Empiece

MATH

na_skip_highestLibrary "na_skip_highest" Finds the highest historic value over len bars but skip na valued bars (eg, off days). In other words, this will ensure we find the highest value over len bars with a real value, and if there are any na bars in-between, we skip over but the loop will continue. This allows to mimic calculations on markets with off days (eg, weekends). na_skip_highest(src, len)   Finds the highest historic value over len bars but skip na valued bars (eg, off days). In other words, this will ensure we find the highest value over len bars with a real value, and if there are any na bars in-between, we skip over but the loop will continue. This allows to mimic calculations on markets with off days (eg, weekends).   Parameters:      src : series float source (eg, close)      len : int length, number of recent bars to consider in the window to find the highest value   Returns: highest float highest value found over the len window
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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.
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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
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2
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.
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1
FibonacciLibrary "Fibonacci" General Fibonacci functions. Get fib numbers, ratios, etc. fib_precise(f, precision)   Get the precise Fibonacci ratio, to the specified number of decimal places   Parameters:      f : Fibonacci ratio (string, in form #.###)      precision : Number of decimal places (optional int, dft = 16, max = 32)   Returns: Precise Fibonacci ratio (float) fib_n(n)   Calculate the Nth number in the Fibonacci sequence   Parameters:      n : Index/number in sequence (int)   Returns: Fibonacci number (int)
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2
TrigLibrary "Trig" Trigonometric functions rt_get_angleAlpha(a, b, c, deg)   Get angle α of a right triangle, given the lengths of its sides   Parameters:      a : length of leg a (float)      b : length of leg b (float)      c : length of hypotenuse (float)      deg : flag to return angle in degrees (bool - default = false)   Returns: angle α in radians (or degrees if deg == true) rt_get_angleAlphaFromLine(x1, y1, x2, y2, l, deg)   Get angle α of a right triangle formed by the given line   Parameters:      x1 : x coordinate 1 (int - optional, required if argument l is not specified)      y1 : y coordinate 1 (float - optional, required if argument l is not specified)      x2 : x coordinate 2 (int - optional, required if argument l is not specified)      y2 : y coordinate 2 (float - optional, required if argument l is not specified)      l : line object (line - optional, required if x1, y1, x2, and y2 agruments are not specified)      deg : flag to return angle in degrees (bool - default = false)   Returns: angle α in radians (or degrees if deg == true) rt_get_angleBeta(a, b, c, deg)   Get angle β of a right triangle, given the lengths of its sides   Parameters:      a : length of leg a (float)      b : length of leg b (float)      c : length of hypotenuse (float)      deg : flag to return angle in degrees (bool - default = false)   Returns: angle β in radians (or degrees if deg == true) rt_get_angleBetaFromLine(x1, y1, x2, y2, l, deg)   Get angle β of a right triangle formed by the given line   Parameters:      x1 : x coordinate 1 (int - optional, required if argument l is not specified)      y1 : y coordinate 1 (float - optional, required if argument l is not specified)      x2 : x coordinate 2 (int - optional, required if argument l is not specified)      y2 : y coordinate 2 (float - optional, required if argument l is not specified)      l : line object (line - optional, required if x1, y1, x2, and y2 agruments are not specified)      deg : flag to return angle in degrees (bool - default = false)   Returns: angle β in radians (or degrees if deg == true)
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AlgebraLibrary "Algebra" Algebra functions. line_fromXy(x1, y1, x2, y2)   Get line slope and y-intercept from coordinates   Parameters:      x1 : x coordinate 1 (int - bar index)      y1 : y coordinate 1 (float - price/value)      x2 : x coordinate 2 (int - bar index)      y2 : y coordinate 2 (float - price/value)   Returns: of line line_getPrice(x, slope, yInt)   Get line slope and y-intercept from coordinates   Parameters:      x : x coordinate to solve for y (int - bar index)      slope : slope of line (float)      yInt : y-intercept of line (float)   Returns: y (price/value)
