asinh(value) Hyperbolic area sine. Parameters: value : float, value. Returns: float. acosh(value) Hyperbolic area cosine. Parameters: value : float, value. Returns: float. atanh(value) Hyperbolic area tangent. Parameters: value : float, value. Returns: float. acoth(value) Hyperbolic area cotangent. Parameters: value : float, value. Returns: float. asech(value) Hyperbolic area secant. Parameters: value : float, value. Returns: float. acsch(value) Hyperbolic area cosecant. Parameters: value : float, value. Returns: float.
Pine Script™ library
by RicardoSantos
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.
Pine Script™ library
by RicardoSantos
5
MathGaussFunctionLibrary "MathGaussFunction" Implements multiple gauss methods. f_1d(point_x, sigma) 1-D Gaussian function. Parameters: point_x : float, x value. sigma : float, sigma value, default=1.0. Returns: float, function's value at point_x. f_2d(point_x, point_y, sigma) 2-D Gaussian function. Parameters: point_x : float, x value. point_y : float, y value. sigma : float, sigma value, default=1.0. Returns: float, function's value at (point_x, point_y). kernel_1d(size, sigma) 1-D Gaussian kernel. Parameters: size : int, Kernel size (should be odd), . sigma : float, sigma value, default=1.0. Returns: float array, Returns 1-D Gaussian kernel of the specified size. kernel_2d(size, sigma) 2-D Gaussian kernel. Parameters: size : int, Kernel size (should be odd), . sigma : float, sigma value, default=1.0. Returns: float array, Returns 2-D Gaussian kernel of the specified size.
Pine Script™ library
by RicardoSantos
3
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.
Pine Script™ library
by RicardoSantos
2
MathOperatorLibrary "MathOperator" Methods to handle operators. add(value_a, value_b) Add value a to b. Parameters: value_a : float, value a. value_b : float, value b. Returns: float. subtract(value_a, value_b) subtract value b from a. Parameters: value_a : float, value a. value_b : float, value b. Returns: float. multiply(value_a, value_b) multiply value a with b. Parameters: value_a : float, value a. value_b : float, value b. Returns: float. divide(value_a, value_b) divide value a with b. Parameters: value_a : float, value a. value_b : float, value b. Returns: float. remainder(value_a, value_b) remainder of a with b. Parameters: value_a : float, value a. value_b : float, value b. Returns: float. equal(value_a, value_b) equality of value a with b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. not_equal(value_a, value_b) inequality of value a with b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. over(value_a, value_b) value a is over b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. under(value_a, value_b) value a is under b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. over_equal(value_a, value_b) value a is over equal b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. under_equal(value_a, value_b) value a is under equal b. Parameters: value_a : float, value a. value_b : float, value b. Returns: bool. and_(value_a, value_b) logical and of a with b Parameters: value_a : bool, value a. value_b : bool, value b. Returns: bool. or_(value_a, value_b) logical or of a with b. Parameters: value_a : bool, value a. value_b : bool, value b. Returns: bool.
Pine Script™ library
by RicardoSantos
3