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LibIndicadoresUteisLibrary "LibIndicadoresUteis" Collection of useful indicators. This collection does not do any type of plotting on the graph, as the methods implemented can and should be used to get the return of mathematical formulas, in a way that speeds up the development of new scripts. The current version contains methods for stochastic return, slow stochastic, IFR, leverage calculation for B3 futures market, leverage calculation for B3 stock market, bollinger bands and the range of change. estocastico(PeriodoEstocastico)   Returns the value of stochastic   Parameters:      PeriodoEstocastico : Period for calculation basis   Returns: Float with the stochastic value of the period estocasticoLento(PeriodoEstocastico, PeriodoMedia)   Returns the value of slow stochastic   Parameters:      PeriodoEstocastico : Stochastic period for calculation basis      PeriodoMedia : Average period for calculation basis   Returns: Float with the value of the slow stochastic of the period ifrInvenenado(PeriodoIFR, OrigemIFR)   Returns the value of the RSI/IFR Poisoned of Guima   Parameters:      PeriodoIFR : RSI/IFR period for calculation basis      OrigemIFR : Source of RSI/IFR for calculation basis   Returns: Float with the RSI/IFR value for the period calculoAlavancagemFuturos(margem, alavancagemMaxima)   Returns the number of contracts to work based on margin   Parameters:      margem : Margin for contract unit      alavancagemMaxima : Maximum number of contracts to work   Returns: Integer with the number of contracts suggested for trading calculoAlavancagemAcoes(alavancagemMaxima)   Returns the number of batches to work based on the margin   Parameters:      alavancagemMaxima : Maximum number of batches to work   Returns: Integer with the amount of lots suggested for trading bandasBollinger(periodoBB, origemBB, desvioPadrao)   Returns the value of bollinger bands   Parameters:      periodoBB : Period of bollinger bands for calculation basis      origemBB : Origin of bollinger bands for calculation basis      desvioPadrao : Standard Deviation of bollinger bands for calculation basis   Returns: Two-position array with upper and lower band values ​​respectively theRoc(periodoROC, origemROC)   Returns the value of Rate Of Change   Parameters:      periodoROC : Period for calculation basis      origemROC : Source of calculation basis   Returns: Float with the value of Rate Of Change
Pine Script®腳本庫
由Fabricio_Guima提供
BpaLibrary "Bpa" TODO: library of Brooks Price Action concepts isBreakoutBar(atr, high, low, close, open, tail, size)   TODO: check if the bar is a breakout based on the specified conditions   Parameters:      atr : TODO: atr value      high : TODO: high price      low : TODO: low price      close : TODO: close price      open : TODO: open price      tail : TODO: decimal value for a percent that represent the size of the tail of the bar that cant be preceeded to be considered strong close      size : TODO: decimal value for a percent that represents by how much the breakout bar should be bigger than others to be considered one   Returns: TODO: boolean value, true if breakout bar, false otherwise
Pine Script®腳本庫
由yohtza提供
11
LibraryCOTLibrary "LibraryCOT" This library provides tools to help Pine programmers fetch Commitment of Traders (​COT) data for futures. 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.