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1
FunctionLAPACKdsyrkLibrary "FunctionLAPACKdsyrk" subroutine part of LAPACK: Linear Algebra Package, performs one of the symmetric rank k operations . C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, . where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. . reference: netlib.org dsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc)   performs one of the symmetric rank k operations . C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, . where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. .   Parameters:      uplo : string specifies whether the upper or lower triangular part of the array C is to be referenced as follows: UPLO = 'U' or 'u' Only the upper triangular part of C is to be referenced. UPLO = 'L' or 'l' Only the lower triangular part of C is to be referenced. .      trans : string specifies the operation to be performed as follows: TRANS = 'N' or 'n' C := alpha*A*A**T + beta*C. TRANS = 'T' or 't' C := alpha*A**T*A + beta*C. TRANS = 'C' or 'c' C := alpha*A**T*A + beta*C. .      n : int specifies the order of the matrix C. N must be at least zero.      k : int On entry with: TRANS = 'N' or 'n', K specifies the number of columns of the matrix A. TRANS = 'T' or 't' or 'C' or 'c', K specifies the number of rows of the matrix A. K must be at least zero. .      alpha : float scalar.      a : matrix matrix A.      lda : int specifies the first dimension of A.      beta : float scalar.      c : matrix matrix C, is overwritten by the lower triangular part of the updated matrix.      ldc : int specifies the first dimension of C   Returns: void, C is overwritten by the lower triangular part of the updated matrix.
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FunctionLAPACKdtrsmLibrary "FunctionLAPACKdtrsm" subroutine in the LAPACK:linear algebra package, used to solve one of the following matrix equations: op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. reference: netlib.org dtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb)   solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B.   Parameters:      side : string , On entry, SIDE specifies whether op( A ) appears on the left or right of X as follows: SIDE = 'L' or 'l' op( A )*X = alpha*B. SIDE = 'R' or 'r' X*op( A ) = alpha*B.      uplo : string , specifies whether the matrix A is an upper or lower triangular matrix as follows: UPLO = 'U' or 'u' A is an upper triangular matrix. UPLO = 'L' or 'l' A is a lower triangular matrix.      transa : string , specifies the form of op( A ) to be used in the matrix multiplication as follows: TRANSA = 'N' or 'n' op( A ) = A. TRANSA = 'T' or 't' op( A ) = A**T. TRANSA = 'C' or 'c' op( A ) = A**T.      diag : string , specifies whether or not A is unit triangular as follows: DIAG = 'U' or 'u' A is assumed to be unit triangular. DIAG = 'N' or 'n' A is not assumed to be unit triangular.      m : int , the number of rows of B. M must be at least zero.      n : int , the number of columns of B. N must be at least zero.      alpha : float , specifies the scalar alpha. When alpha is zero then A is not referenced and B need not be set before entry.      a : matrix, Triangular matrix.      lda : int , specifies the first dimension of A.      b : matrix, right-hand side matrix B, and on exit is overwritten by the solution matrix X.      ldb : int , specifies the first dimension of B.   Returns: void, modifies matrix b. usage: dtrsm ('L', 'U', 'N', 'N', 5, 3, 1.0, a, 7, b, 6)
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BoxLine_LibLibrary "BoxLine_Lib" personal Library for line and box built in functions lineXY(x)    get x1,y1,x2,y2 in a tuple   Parameters:      x : TODO: line   Returns: tuple of x1,y1,x2,y2 line(x)   Create line with only the y1 value(when line == na) or all when line != na set x1,y1,x2,y2 individually just 1 or all - use just the line value to set the x2 to current bar or time will set to time - will auto pick xloc.bar_index or xloc.bar_time if not used   Parameters:      x : (line line,int x1,float y1,int x2,float y2, string xloc,string extend,color color,string style,int width)   Returns: Line boxXY(x)    get left,top,right,bottom in a tuple   Parameters:      x : box   Returns: tuple of left,top,right,bottom box(x)   Create line with only the top,bottom value(when line == na) or all when box != na set left,top,right,bottom individually just 1 or all - use just the box value to set the right to current bar or time will set to time - if right is above a number that a bar_index wouldnt be   Parameters:      x : box box,int left,float top,int right, float bottom,color border_color, int border_width, string border_style,string extend,string xloc, color bgcolor,string text,string text_size, color text_color, string text_halign,string text_valign,string text_wrap)   Returns: TODO: Box
