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MathProbabilityDistributionLibrary "MathProbabilityDistribution" Probability Distribution Functions. name(idx) Indexed names helper function.   Parameters:      idx : int, position in the range (0, 6).   Returns: string, distribution name. usage: .name(1) Notes: (0) => 'StdNormal' (1) => 'Normal' (2) => 'Skew Normal' (3) => 'Student T' (4) => 'Skew Student T' (5) => 'GED' (6) => 'Skew GED' zscore(position, mean, deviation) Z-score helper function for x calculation.   Parameters:      position : float, position.      mean : float, mean.      deviation : float, standard deviation.   Returns: float, z-score. usage: .zscore(1.5, 2.0, 1.0) std_normal(position) Standard Normal Distribution.   Parameters:      position : float, position.   Returns: float, probability density. usage: .std_normal(0.6) normal(position, mean, scale) Normal Distribution.   Parameters:      position : float, position in the distribution.      mean : float, mean of the distribution, default=0.0 for standard distribution.      scale : float, scale of the distribution, default=1.0 for standard distribution.   Returns: float, probability density. usage: .normal(0.6) skew_normal(position, skew, mean, scale) Skew Normal Distribution.   Parameters:      position : float, position in the distribution.      skew : float, skewness of the distribution.      mean : float, mean of the distribution, default=0.0 for standard distribution.      scale : float, scale of the distribution, default=1.0 for standard distribution.   Returns: float, probability density. usage: .skew_normal(0.8, -2.0) ged(position, shape, mean, scale) Generalized Error Distribution.   Parameters:      position : float, position.      shape : float, shape.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .ged(0.8, -2.0) skew_ged(position, shape, skew, mean, scale) Skew Generalized Error Distribution.   Parameters:      position : float, position.      shape : float, shape.      skew : float, skew.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .skew_ged(0.8, 2.0, 1.0) student_t(position, shape, mean, scale) Student-T Distribution.   Parameters:      position : float, position.      shape : float, shape.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .student_t(0.8, 2.0, 1.0) skew_student_t(position, shape, skew, mean, scale) Skew Student-T Distribution.   Parameters:      position : float, position.      shape : float, shape.      skew : float, skew.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.   Returns: float, probability. usage: .skew_student_t(0.8, 2.0, 1.0) select(distribution, position, mean, scale, shape, skew, log) Conditional Distribution.   Parameters:      distribution : string, distribution name.      position : float, position.      mean : float, mean, default=0.0 for standard distribution.      scale : float, scale, default=1.0 for standard distribution.      shape : float, shape.      skew : float, skew.      log : bool, if true apply log() to the result.   Returns: float, probability. usage: .select('StdNormal', __CYCLE4F__, log=true)
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por RicardoSantos
5
historicalrangeLibrary "historicalrange" Library provices a method to calculate historical percentile range of series. hpercentrank(source) calculates historical percentrank of the source   Parameters:      source : Source for which historical percentrank needs to be calculated. Source should be ranging between 0-100. If using a source which can beyond 0-100, use short term percentrank to baseline them.   Returns: pArray - percentrank array which contains how many instances of source occurred at different levels. upperPercentile - percentile based on higher value lowerPercentile - percentile based on lower value median - median value of the source max - max value of the source distancefromath(source) returns stats on historical distance from ath in terms of percentage   Parameters:      source : for which stats are calculated   Returns: percentile and related historical stats regarding distance from ath distancefromma(maType, length, source) returns stats on historical distance from moving average in terms of percentage   Parameters:      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      source : for which stats are calculated   Returns: percentile and related historical stats regarding distance from ath bpercentb(source, maType, length, multiplier, sticky) returns percentrank and stats on historical bpercentb levels   Parameters:      source : Moving Average Source      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      multiplier : Standard Deviation multiplier      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Bollinger Percent B kpercentk(source, maType, length, multiplier, useTrueRange, sticky) returns percentrank and stats on historical kpercentk levels   Parameters:      source : Moving Average Source      maType : Moving Average Type : Can be sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      length : Moving Average Length      multiplier : Standard Deviation multiplier      useTrueRange : - if set to false, uses high-low.      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Keltener Percent K dpercentd(useAlternateSource, alternateSource, length, sticky) returns percentrank and stats on historical dpercentd levels   Parameters:      useAlternateSource : - Custom source is used only if useAlternateSource is set to true      alternateSource : - Custom source      length : - donchian channel length      sticky : - sticky boundaries which will only change when value is outside boundary.   