Pine Script®腳本庫
由ILuvMarkets提供
1
TradingWolfLibaryLibrary "TradingWolfLibary" getMA(int, string)   Gets a Moving Average based on type   Parameters:      int : length The MA period      string : maType The type of MA   Returns: A moving average with the given parameters minStop(float, simple, float, string)   Calculates and returns Minimum stop loss   Parameters:      float : entry price (Close if calculating on the entry candle)      simple : int Calculate how many bars back to look at swings      float : Minimum Stop Loss allowed (Should be x 0.01) if input      string : Direciton of trade either "Long" or "Short"   Returns: Stop Loss Value
Pine Script®腳本庫
由TradingWolf提供
KernelFunctionsLibrary "KernelFunctions" This library provides non-repainting kernel functions for Nadaraya-Watson estimator implementations. This allows for easy substitution/comparison of different kernel functions for one another in indicators. Furthermore, kernels can easily be combined with other kernels to create newer, more customized kernels. Compared to Moving Averages (which are really just simple kernels themselves), these kernel functions are more adaptive and afford the user an unprecedented degree of customization and flexibility. rationalQuadratic(_src, _lookback, _relativeWeight, _startAtBar)   Rational Quadratic Kernel - An infinite sum of Gaussian Kernels of different length scales.   Parameters:      _src : The source series.      _lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.      _relativeWeight : Relative weighting of time frames. Smaller values result in a more stretched-out curve, and larger values will result in a more wiggly curve. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel.      _startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.   Returns: yhat The estimated values according to the Rational Quadratic Kernel. gaussian(_src, _lookback, _startAtBar)   Gaussian Kernel - A weighted average of the source series. The weights are determined by the Radial Basis Function (RBF).   Parameters:      _src : The source series.      _lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.      _startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.   Returns: yhat The estimated values according to the Gaussian Kernel. periodic(_src, _lookback, _period, _startAtBar)   Periodic Kernel - The periodic kernel (derived by David Mackay) allows one to model functions that repeat themselves exactly.   Parameters:      _src : The source series.      _lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.      _period : The distance between repititions of the function.      _startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.   Returns: yhat The estimated values according to the Periodic Kernel. locallyPeriodic(_src, _lookback, _period, _startAtBar)   Locally Periodic Kernel - The locally periodic kernel is a periodic function that slowly varies with time. It is the product of the Periodic Kernel and the Gaussian Kernel.   Parameters:      _src : The source series.      _lookback : The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars.      _period : The distance between repititions of the function.      _startAtBar : Bar index on which to start regression. The first bars of a chart are often highly volatile, and omitting these initial bars often leads to a better overall fit.   Returns: yhat The estimated values according to the Locally Periodic Kernel.
Pine Script®腳本庫
由jdehorty提供
11
ahpuhelperLibrary "ahpuhelper" Helper Library for Auto Harmonic Patterns UltimateX. It is not meaningful for others. This is supposed to be private library. But, publishing it to make sure that I don't delete accidentally. Some functions may be useful for coders. insert_open_trades_table_column(showOpenTrades, table_id, column, colors, values, intStatus, harmonicTrailingStartState, lblSizeOpenTrades)   add data to open trades table column   Parameters:      showOpenTrades : flag to show open trades table      table_id : Table Id      column : refers to pattern data      colors : backgroud and text color array      values : cell values      intStatus : status as integer      harmonicTrailingStartState : trailing Start state as per configs      lblSizeOpenTrades : text size   Returns: nextColumn populate_closed_stats(ClosedStatsPosition, bullishCounts, bearishCounts, bullishRetouchCounts, bearishRetouchCounts, bullishSizeMatrix, bearishSizeMatrix, bullishRR, bearishRR, allPatternLabels, flags, rowMain, rowHeaders)   populate closed stats for harmonic patterns   Parameters:      ClosedStatsPosition : Table position for closed stats      bullishCounts : Matrix containing bullish trade stats      bearishCounts : Matrix containing bearish trade stats      bullishRetouchCounts : Matrix containing bullish trade stats for those which retouched entry      bearishRetouchCounts : Matrix containing bearish trade stats for those which retouched entry      bullishSizeMatrix : Matrix containing data about size of bullish patterns      bearishSizeMatrix : Matrix containing data about size of bearish patterns      bullishRR : Matrix containing Risk Reward data of bullish patterns      bearishRR : Matrix containing Risk Reward data of bearish patterns      allPatternLabels : array containing pattern labels      flags : display flags      rowMain : Pattern header data      rowHeaders : header grouping data   Returns: void get_rr_details(patternTradeDetails, harmonicTrailingStartState, disableTrail, breakEvenTrail)   calculate and return risk reward based on targets and stops   Parameters:      patternTradeDetails : array containing stop, entry and targets      harmonicTrailingStartState : trailing point      disableTrail : If set, ignores trailing point      breakEvenTrail : If set, trailing does not go beyond breakeven.   Returns: nextColumn