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2
calcLibrary "calc" Library for math functions. will expand over time. split(_sumTotal, _divideBy, _forceMinimum, _haltOnError)   Split a large number into integer sized chunks   Parameters:      _sumTotal : (int) Total numbert of items      _divideBy : (int) Groups to make      _forceMinimum : (bool) force minimum number 1/group      _haltOnError : (bool) force error if too few groups   Returns: int array of items per group
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3
percentageLibLibrary "percentageLib" : every thing need anout percentage getPercentage(entry, exit)   : get percentage change of of two value   Parameters:      entry : : value of entry price      exit : : value of exit price   Returns: : negative or positive value applyPercentageNoAddUp(price, percentage)   : apply percentage change on value decrease or increase   Parameters:      price : : value of price      percentage : : percentage change can be negative or positive   Returns: : return only positive value applyPercentageAddUp(price, percentage)   : apply percentage change on value decrease or increase   Parameters:      price : : value of price      percentage : : percentage change can be negative or positive   Returns: : return only positive value reversePercentage(percentage)   : get percentage (positive or negative) and return the percentage need to back to previous price   Parameters:      percentage : : percentage change can be negative or positive   Returns: : return positive/negative value @example : reversePercentage(10) =>11.11111111111111111111111 , reversePercentage(10) =>9.0909090909090909 getReversePercentage(price, percentage)   : get two prices and return the percentage need to back to previous price   Parameters:      price : : value of price      percentage : : percentage change can be negative or positive   Returns: : return only positive value @example : getReversePercentage(100,90) =>11.11111111111111111111111 multipeBarTotalPercentage()
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xor logical operatorLibrary "xor" xor(a, b)   xor: Exclusive or, or exclusive disjunction is a logical operation that is true if and only if its arguments differ (one is true, the other is false).   Parameters:      a : first argument      b : second argument   Returns: returns xor (true only if a and b are true, but not both) Example: true xor true = false true xor false = true false xor true = true false xor false = false
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por TheSocialCryptoClub
Moving_AveragesLibrary "Moving_Averages" This library contains majority important moving average functions with int series support. Which means that they can be used with variable length input. For conventional use, please use tradingview built-in ta functions for moving averages as they are more precise. I'll use functions in this library for my other scripts with dynamic length inputs. ema(src, len)   Exponential Moving Average (EMA)   Parameters:      src : Source      len : Period   Returns: Exponential Moving Average with Series Int Support (EMA) alma(src, len, a_offset, a_sigma)   Arnaud Legoux Moving Average (ALMA)   Parameters:      src : Source      len : Period      a_offset : Arnaud Legoux offset      a_sigma : Arnaud Legoux sigma   Returns: Arnaud Legoux Moving Average (ALMA) covwema(src, len)   Coefficient of Variation Weighted Exponential Moving Average (COVWEMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Exponential Moving Average (COVWEMA) covwma(src, len)   Coefficient of Variation Weighted Moving Average (COVWMA)   Parameters:      src : Source      len : Period   Returns: Coefficient of Variation Weighted Moving Average (COVWMA) dema(src, len)   DEMA - Double Exponential Moving Average   Parameters:      src : Source      