Returns: percentile and related historical stats regarding Donchian Percent D oscillator(type, length, shortLength, longLength, source, highSource, lowSource, method, highlowLength, sticky) oscillator - returns Choice of oscillator with custom overbought/oversold range   Parameters:      type : - oscillator type. Valid values : cci, cmo, cog, mfi, roc, rsi, stoch, tsi, wpr      length : - Oscillator length - not used for TSI      shortLength : - shortLength only used for TSI      longLength : - longLength only used for TSI      source : - custom source if required      highSource : - custom high source for stochastic oscillator      lowSource : - custom low source for stochastic oscillator      method : - Valid values for method are : sma, ema, hma, rma, wma, vwma, swma, highlow, linreg, median      highlowLength : - length on which highlow of the oscillator is calculated      sticky : - overbought, oversold levels won't change unless crossed   Returns: percentile and related historical stats regarding oscillator
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por Trendoscope
1
ArrayOperationsLibrary "ArrayOperations" Array element wise basic operations. 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: array with added results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b subtract(sample_a, sample_b) Subtracts sample_b from sample_a and returns a new array.      sample_a : values to be subtracted from.      sample_b : values to subtract.   Returns: array with subtracted results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b multiply(sample_a, sample_b) multiply sample_a by sample_b and returns a new array.      sample_a : values to multiply.      sample_b : values to multiply with.   Returns: array with multiplied results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b divide(sample_a, sample_b) Divide sample_a by sample_b and returns a new array.      sample_a : values to divide.      sample_b : values to divide with.   Returns: array with divided results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b power(sample_a, sample_b) power sample_a by sample_b and returns a new array.      sample_a : values to power.      sample_b : values to power with.   Returns: float array with power results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b remainder(sample_a, sample_b) Remainder sample_a by sample_b and returns a new array.      sample_a : values to remainder.      sample_b : values to remainder with.   Returns: array with remainder results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b equal(sample_a, sample_b) Check element wise sample_a equals sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b not_equal(sample_a, sample_b) Check element wise sample_a not equals sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b over_or_equal(sample_a, sample_b) Check element wise sample_a over or equals sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b under_or_equal(sample_a, sample_b) Check element wise sample_a under or equals sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b over(sample_a, sample_b) Check element wise sample_a over sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b under(sample_a, sample_b) Check element wise sample_a under sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b and_(sample_a, sample_b) Check element wise sample_a and sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b or_(sample_a, sample_b) Check element wise sample_a or sample_b and returns a new array.      sample_a : values to check.      sample_b : values to check.   Returns: int array with results. - sample_a provides type format for output. - arrays do not need to be symmetric. - sample_a must have same or more elements than sample_b all(sample) Check element wise if all numeric samples are true (!= 0).      sample : values to check.   Returns: int. any(sample) Check element wise if any numeric samples are true (!= 0).      sample : values to check.   Returns: int.
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por RicardoSantos
5
FunctionBlackScholesLibrary "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 time 0.      mu : float, growth rate.      sigma : float, volatility.      t1 : float, time to expiry date.      n : int, time steps to expiry date.   Returns: option values at each equal timed step (0 -> t1) binomial(s0, e, r, sigma, t1, m) Uses the binomial method for a European call.   Parameters:      s0 : float, asset price at time 0.      e : float, exercise price.      r : float, interest rate.      sigma : float, volatility.      t1 : float, time to expiry date.      m : int, time steps to expiry date.   Returns: option value at time 0. bsf(s0, t0, e, r, sigma, t1) Evaluates the Black-Scholes formula for a European call.   Parameters:      s0 : float, asset price at time 0.      t0 : float, time at which the price is known.      e : float, exercise price.      r : float, interest rate.      sigma : float, volatility.      t1 : float, time to expiry date.   Returns: option value at time 0. forward(e, r, sigma, t1, nx, nt, smax) Forward difference method to value a European call option.   Parameters:      e : float, exercise price.      r : float, interest rate.      sigma : float, volatility.      t1 : float, time to expiry date.      nx : int, number of space steps in interval (0, L).      nt : int, number of time steps.      smax : float, maximum value of S to consider.   Returns: option values for the european call, float array of size ((nx-1) * (nt+1)). mc(s0, e, r, sigma, t1, m) Uses Monte Carlo valuation on a European call.   Parameters:      s0 : float, asset price at time 0.      e : float, exercise price.      r : float, interest rate.      sigma : float, volatility.      t1 : float, time to expiry date.      m : int, time steps to expiry date.   Returns: confidence interval for the estimated range of valuation.