Pine Script®腳本庫
由Trendoscope提供
2
normsinvLibrary "normsinv" Description: Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one; i.e., normsinv seeks that value z such that a normal distribtuion of mean of zero and standard deviation one is equal to the input probability. Reference: github.com normsinv(y0)   Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one.   Parameters:      y0 : float, probability corresponding to the normal distribution.   Returns: float, z-score
Pine Script®腳本庫
由loxx提供
4
Strategy PnL LibraryLibrary "Strategy_PnL_Library" TODO: This is a library that helps you learn current pnl of open position and use it to create your own dynamic take profit or stop loss rules based on current level of your profit. It should only be used with strategies. inTrade()   inTrade: Checks if a position is currently open.   Returns: bool: true for yes, false for no. notInTrade()   inTrade: Checks if a position is currently open. Interchangeable with inTrade but just here for simple semantics.   Returns: bool: true for yes, false for no. pnl()   pnl: Calculates current profit or loss of position after the commission. If the strategy is not in trade it will always return na.   Returns: float: Current Profit or Loss of position, positive values for profit, negative values for loss. entryBars()   entryBars: Checks how many bars it's been since the entry of the position.   Returns: int: Returns a int of strategy entry bars back. Minimum value is always corrected to 1 to avoid lookback errors. pnlvelocity()   pnlvelocity: Calculates the velocity of pnl by following the change in open profit compared to previous bar. If the strategy is not in trade it will always return na.   Returns: float: Returns a float value of pnl velocity. pnlacc()   pnlacc: Calculates the acceleration of pnl by following the change in profit velocity compared to previous bar. If the strategy is not in trade it will always return na.   Returns: float: Returns a float value of pnl acceleration. pnljerk()   pnljerk: Calculates the jerk of pnl by following the change in profit acceleration compared to previous bar. If the strategy is not in trade it will always return na.   Returns: float: Returns a float value of pnl jerk. pnlhigh()   pnlhigh: Calculates the highest value the pnl has reached since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float highest value the pnl has reached. pnllow()   pnllow: Calculates the lowest value the pnl has reached since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float lowest value the pnl has reached. pnldev()   pnldev: Calculates the deviance of the pnl since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float deviance value of the pnl. pnlvar()   pnlvar: Calculates the variance value of the pnl since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float variance value of the pnl. pnlstdev()   pnlstdev: Calculates the stdev value of the pnl since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float stdev value of the pnl. pnlmedian()   pnlmedian: Calculates the median value of the pnl since the start of the current position. If the strategy is not in trade it will always return na.   Returns: float: Returns a float median value of the pnl.
Pine Script®腳本庫
由serkany88提供
2
ctndLibrary "ctnd" Description: Double precision algorithm to compute the cumulative trivariate normal distribution found in A.Genz, Numerical computation of rectangular bivariate and trivariate normal and t probabilities”, Statistics and Computing, 14, (3), 2004. The cumulative trivariate normal is needed to price window barrier options, see G.F. Armstrong, Valuation formulae or window barrier options”, Applied Mathematical Finance, 8, 2001. References: link.springer.com www.tandfonline.com citeseerx.ist.psu.edu The Complete Guide to Option Pricing Formulas, 2nd ed. (Espen Gaarder Haug) CTND(LIMIT1, LIMIT2, LIMIT3, SIGMA1, SIGMA2, SIGMA3)   Returns the Cumulative Trivariate Normal Distribution   Parameters:      LIMIT1 : float,      LIMIT2 : float,      LIMIT3 : float,      SIGMA1 : float,      SIGMA2 : float,      SIGMA3 : float,   Returns: float.
Pine Script®腳本庫
由loxx提供
combinLibrary "combin" Description: The combin function is a the combination function as it calculates the number of possible combinations for two given numbers. This function takes two arguments: the number and the number_chosen. For example, if the number is 5 and the number chosen is 1, there are 5 combinations, giving 5 as a result. Reference: ideone.com support.microsoft.com combin(n, kin)   Returns the number of combinations for a given number of items. Use to determine the total possible number of groups for a given number of items.   Parameters:      n : int, The number of items.      kin : int, The number of items in each combination.   Returns: int.
Pine Script®腳本庫
由loxx提供
1
norminvLibrary "norminv" Description: An inverse normal distribution is a way to work backwards from a known probability to find an x-value. It is an informal term and doesn't refer to a particular probability distribution. Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation. Reference: github.com support.microsoft.com norminv(x, mean, stdev)   Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation.   Parameters:      x : float, The input to the normal distribution function.      mean : float, The mean (mu) of the normal distribution function      stdev : float, The standard deviation (sigma) of the normal distribution function.   Returns: float.