len : Period   Returns: DEMA - Double Exponential Moving Average edsma(src, len, ssfLength, ssfPoles)   EDSMA - Ehlers Deviation Scaled Moving Average   Parameters:      src : Source      len : Period      ssfLength : EDSMA - Super Smoother Filter Length      ssfPoles : EDSMA - Super Smoother Filter Poles   Returns: Ehlers Deviation Scaled Moving Average (EDSMA) eframa(src, len, FC, SC)   Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA)   Parameters:      src : Source      len : Period      FC : Lower Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average      SC : Upper Shift Limit for Ehlrs Modified Fractal Adaptive Moving Average   Returns: Ehlrs Modified Fractal Adaptive Moving Average (EFRAMA) ehma(src, len)   EHMA - Exponential Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Exponential Hull Moving Average (EHMA) etma(src, len)   Exponential Triangular Moving Average (ETMA)   Parameters:      src : Source      len : Period   Returns: Exponential Triangular Moving Average (ETMA) frama(src, len)   Fractal Adaptive Moving Average (FRAMA)   Parameters:      src : Source      len : Period   Returns: Fractal Adaptive Moving Average (FRAMA) hma(src, len)   HMA - Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Hull Moving Average (HMA) jma(src, len, jurik_phase, jurik_power)   Jurik Moving Average - JMA   Parameters:      src : Source      len : Period      jurik_phase : Jurik (JMA) Only - Phase      jurik_power : Jurik (JMA) Only - Power   Returns: Jurik Moving Average (JMA) kama(src, len, k_fastLength, k_slowLength)   Kaufman's Adaptive Moving Average (KAMA)   Parameters:      src : Source      len : Period      k_fastLength : Number of periods for the fastest exponential moving average      k_slowLength : Number of periods for the slowest exponential moving average   Returns: Kaufman's Adaptive Moving Average (KAMA) kijun(_high, _low, len, kidiv)   Kijun v2   Parameters:      _high : High value of bar      _low : Low value of bar      len : Period      kidiv : Kijun MOD Divider   Returns: Kijun v2 lsma(src, len, offset)   LSMA/LRC - Least Squares Moving Average / Linear Regression Curve   Parameters:      src : Source      len : Period      offset : Offset   Returns: Least Squares Moving Average (LSMA)/ Linear Regression Curve (LRC) mf(src, len, beta, feedback, z)   MF - Modular Filter   Parameters:      src : Source      len : Period      beta : Modular Filter, General Filter Only - Beta      feedback : Modular Filter Only - Feedback      z : Modular Filter Only - Feedback Weighting   Returns: Modular Filter (MF) rma(src, len)   RMA - RSI Moving average   Parameters:      src : Source      len : Period   Returns: RSI Moving average (RMA) sma(src, len)   SMA - Simple Moving Average   Parameters:      src : Source      len : Period   Returns: Simple Moving Average (SMA) smma(src, len)   Smoothed Moving Average (SMMA)   Parameters:      src : Source      len : Period   Returns: Smoothed Moving Average (SMMA) stma(src, len)   Simple Triangular Moving Average (STMA)   Parameters:      src : Source      len : Period   Returns: Simple Triangular Moving Average (STMA) tema(src, len)   TEMA - Triple Exponential Moving Average   Parameters:      src : Source      len : Period   Returns: Triple Exponential Moving Average (TEMA) thma(src, len)   THMA - Triple Hull Moving Average   Parameters:      src : Source      len : Period   Returns: Triple Hull Moving Average (THMA) vama(src, len, volatility_lookback)   VAMA - Volatility Adjusted Moving Average   Parameters:      src : Source      len : Period      volatility_lookback : Volatility lookback length   Returns: Volatility Adjusted Moving Average (VAMA) vidya(src, len)   Variable Index Dynamic Average (VIDYA)   Parameters:      src : Source      len : Period   Returns: Variable Index Dynamic Average (VIDYA) vwma(src, len)   Volume-Weighted Moving Average (VWMA)   Parameters:      src : Source      len : Period   Returns: Volume-Weighted Moving Average (VWMA) wma(src, len)   WMA - Weighted Moving Average   Parameters:      src : Source      len : Period   Returns: Weighted Moving Average (WMA) zema(src, len)   Zero-Lag Exponential Moving Average (ZEMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Exponential Moving Average (ZEMA) zsma(src, len)   Zero-Lag Simple Moving Average (ZSMA)   Parameters:      src : Source      len : Period   Returns: Zero-Lag Simple Moving Average (ZSMA) evwma(src, len)   EVWMA - Elastic Volume Weighted Moving Average   Parameters:      src : Source      len : Period   Returns: Elastic Volume Weighted Moving Average (EVWMA) tt3(src, len, a1_t3)   Tillson T3   Parameters:      src : Source      len : Period      a1_t3 : Tillson T3 Volume Factor   Returns: Tillson T3 gma(src, len)   GMA - Geometric Moving Average   Parameters:      src : Source      len : Period   Returns: Geometric Moving Average (GMA) wwma(src, len)   WWMA - Welles Wilder Moving Average   Parameters:      src : Source      len : Period   Returns: Welles Wilder Moving Average (WWMA) ama(src, _high, _low, len, ama_f_length, ama_s_length)   AMA - Adjusted Moving Average   Parameters:      src : Source      _high : High value of bar      _low : Low value of bar      len : Period      ama_f_length : Fast EMA Length      ama_s_length : Slow EMA Length   Returns: Adjusted Moving Average (AMA) cma(src, len)   Corrective Moving average (CMA)   Parameters:      src : Source      len : Period   Returns: Corrective Moving average (CMA) gmma(src, len)   Geometric Mean Moving Average (GMMA)   Parameters:      src : Source      len : Period   Returns: Geometric Mean Moving Average (GMMA) ealf(src, len, LAPercLen_, FPerc_)   Ehler's Adaptive Laguerre filter (EALF)   Parameters:      src : Source      len : Period      LAPercLen_ : Median Length      FPerc_ : Median Percentage   Returns: Ehler's Adaptive Laguerre filter (EALF) elf(src, len, LAPercLen_, FPerc_)   ELF - Ehler's Laguerre filter   Parameters:      src : Source      len : Period      LAPercLen_ : Median Length      FPerc_ : Median Percentage   Returns: Ehler's Laguerre Filter (ELF) edma(src, len)   Exponentially Deviating Moving Average (MZ EDMA)   Parameters:      src : Source      len : Period   Returns: Exponentially Deviating Moving Average (MZ EDMA) pnr(src, len, rank_inter_Perc_)   PNR - percentile nearest rank   Parameters:      src : Source      len : Period      rank_inter_Perc_ : Rank and Interpolation Percentage   Returns: Percentile Nearest Rank (PNR) pli(src, len, rank_inter_Perc_)   PLI - Percentile Linear Interpolation   Parameters:      src : Source      len : Period      rank_inter_Perc_ : Rank and Interpolation Percentage   Returns: Percentile Linear Interpolation (PLI) rema(src, len)   Range EMA (REMA)   Parameters:      src : Source      len : Period   Returns: Range EMA (REMA) sw_ma(src, len)   Sine-Weighted Moving Average (SW-MA)   Parameters:      src : Source      len : Period   Returns: Sine-Weighted Moving Average (SW-MA) vwap(src, len)   Volume Weighted Average Price (VWAP)   Parameters:      src : Source      len : Period   Returns: Volume Weighted Average Price (VWAP) mama(src, len)   MAMA - MESA Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: MESA Adaptive Moving Average (MAMA) fama(src, len)   FAMA - Following Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Following Adaptive Moving Average (FAMA) hkama(src, len)   HKAMA - Hilbert based Kaufman's Adaptive Moving Average   Parameters:      src : Source      len : Period   Returns: Hilbert based Kaufman's Adaptive Moving Average (HKAMA)
Biblioteca Pine Script®
por MightyZinger
Price Displacement - Candlestick (OHLC) CalculationsA Magical little helper friend for Candle Math. When composing scripts, it is often necessary to manipulate the math around the OHLC. At times, you want a scalar (absolute) value others you want a vector (+/-). Sometimes you want the open - close and sometimes you want just the positive number of the body size. You might want it in ticks or you might want it in points or you might want in percentages. And every time you try to put it together you waste precious time and brain power trying to think about how to properly structure what you're looking for. Not to mention it's normally not that aesthetically pleasing to look at in the code. So, this fixes all of that. Using this library. A function like 'pd.pt(_exp)' can call any kind of candlestick math you need. The function returns the candlestick math you define using particular expressions. Candle Math Functions Include: Points: pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp. vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp. Ticks: tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp. vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp. Percentages: pct(_exp, _prec) Absolute Percent Displacement. (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp. vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp. Expressions