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LibraryCOT█  OVERVIEW This library is a Pine programmer's tool that provides functions to access Commitment of Traders (​COT) data for futures. Four of our scripts use it:  • Commitment of Traders: Legacy Metrics  • Commitment of Traders: Disaggregated Metrics  • Commitment of Traders: Financial Metrics  • Commitment of Traders: Total If you do not program in Pine and want to use ​COT data, please see the indicators linked above. █  CONCEPTS Commitment of Traders ​(​COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) , a US federal agency that oversees the trading of derivative markets such as futures in the US. It is weekly data that provides traders with information about open interest for an asset. The CFTC oversees derivative markets traded on different exchanges, so ​COT data is available for assets that can be traded on CBOT, ​CME, NYMEX, COMEX, and ICEUS. Accessing ​COT data from a Pine script requires the generation of a ticker ID string for use with request.security() . The ticker string must be encoded in a special format that includes both CFTC and TradingView-specific content. The format of the ticker IDs is somewhat complex; this library's functions make their generation easier. Note that if you know the ​COT ticker ID string for specific data, you can enter it from the chart's "Symbol Search" dialog box. A ticker for ​COT data in Pine has the following structure: COT:__<_metricDirection><_metricType> where an underscore prefixing a component name inside <> is only included if the component is not a null string, and:        Is a digit representing the type of the COT report the data comes from: "" for legacy COT data, "2" for disaggregated data and "3" for financial data.        Is a six digit code that represents a commodity. Example: wheat futures (root "ZW") have the code "001602".        Is either "F" if the report data should exclude Options data, or "FO" if such data is included.        Is the TradingView code of the metric. This library's `metricNameAndDirectionToTicker()` function creates both     the and components of a ​COT ticker from the metric names and directions listed in the above chart.     The different metrics are explained in the CFTC's Explanatory Notes .        Is the direction of the metric: "Long", "Short", "Spreading" or "No direction".     Not all directions are applicable to all metrics. The valid ones are listed next to each metric in the above chart.        Is the type of the metric, possible values are "All", "Old" and "Other".     The difference between the types is explained in the "Old and Other Futures" section of the CFTC's Explanatory Notes . As an example, the Legacy report Open Interest data for ZW futures (options included) in the old standard has the ticker "COT:001602_FO_OI_OLD". The same data using the current standard without futures has the ticker "COT:001602_F_OI". █  USING THE LIBRARY The first functions in the library are helper functions that generate components of a ​COT ticker ID. The last function, `COTTickerid()`, is the one that generates the full ticker ID string by calling some of the helper functions. We use it like this in our example: exampleTicker = COTTickerid( COTType = "Legacy", CFTCCode = convertRootToCOTCode("Auto"), includeOptions = false, metricName = "Open Interest", metricDirection = "No direction", metricType = "All") This library's chart displays the valid values for the `metricName` and `metricDirection` arguments. They vary for each of the three types of ​COT data (the `COTType` argument). The chart also displays the ​COT ticker ID string in the `exampleTicker` variable. Look first. Then leap. The library's functions are: rootToCFTCCode(root)   Accepts a futures root and returns the relevant CFTC code.   Parameters:      root : Root prefix of the future's symbol, e.g. "ZC" for "ZC1!"" or "ZCU2021".   Returns: The part of a COT ticker corresponding to `root`, or "" if no CFTC code exists for the `root`. currencyToCFTCCode(curr)   Converts a currency string to its corresponding CFTC code.   Parameters:      curr : Currency code, e.g., "USD" for US Dollar.   Returns: The corresponding to the currency, if one exists. optionsToTicker(includeOptions)   Returns the part of a COT ticker using the `includeOptions` value supplied, which determines whether options data is to be included.   