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por RicardoSantos
4
FunctionMinkowskiDistanceLibrary "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, point_ay, point_bx, point_by, p_value) Minkowsky Distance for single points.   Parameters:      point_ax : float, x value of point a.      point_ay : float, y value of point a.      point_bx : float, x value of point b.      point_by : float, y value of point b.      p_value : float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.   Returns: float ndim(point_x, point_y, p_value) Minkowsky Distance for N dimensions.   Parameters:      point_x : float array, point x dimension attributes.      point_y : float array, point y dimension attributes.      p_value : float, p value, default=1.0(1: manhatan, 2: euclidean), does not support chebychev.   Returns: float
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TimeLockedMALibrary "TimeLockedMA" Library & function(s) which generates a moving average that stays locked to users desired time preference. TODO - Add functionality for more moving average types. IE: smooth, weighted etc... Example: time_locked_ma(close, length=1, timeframe='days', type='ema') Will generate a 1 day exponential moving average that will stay consistent across all chart intervals. Error Handling On small time frames with large moving averages (IE: 1min chart with a 50 week moving average), you'll get a study error that says "(function "sma") references too many candles in history" . To fix this, make sure you have timeframe="" as an indicator() header. Next, in the indicator settings, increase the timeframe from to a higher interval until the error goes away. By default, it's set to "Chart". Bringing the interval up to 1hr will usually solve the issue. Furthermore, adding timeframe_gaps=false to your indicator() header will give you an approximation of real-time values. Misc Info For time_lock_ma() setting type='na' will return the relative length value that adjusts dynamically to user's chart time interval. This is good for plugging into other functions where a lookback or length is required. (IE: Bollinger Bands) time_locked_ma(source, length, timeframe, type) Creates a moving average that is locked to a desired timeframe   Parameters:      source : float, Moving average source      length : int, Moving average length      timeframe : string, Desired timeframe. Use: "minutes", "hours", "days", "weeks", "months", "chart"      type : string, string Moving average type. Use "SMA" (default) or "EMA". Value of "NA" will return relative lookback length.   Returns: moving average that is locked to desired timeframe. timeframe_convert(t, a, b) Converts timeframe to desired timeframe. From a --> b   Parameters:      t : int, Time interval      a : string, Time period      b : string, Time period to convert to   Returns: Converted timeframe value chart_time(timeframe_period, timeframe_multiplier) Separates timeframe.period function and returns chart interval and period   Parameters:      timeframe_period : string, timeframe.period      timeframe_multiplier : int, timeframe.multiplier Enjoy :)
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por JBroadway
2
FunctionGenerateRandomPointsInShapeLibrary "FunctionGenerateRandomPointsInShape" Generate random vector points in geometric shape (parallelogram, triangle) random_parallelogram(vector_a, vector_b) Generate random vector point in a parallelogram shape.   Parameters:      vector_a : float array, vector of (x, y) shape.      vector_b : float array, vector of (x, y) shape.   Returns: float array, vector of (x, y) shape. random_triangle(vector_a, vector_b) Generate random vector point in a triangle shape.   Parameters:      vector_a : float array, vector of (x, y) shape.      vector_b : float array, vector of (x, y) shape.   Returns: float array, vector of (x, y) shape.