Pine Script®腳本庫
由loxx提供
cbndLibrary "cbnd" Description: A standalone Cumulative Bivariate Normal Distribution (CBND) functions that do not require any external libraries. This includes 3 different CBND calculations: Drezner(1978), Drezner and Wesolowsky (1990), and Genz (2004) Comments: The standardized cumulative normal distribution function returns the probability that one random variable is less than a and that a second random variable is less than b when the correlation between the two variables is p. Since no closed-form solution exists for the bivariate cumulative normal distribution, we present three approximations. The first one is the well-known Drezner (1978) algorithm. The second one is the more efficient Drezner and Wesolowsky (1990) algorithm. The third is the Genz (2004) algorithm, which is the most accurate one and therefore our recommended algorithm. West (2005b) and Agca and Chance (2003) discuss the speed and accuracy of bivariate normal distribution approximations for use in option pricing in ore detail. Reference: The Complete Guide to Option Pricing Formulas, 2nd ed. (Espen Gaarder Haug) CBND1(A, b, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Drezner 1978 Algorithm   Parameters:      A : float,      b : float,      rho : float,   Returns: float. CBND2(A, b, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Drezner and Wesolowsky (1990) function   Parameters:      A : float,      b : float,      rho : float,   Returns: float. CBND3(x, y, rho)   Returns the Cumulative Bivariate Normal Distribution (CBND) using Genz (2004) algorithm (this is the preferred method)   Parameters:      x : float,      y : float,      rho : float,   Returns: float.
Pine Script®腳本庫
由loxx提供
cndLibrary "cnd" Cumulative Normal Distribution CND1(x)   Returns the Cumulative Normal Distribution (CND) using the Hart (1968) method. (preferred method, 14-18 decimal accuracy)   Parameters:      x : float,   Returns: float. CND2(x)   Returns the Cumulative Normal Distribution (CND) using the Abromowitz and Stegun (1974) Polynomial Approximation.   Parameters:      x : float,   Returns: float. CND3(x)   Returns the Cumulative Normal Distribution (CND) using Newton-Cotes method, Boole’s rule   Parameters:      x : float,   Returns: float.
Pine Script®腳本庫
由loxx提供
1
TALibrary "TA" General technical analysis functions div_bull(pS, iS, cp_length_after, cp_length_before, pivot_length, lookback, no_broken, pW, iW, hidW, regW)   Test for bullish divergence   Parameters:      pS : Price series (float)      iS : Indicator series (float)      cp_length_after : Bars after current (divergent) pivot low to be considered a valid pivot (optional int)      cp_length_before : Bars before current (divergent) pivot low to be considered a valid pivot (optional int)      pivot_length : Bars before and after prior pivot low to be considered valid pivot (optional int)      lookback : Bars back to search for prior pivot low (optional int)      no_broken : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)      pW : Weight of change in price, used in degree of divergence calculation (optional float)      iW : Weight of change in indicator, used in degree of divergence calculation (optional float)      hidW : Weight of hidden divergence, used in degree of divergence calculation (optional float)      regW : Weight of regular divergence, used in degree of divergence calculation (optional float)   Returns: flag = true if divergence exists (bool) degree = degree (strength) of divergence (float) type = 1 = regular, 2 = hidden (int) lx1 = x coordinate 1 (int) ly1 = y coordinate 1 (float) lx2 = x coordinate 2 (int) ly2 = y coordinate 2 (float) div_bear(pS, iS, cp_length_after, cp_length_before, pivot_length, lookback, no_broken, pW, iW, hidW, regW)   Test for bearish divergence   Parameters:      pS : Price series (float)      iS : Indicator series (float)      cp_length_after : Bars after current (divergent) pivot high to be considered a valid pivot (optional int)      cp_length_before : Bars before current (divergent) pivot highto be considered a valid pivot (optional int)      pivot_length : Bars before and after prior pivot high to be considered valid pivot (optional int)      lookback : Bars back to search for prior pivot high (optional int)      no_broken : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)      pW : Weight of change in price, used in degree of divergence calculation (optional float)      iW : Weight of change in indicator, used in degree of divergence calculation (optional float)      hidW : Weight of hidden divergence, used in degree of divergence calculation (optional float)      regW : Weight of regular divergence, used in degree of divergence calculation (optional float)   Returns: flag = true if divergence exists (bool) degree = degree (strength) of divergence (float) type = 1 = regular, 2 = hidden (int) lx1 = x coordinate 1 (int) ly1 = y coordinate 1 (float) lx2 = x coordinate 2 (int) ly2 = y coordinate 2 (float)
Pine Script®腳本庫
由reees提供
3
AllTimeHighLowLibrary "AllTimeHighLow" Provides functions calculating the all-time high/low of values. hi(val)   Calculates the all-time high of a series.   Parameters:      val : Series to use (`high` is used if no argument is supplied).   Returns: The all-time high for the series. lo(val)   Calculates the all-time low of a series.   Parameters:      val : Series to use (`low` is used if no argument is supplied).   Returns: The all-time low for the series.