Parameters:      includeOptions : A "bool" value: 'true' if the symbol should include options and 'false' otherwise.   Returns: The part of a COT ticker: "FO" for data that includes options and "F" for data that doesn't. metricNameAndDirectionToTicker(metricName, metricDirection)   Returns a string corresponding to a metric name and direction, which is one component required to build a valid COT ticker ID.   Parameters:      metricName : One of the metric names listed in this library's chart. Invalid values will cause a runtime error.      metricDirection : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction".       Valid values vary with metrics. Invalid values will cause a runtime error.   Returns: The part of a COT ticker ID string, e.g., "OI_OLD" for "Open Interest" and "No direction",     or "TC_L" for "Traders Commercial" and "Long". typeToTicker(metricType)   Converts a metric type into one component required to build a valid COT ticker ID.   See the "Old and Other Futures" section of the CFTC's Explanatory Notes for details on types.   Parameters:      metricType : Metric type. Accepted values are: "All", "Old", "Other".   Returns: The part of a COT ticker. convertRootToCOTCode(mode, convertToCOT)   Depending on the `mode`, returns a CFTC code using the chart's symbol or its currency information when `convertToCOT = true`.   Otherwise, returns the symbol's root or currency information. If no COT data exists, a runtime error is generated.   Parameters:      mode : A string determining how the function will work. Valid values are:       "Root": the function extracts the futures symbol root (e.g. "ES" in "ESH2020") and looks for its CFTC code.       "Base currency": the function extracts the first currency in a pair (e.g. "EUR" in "EURUSD") and looks for its CFTC code.       "Currency": the function extracts the quote currency ("JPY" for "TSE:9984" or "USDJPY") and looks for its CFTC code.       "Auto": the function tries the first three modes (Root -> Base Currency -> Currency) until a match is found.      convertToCOT : "bool" value that, when `true`, causes the function to return a CFTC code.       Otherwise, the root or currency information is returned. Optional. The default is `true`.   Returns: If `convertToCOT` is `true`, the part of a COT ticker ID string.     If `convertToCOT` is `false`, the root or currency extracted from the current symbol. COTTickerid(COTType, CTFCCode, includeOptions, metricName, metricDirection, metricType)   Returns a valid TradingView ticker for the COT symbol with specified parameters.   Parameters:      COTType : A string with the type of the report requested with the ticker, one of the following: "Legacy", "Disaggregated", "Financial".      CTFCCode : The for the asset, e.g., wheat futures (root "ZW") have the code "001602".      includeOptions : A boolean value. 'true' if the symbol should include options and 'false' otherwise.      metricName : One of the metric names listed in this library's chart.      metricDirection : Direction of the metric, one of the following: "Long", "Short", "Spreading", "No direction".      metricType : Type of the metric. Possible values: "All", "Old", and "Other".   Returns: A ticker ID string usable with `request.security()` to fetch the specified Commitment of Traders data. █ AVAILABLE METRICS Different COT types provide different metrics. The table of all metrics available for each of the types can be found below. +------------------------------+------------------------+ | Legacy (​COT) Metric Names | Directions | +------------------------------+------------------------+ | Open Interest | No direction | | Noncommercial Positions | Long, Short, Spreading | | Commercial Positions | Long, Short | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No direction | | Traders Noncommercial | Long, Short, Spreading | | Traders Commercial | Long, Short | | Traders Total Reportable | Long, Short | | Concentration Gross ​LT 4 TDR | Long, Short | | Concentration Gross ​LT 8 TDR | Long, Short | | Concentration Net ​LT 4 TDR | Long, Short | | Concentration Net ​LT 8 TDR | Long, Short | +------------------------------+------------------------+ +-----------------------------------+------------------------+ | Disaggregated (COT2) Metric Names | Directions | +-----------------------------------+------------------------+ | Open Interest | No Direction | | Producer Merchant Positions | Long, Short | | Swap Positions | Long, Short, Spreading | | Managed Money Positions | Long, Short, Spreading | | Other Reportable Positions | Long, Short, Spreading | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No Direction | | Traders Producer Merchant | Long, Short | | Traders Swap | Long, Short, Spreading | | Traders Managed Money | Long, Short, Spreading | | Traders Other Reportable | Long, Short, Spreading | | Traders Total Reportable | Long, Short | | Concentration Gross LE 4 TDR | Long, Short | | Concentration Gross LE 8 TDR | Long, Short | | Concentration Net LE 4 TDR | Long, Short | | Concentration Net LE 8 TDR | Long, Short | +-----------------------------------+------------------------+ +-------------------------------+------------------------+ | Financial (COT3) Metric Names | Directions | +-------------------------------+------------------------+ | Open Interest | No Direction | | Dealer Positions | Long, Short, Spreading | | Asset Manager Positions | Long, Short, Spreading | | Leveraged Funds Positions | Long, Short, Spreading | | Other Reportable Positions | Long, Short, Spreading | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No Direction | | Traders Dealer | Long, Short, Spreading | | Traders Asset Manager | Long, Short, Spreading | | Traders Leveraged Funds | Long, Short, Spreading | | Traders Other Reportable | Long, Short, Spreading | | Traders Total Reportable | Long, Short | | Concentration Gross LE 4 TDR | Long, Short | | Concentration Gross LE 8 TDR | Long, Short | | Concentration Net LE 4 TDR | Long, Short | | Concentration Net LE 8 TDR | Long, Short | +-------------------------------+------------------------+
Pine Script® library
by TradingView
22
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.
Pine Script® library
by 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
Pine Script® library
by RicardoSantos
regressLibrary "regress" produces the slope (beta), y-intercept (alpha) and coefficient of determination for a linear regression regress(x, y, len) regress: computes alpha, beta, and r^2 for a linear regression of y on x   Parameters:      x : the explaining (independent) variable      y : the dependent variable      len : use the most recent "len" values of x and y   Returns: : alpha is the x-intercept, beta is the slope, an r2 is the coefficient of determination Note: the chart does not show anything, use the return values to compute model values in your own application, if you wish.
Pine Script® library
by taylor_o
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
Pine Script® library
by RicardoSantos
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
Pine Script® library
by RicardoSantos
1
MLActivationFunctionsLibrary "MLActivationFunctions" Activation functions for Neural networks. binary_step(value) Basic threshold output classifier to activate/deactivate neuron.   Parameters:      value : float, value to process.   Returns: float linear(value) Input is the same as output.   Parameters:      value : float, value to process.   Returns: float sigmoid(value) Sigmoid or logistic function.   Parameters:      value : float, value to process.   Returns: float sigmoid_derivative(value) Derivative of sigmoid function.   Parameters:      value : float, value to process.   Returns: float tanh(value) Hyperbolic tangent function.   Parameters:      value : float, value to process.   Returns: float tanh_derivative(value) Hyperbolic tangent function derivative.   Parameters:      value : float, value to process.   Returns: float relu(value) Rectified linear unit (RELU) function.   Parameters:      value : float, value to process.   Returns: float relu_derivative(value) RELU function derivative.   Parameters:      value : float, value to process.   Returns: float leaky_relu(value) Leaky RELU function.   Parameters:      value : float, value to process.   Returns: float leaky_relu_derivative(value) Leaky RELU function derivative.   Parameters:      value : float, value to process.   Returns: float relu6(value) RELU-6 function.   Parameters:      value : float, value to process.   Returns: float softmax(value) Softmax function.   Parameters:      value : float array, values to process.   Returns: float softplus(value) Softplus function.   Parameters:      value : float, value to process.   Returns: float softsign(value) Softsign function.   