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por RicardoSantos
1
FunctionNNLayerLibrary "FunctionNNLayer" Generalized Neural Network Layer method. function(inputs, weights, n_nodes, activation_function, bias, alpha, scale) Generalized Layer.   Parameters:      inputs : float array, input values.      weights : float array, weight values.      n_nodes : int, number of nodes in layer.      activation_function : string, default='sigmoid', name of the activation function used.      bias : float, default=1.0, bias to pass into activation function.      alpha : float, default=na, if required to pass into activation function.      scale : float, default=na, if required to pass into activation function.   Returns: float
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FunctionNNPerceptronLibrary "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 of the perceptron.      activation_function : string, default='sigmoid', activation function applied to the output.      alpha : float, default=na, if required for activation.      scale : float, default=na, if required for activation. @outputs float
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por RicardoSantos
1
InterpolationLibrary "Interpolation" Functions for interpolating values. Can be useful in signal processing or applied as a sigmoid function. linear(k, delta, offset, unbound) Returns the linear adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the line the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadIn(k, delta, offset, unbound) Returns the quadratic (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadOut(k, delta, offset, unbound) Returns the quadratic (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). quadInOut(k, delta, offset, unbound) Returns the quadratic (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicIn(k, delta, offset, unbound) Returns the cubic (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicOut(k, delta, offset, unbound) Returns the cubic (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). cubicInOut(k, delta, offset, unbound) Returns the cubic (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoIn(k, delta, offset, unbound) Returns the exponential (easing-in) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoOut(k, delta, offset, unbound) Returns the exponential (easing-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). expoInOut(k, delta, offset, unbound) Returns the exponential (easing-in-out) adjusted value.   Parameters:      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta). using(fn, k, delta, offset, unbound) Returns the adjusted value by function name.   Parameters:      fn : The name of the function. Allowed values: linear, quadIn, quadOut, quadInOut, cubicIn, cubicOut, cubicInOut, expoIn, expoOut, expoInOut.      k : A number (float) from 0 to 1 representing where the on the curve the value is.      delta : The amount the value should change as k reaches 1.      offset : The start value.      unbound : When true, k values less than 0 or greater than 1 are still calculated. When false (default), k values less than 0 will return the offset value and values greater than 1 will return (offset + delta).
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por Electrified
CRC.lib Bars - Bar FunctionsLibrary "CRCBars" min_max(open, open) Get bar min (low) and max (high) price points   Parameters:      open : Open price data      open : Close price data   Returns: is_bullish_bearish(open, open) Get bar bullish/bearish boolean signals   Parameters:      open : Open price data      open : Close price data   Returns: sizes(open, open, open, open) Get bar sizes based on open/high/low/close data   Parameters:      open : Open price data      open : High price data      open : Low price data      open : Close price data   Returns:
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por calmrat
options_expiration_and_strike_price_calculatorLibrary "options_expiration_and_strike_price_calculator" TODO: add library description here fun() this is a library to help calculate options strike price and expiration that you can add to a script i use it mainly for symbol calulation to place orders to buy options on TD ameritrade so it will be set up to order options on TD ameritrade using json order placer and webhook it fills in the area in the json under symbol i suggest manually adding the year it should look like this is an example of an order to buy 10 call options using json through td ameritrade api "complexOrderStrategyType": "NONE", "orderType": "LIMIT", "session": "NORMAL", "price": "6.45", "duration": "DAY", "orderStrategyType": "SINGLE", "orderLegCollection": }
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por natronix
4
GenericTALibrary "GenericTA" What is it? The real generic library. Which means it is just covering most built-in indicators / functions, but with more parameters, so the user don't have to write more few lines to achieve something simple and replicative. Development process? Will tidy it up, and setting up in later stage. Welcome to inbox me to improve the library ------ If you are finding a similar thing. That's a good news. Because I am making it.
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por BillionaireLau
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).
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por tbiktag
8
FunctionArrayReduceLibrary "FunctionArrayReduce" A limited method to reduce a array using a mathematical formula. float_(formula, samples, arguments, initial_value) Method to reduce a array using a mathematical formula.   Parameters:      formula : string, the mathematical formula, accepts some default name codes (index, output, previous, current, integer index of arguments array).      samples : float array, the data to process.      arguments : float array, the extra arguments of the formula.      initial_value : float, default=0, a initial base value for the process.   Returns: float. Notes: ** if initial value is specified as "na" it will default to the first element of samples.
Biblioteca Pine Script™
por RicardoSantos
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.
Biblioteca Pine Script™
por RicardoSantos
4
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))
Biblioteca Pine Script™
por tbiktag
6