Pine Script®腳本庫
由mehmegunes提供
FunctionPatternFrequencyLibrary "FunctionPatternFrequency" Counts the word or integer number pattern frequency on a array. reference: rosettacode.org count(pattern)   counts the number a pattern is repeated.   Parameters:      pattern : : array : array with patterns to be counted.   Returns:     array : list of unique patterns.     array : list of counters per pattern.   usage:     count(array.from('a','b','c','a','b','a')) count(pattern)   counts the number a pattern is repeated.   Parameters:      pattern : : array : array with patterns to be counted.   Returns:     array : list of unique patterns.     array : list of counters per pattern.   usage:     count(array.from(1,2,3,1,2,1))
Pine Script®腳本庫
由RicardoSantos提供
DatasetWeatherTokyoMeanAirTemperatureLibrary "DatasetWeatherTokyoMeanAirTemperature" Provides a data set of the monthly mean air temperature (°C) for the city of Tokyo in Japan. this was just for fun, no financial implications in this. reference: www.data.jma.go.jp TOKYO WMO Station ID:47662 Lat 35o41.5'N Lon 139o45.0'E year_()   the years of the data set.   Returns: array : year values. january()   the january values of the dataset   Returns: array\ : data values for january. february()   the february values of the dataset   Returns: array\ : data values for february. march()   the march values of the dataset   Returns: array\ : data values for march. april()   the april values of the dataset   Returns: array\ : data values for april. may()   the may values of the dataset   Returns: array\ : data values for may. june()   the june values of the dataset   Returns: array\ : data values for june. july()   the july values of the dataset   Returns: array\ : data values for july. august()   the august values of the dataset   Returns: array\ : data values for august. september()   the september values of the dataset   Returns: array\ : data values for september. october()   the october values of the dataset   Returns: array\ : data values for october. november()   the november values of the dataset   Returns: array\ : data values for november. december()   the december values of the dataset   Returns: array\ : data values for december. annual()   the annual values of the dataset   Returns: array\ : data values for annual. select_month(idx)   get the temperature values for a specific month.   Parameters:      idx : int, month index (1 -> 12 | any other value returns annual average values).   Returns: array\ : data values for selected month. select_value(year_, month_)   get the temperature value of a specified year and month.   Parameters:      year_ : int, year value.      month_ : int, month index (1 -> 12 | any other value returns annual average values).   Returns: float : value of specified year and month. diff_to_median(month_)   the difference of the month air temperature (ºC) to the median of the sample.   Parameters:      month_ : int, month index (1 -> 12 | any other value returns annual average values).   Returns: float : difference of current month to median in (Cº)
Pine Script®腳本庫
由RicardoSantos提供
7
FunctionDynamicTimeWarpingLibrary "FunctionDynamicTimeWarping" "In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed. For instance, similarities in walking could be detected using DTW, even if one person was walking faster than the other, or if there were accelerations and decelerations during the course of an observation. DTW has been applied to temporal sequences of video, audio, and graphics data — indeed, any data that can be turned into a linear sequence can be analyzed with DTW. A well-known application has been automatic speech recognition, to cope with different speaking speeds. Other applications include speaker recognition and online signature recognition. It can also be used in partial shape matching applications." "Dynamic time warping is used in finance and econometrics to assess the quality of the prediction versus real-world data." ~~ wikipedia reference: en.wikipedia.org towardsdatascience.com github.com cost_matrix(a, b, w)   Dynamic Time Warping procedure.   Parameters:      a : array, data series.      b : array, data series.      w : int , minimum window size.   Returns: matrix optimum match matrix. traceback(M)   perform a backtrace on the cost matrix and retrieve optimal paths and cost between arrays.   Parameters:      M : matrix, cost matrix.   Returns: tuple: array aligned 1st array of indices. array aligned 2nd array of indices. float final cost. reference: github.com report(a, b, w)   report ordered arrays, cost and cost matrix.   Parameters:      a : array, data series.      b : array, data series.      w : int , minimum window size.   Returns: string report.
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