Parameters:      value : float, value to process.   Returns: float elu(value, alpha) Exponential Linear Unit (ELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.0, predefined constant, controls the value to which an ELU saturates for negative net inputs. .   Returns: float selu(value, alpha, scale) Scaled Exponential Linear Unit (SELU) function.   Parameters:      value : float, value to process.      alpha : float, default=1.67326324, predefined constant, controls the value to which an SELU saturates for negative net inputs. .      scale : float, default=1.05070098, predefined constant.   Returns: float exponential(value) Pointer to math.exp() function.   Parameters:      value : float, value to process.   Returns: float function(name, value, alpha, scale) Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float derivative(name, value, alpha, scale) Derivative Activation function.   Parameters:      name : string, name of activation function.      value : float, value to process.      alpha : float, default=na, if required.      scale : float, default=na, if required.   Returns: float
Pine Script® library
by RicardoSantos
2
MLLossFunctionsLibrary "MLLossFunctions" Methods for Loss functions. mse(expects, predicts) Mean Squared Error (MSE) " MSE = 1/N * sum ((y - y')^2) ".   Parameters:      expects : float array, expected values.      predicts : float array, prediction values.   Returns: float binary_cross_entropy(expects, predicts) Binary Cross-Entropy Loss (log).   Parameters:      expects : float array, expected values.      predicts : float array, prediction values.   Returns: float
Pine Script® library
by RicardoSantos
DivergenceLibrary "Divergence" Calculates a divergence between 2 series bullish(_src, _low, depth) Calculates bullish divergence   Parameters:      _src : Main series      _low : Comparison series (`low` is used if no argument is supplied)      depth : Fractal Depth (`2` is used if no argument is supplied)   Returns: 2 boolean values for regular and hidden divergence bearish(_src, _high, depth) Calculates bearish divergence   Parameters:      _src : Main series      _high : Comparison series (`high` is used if no argument is supplied)      depth : Fractal Depth (`2` is used if no argument is supplied)   Returns: 2 boolean values for regular and hidden divergence I created this library to plug and play divergences in any code. You can create a divergence indicator from any series you like. Fractals are used to pinpoint the edge of the series. The higher the depth, the slower the divergence updates get. My Plain Stochastic Divergence uses the same calculation. Watch it in action.
Pine Script® library
by DevLucem
9
FunctionPeakDetectionLibrary "FunctionPeakDetection" Method used for peak detection, similar to MATLAB peakdet method function(sample_x, sample_y, delta) Method for detecting peaks.   Parameters:      sample_x : float array, sample with indices.      sample_y : float array, sample with data.      delta : float, positive threshold value for detecting a peak.   Returns: tuple with found max/min peak indices.
Pine Script® library
by RicardoSantos
17
DailyDeviationLibrary "DailyDeviation" Helps in determining the relative deviation from the open of the day compared to the high or low values. hlcDeltaArrays(daysPrior, maxDeviation, spec, res) Retuns a set of arrays representing the daily deviation of price for a given number of days.   Parameters:      daysPrior : Number of days back to get the close from.      maxDeviation : Maximum deviation before a value is considered an outlier. A value of 0 will not filter results.      spec : session.regular (default), session.extended or other time spec.      res : The resolution (default = '1440').   Returns: Where OH = Open vs High, OL = Open vs Low, and OC = Open vs Close fromOpen(daysPrior, maxDeviation, comparison, spec, res) Retuns a value representing the deviation from the open (to the high or low) of the current day given number of days to measure from.   Parameters:      daysPrior : Number of days back to get the close from.      maxDeviation : Maximum deviation before a value is considered an outlier. A value of 0 will not filter results.      comparison : The value use in comparison to the current open for the day.      spec : session.regular (default), session.extended or other time spec.      res : The resolution (default = '1440').
Pine Script® library
by Electrified
VolatilityLibrary "Volatility" Functions for determining if volatility (true range) is within or exceeds normal. The "True Range" (ta.tr) is used for measuring volatility. Values are normalized by the volume adjusted weighted moving average (VAWMA) to be more like percent moves than price. current(len) Returns the current price adjusted volatitlity ratio.   Parameters:      len : Number of bars to get a volume adjusted weighted average price. normal(len, maxDeviation, level, gapDays, spec, res) Returns the normal upper range of volatility. Compensates for overnight gaps within a regular session.   Parameters:      len : Number of bars to measure volatility.      maxDeviation : The limit of volatility before considered an outlier.      level : The amount of standard deviation after cleaning outliers to be considered within normal.      gapDays : The number of days in the past to measure overnight gap volaility.      spec : session.regular (default), session.extended or other time spec.      res : The resolution (default = '1440'). isNormal(len, maxDeviation, level, gapDays, spec, res) Returns true if the volatility (true range) is within normal levels. Compensates for overnight gaps within a regular session.   Parameters:      len : Number of bars to measure volatility.      maxDeviation : The limit of volatility before considered an outlier.      level : The amount of standard deviation after cleaning outliers to be considered within normal.      gapDays : The number of days in the past to measure overnight gap volaility.      spec : session.regular (default), session.extended or other time spec.      res : The resolution (default = '1440'). severity(len, maxDeviation, level, gapDays, spec, res) Returns ratio of the current value to the normal value. Compensates for overnight gaps within a regular session.   Parameters:      len : Number of bars to measure volatility.      maxDeviation : The limit of volatility before considered an outlier.      level : The amount of standard deviation after cleaning outliers to be considered within normal.      gapDays : The number of days in the past to measure overnight gap volaility.      spec : session.regular (default), session.extended or other time spec.      res : The resolution (default = '1440').
Pine Script® library
by Electrified
DataCleanerLibrary "DataCleaner" Functions for acquiring outlier levels and acquiring a cleaned version of a series. outlierLevel(src, len, level) Gets the (standard deviation) outlier level for a given series.   Parameters:      src : The series to average and add a multiple of the standard deviation to.      len : The The number of bars to measure.      level : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary.   Returns: The average of the series plus the multiple of the standard deviation. cleanUsing(src, result, len, maxDeviation) Returns an array representing the result series with (outliers provided by the source) removed.   Parameters:      src : The source series to read from.      result : The result series.      len : The maximum size of the resultant array.      maxDeviation : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary.   Returns: An array containing the cleaned series. clean(src, len, maxDeviation) Returns an array representing the source series with outliers removed.   Parameters:      src : The source series to read from.      len : The maximum size of the resultant array.      maxDeviation : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary.   Returns: An array containing the cleaned series. outlierLevelAdjusted(src, level, len, maxDeviation) Gets the (standard deviation) outlier level for a given series after a single pass of removing any outliers.   Parameters:      src : The series to average and add a multiple of the standard deviation to.      level : The positive or negative multiple of the standard deviation to apply to the average. A positive number will be the upper boundary and a negative number will be the lower boundary.      len : The The number of bars to measure.      maxDeviation : The optional standard deviation level to use when cleaning the series. The default is the value of the provided level.   Returns: The average of the series plus the multiple of the standard deviation.
Pine Script® library
by Electrified
4
benchLibrary "bench" A simple banchmark library to analyse script performance and bottlenecks. Very useful if you are developing an overly complex application in Pine Script, or trying to optimise a library / function / algorithm... Supports artificial looping benchmarks (of fast functions) Supports integrated linear benchmarks (of expensive scripts) One important thing to note is that the Pine Script compiler will completely ignore any calculations that do not eventually produce chart output. Therefore, if you are performing an artificial benchmark you will need to use the bench.reference(value) function to ensure the calculations are executed. Please check the examples towards the bottom of the script. Quick Reference (Be warned this uses non-standard space characters to get the line indentation to work in the description!) ``` // Looping benchmark style benchmark = bench.new(samples = 500, loops = 5000) data = array.new_int() if bench.start(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.mark(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.mark(benchmark)   while bench.loop(benchmark)     array.unshift(data, timenow)   bench.stop(benchmark)   bench.reference(array.get(data, 0)) bench.report(benchmark, '1x array.unshift()') // Linear benchmark style benchmark = bench.new() data = array.new_int() bench.start(benchmark) for i = 0 to 1000   array.unshift(data, timenow) bench.mark(benchmark) for i = 0 to 1000   array.unshift(data, timenow) bench.stop(benchmark) bench.reference(array.get(data, 0)) bench.report(benchmark,'1000x array.unshift()') ``` Detailed Interface new(samples, loops) Initialises a new benchmark array   Parameters:      samples : int, the number of bars in which to collect samples      loops : int, the number of loops to execute within each sample   Returns: int , the benchmark array active(benchmark) Determing if the benchmarks state is active   Parameters:      benchmark : int , the benchmark array   Returns: bool, true only if the state is active start(benchmark) Start recording a benchmark from this point   Parameters:      benchmark : int , the benchmark array   Returns: bool, true only if the benchmark is unfinished loop(benchmark) Returns true until call count exceeds bench.new(loop) variable   Parameters:      benchmark : int , the benchmark array   Returns: bool, true while looping reference(number, string) Add a compiler reference to the chart so the calculations don't get optimised away   Parameters:      number : float, a numeric value to reference      string : string, a string value to reference mark(benchmark, number, string) Marks the end of one recorded interval and the start of the next   Parameters:      benchmark : int , the benchmark array      number : float, a numeric value to reference      string : string, a string value to reference stop(benchmark, number, string) Stop the benchmark, ending the final interval   Parameters:      benchmark : int , the benchmark array      number : float, a numeric value to reference      string : string, a string value to reference report(Prints, benchmark, title, text_size, position)   Parameters:      Prints : the benchmarks results to the screen      benchmark : int , the benchmark array      title : string, add a custom title to the report      text_size : string, the text size of the log console (global size vars)      position : string, the position of the log console (global position vars) unittest_bench(case) Cache module unit tests, for inclusion in parent script test suite. Usage: bench.unittest_bench(__ASSERTS)   Parameters:      case : string , the current test case and array of previous unit tests (__ASSERTS) unittest(verbose) Run the bench module unit tests as a stand alone. Usage: bench.unittest()   Parameters:      verbose : bool, optionally disable the full report to only display failures
Pine Script® library
by GeoffHammond
13
HurstExponentLibrary "HurstExponent" Library to calculate Hurst Exponent refactored from Hurst Exponent - Detrended Fluctuation Analysis demean(src) Calculates a series subtracted from the series mean.   Parameters:      src : The series used to calculate the difference from the mean (e.g. log returns).   Returns: The series subtracted from the series mean cumsum(src, length) Calculates a cumulated sum from the series.   Parameters:      src : The series used to calculate the cumulative sum (e.g. demeaned log returns).      length : The length used to calculate the cumulative sum (e.g. 100).   Returns: The cumulative sum of the series as an array aproximateLogScale(scale, length) Calculates an aproximated log scale. Used to save sample size   Parameters:      scale : The scale to aproximate.      length : The length used to aproximate the expected scale.   Returns: The aproximated log scale of the value rootMeanSum(cumulativeSum, barId, numberOfSegments) Calculates linear trend to determine error between linear trend and cumulative sum   Parameters:      cumulativeSum : The cumulative sum array to regress.      barId : The barId for the slice      numberOfSegments : The total number of segments used for the regression calculation   Returns: The error between linear trend and cumulative sum averageRootMeanSum(cumulativeSum, barId, length) Calculates the Root Mean Sum Measured for each block (e.g the aproximated log scale)   Parameters:      cumulativeSum : The cumulative sum array to regress and determine the average of.      barId : The barId for the slice      length : The length used for finding the average   Returns: The average root mean sum error of the cumulativeSum criticalValues(length) Calculates the critical values for a hurst exponent for a given length   Parameters:      length : The length used for finding the average   Returns: The critical value, upper critical value and lower critical value for a hurst exponent slope(cumulativeSum, length) Calculates the hurst exponent slope measured from root mean sum, scaled to log log plot using linear regression   Parameters:      cumulativeSum : The cumulative sum array to regress and determine the average of.      length : The length used for the hurst exponent sample size   Returns: The slope of the hurst exponent smooth(src, length) Smooths input using advanced linear regression   Parameters:      src : The series to smooth (e.g. hurst exponent slope)      length : The length used to smooth   Returns: The src smoothed according to the given length exponent(src, hurstLength) Wrapper function to calculate the hurst exponent slope   Parameters:      src : The series used for returns calculation (e.g. close)      hurstLength : The length used to calculate the hurst exponent (should be greater than 50)   Returns: The src smoothed according to the given length
Pine Script® library
by nolantait
4
MomentsLibrary "Moments" Based on Moments (Mean,Variance,Skewness,Kurtosis) . Rewritten for Pinescript v5. logReturns(src) Calculates log returns of a series (e.g log percentage change)   Parameters:      src : Source to use for the returns calculation (e.g. close).   Returns: Log percentage returns of a series mean(src, length) Calculates the mean of a series using ta.sma   Parameters:      src : Source to use for the mean calculation (e.g. close).      length : Length to use mean calculation (e.g. 14).   Returns: The sma of the source over the length provided. variance(src, length) Calculates the variance of a series   Parameters:      src : Source to use for the variance calculation (e.g. close).      length : Length to use for the variance calculation (e.g. 14).   Returns: The variance of the source over the length provided. standardDeviation(src, length) Calculates the standard deviation of a series   Parameters:      src : Source to use for the standard deviation calculation (e.g. close).      length : Length to use for the standard deviation calculation (e.g. 14).   Returns: The standard deviation of the source over the length provided. skewness(src, length) Calculates the skewness of a series   Parameters:      src : Source to use for the skewness calculation (e.g. close).      length : Length to use for the skewness calculation (e.g. 14).   Returns: The skewness of the source over the length provided. kurtosis(src, length) Calculates the kurtosis of a series   Parameters:      src : Source to use for the kurtosis calculation (e.g. close).      length : Length to use for the kurtosis calculation (e.g. 14).   Returns: The kurtosis of the source over the length provided. skewnessStandardError(sampleSize) Estimates the standard error of skewness based on sample size   Parameters:      sampleSize : The number of samples used for calculating standard error.   Returns: The standard error estimate for skewness based on the sample size provided. kurtosisStandardError(sampleSize) Estimates the standard error of kurtosis based on sample size   Parameters:      sampleSize : The number of samples used for calculating standard error.   Returns: The standard error estimate for kurtosis based on the sample size provided. skewnessCriticalValue(sampleSize) Estimates the critical value of skewness based on sample size   Parameters:      sampleSize : The number of samples used for calculating critical value.   Returns: The critical value estimate for skewness based on the sample size provided. kurtosisCriticalValue(sampleSize) Estimates the critical value of kurtosis based on sample size   Parameters:      sampleSize : The number of samples used for calculating critical value.   Returns: The critical value estimate for kurtosis based on the sample size provided.
Pine Script® library
by nolantait
pNRTRLibrary "pNRTR" Provides functions for calculating Nick Rypock Trailing Reverse (NRTR) trend values with higher precision offsets for both low, and high points rather than the standard single offset. pnrtr(float low_offset = 0.2, float high_offset = 0.2, float value = close) low_offset Offset used for nrtr low_point calculations. Default is 0.2. high_offset Offset used for nrtr high_point calculations. Default is 0.2. value Variable used for nrtr point calculations. Default is close.
Pine Script® library
by birdgineer
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
Pine Script® library
by GeoffHammond
3
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).
Pine Script® library
by tbtkg
8
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.
Pine Script® library
by 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))
Pine Script® library
by tbtkg
6
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.
Pine Script® library
by RicardoSantos
1