You Can Use with Formulas: The expressions are simple (simple strings that is) and I did my best to make them sensible, generally using just the ohlc abreviations. I also included uw, lw, bd, and rg for when you're just trying to pull a candle component out. That way you don't have to think about which of the ohlc you're trying to get just use pd.tick("uw") and now the variable is assigned the length of the upper wick, absolute value, in ticks. If you wanted the vector in pts its pd.vpt("uw"). It also makes changing things easy too as I write it out. Expression List: Combinations "oh" = open - high "ol" = open - low "oc" = open - close "ho" = high - open "hl" = high - low "hc" = high - close "lo" = low - open "lh" = low - high "lc" = low - close "co" = close - open "ch" = close - high "cl" = close - low Candle Components "uw" = Upper Wick "bd" = Body "lw" = Lower Wick "rg" = Range Pct() Only "scp" = Scalar Close Position "sop" = Scalar Open Position "vcp" = Vector Close Position "vop" = Vector Open Position The attributes are going to be available in the pop up dialogue when you mouse over the function, so you don't really have to remember them. I tried to make that look as efficient as possible. You'll notice it follows the OHLC pattern. Thus, "oh" precedes "ho" (heyo) because "O" would be first in the OHLC. Its a way to help find the expression you're looking for quickly. Like looking through an alphabetized list for traders. There is a copy/paste console friendly helper list in the script itself. Additional Notes on the Pct() Only functions: This is the original reason I started writing this. These concepts place a rating/value on the bar based on candle attributes in one number. These formulas put a open or close value in a percentile of the bar relative to another aspect of the bar. Scalar - Non-directional. Absolute Value. Scalar Position: The position of the price attribute relative to the scale of the bar range (high - low) Example: high = 100. low = 0. close = 25. (A) Measure price distance C-L. How high above the low did the candle close (e.g. close - low = 25) (B) Divide by bar range (high - low). 25 / (100 - 0) = .25 Explaination: The candle closed at the 25th percentile of the bar range given the bar range low = 0 and bar range high = 100. Formula: scp = (close - low) / (high - low) Vector = Directional. Vector Position: The position of the price attribute relative to the scale of the bar midpoint (Vector Position at hl2 = 0) Example: high = 100. low = 0. close = 25. (A) Measure Price distance C-L: How high above the low did the candle close (e.g. close - low = 25) (B) Measure Price distance H-C: How far below the high did the candle close (e.g. high - close = 75) (C) Take Difference: A - B = C = -50 (D) Divide by bar range (high - low). -50 / (100 - 0) = -0.50 Explaination: Candle close at the midpoint between hl2 and the low. Formula: vcp = { / (high - low) } Thank you for checking this out. I hope no one else has already done this (because it took half the day) and I hope you find value in it. Be well. Trade well. Library "PD" Price Displacement pt(_exp) Absolute Point Displacement. Point quantity of given size parameters according to _exp.   Parameters:      _exp : (string) Price Parameter   Returns: Point size of given expression as an absolute value. vpt(_exp) Vector Point Displacement. Point quantity of given size parameters according to _exp.   Parameters:      _exp : (string) Price Parameter   Returns: Point size of given expression as a vector. tick(_exp) Absolute Tick Displacement. Tick quantity of given size parameters according to _exp.   Parameters:      _exp : (string) Price Parameter   Returns: Tick size of given expression as an absolute value. vtick(_exp) Vector Tick Displacement. Tick quantity of given size parameters according to _exp.   Parameters:      _exp : (string) Price Parameter   Returns: Tick size of given expression as a vector. pct(_exp, _prec) Absolute Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.   Parameters:      _exp : (string) Expression      _prec : (int) Overload - Place value precision definition   Returns: Percent size of given expression as decimal. vpct(_exp, _prec) Vector Percent Displacement (w/rounding overload). Percent quantity of bar range of given size parameters according to _exp.   Parameters:      _exp : (string) Expression      _prec : (int) Overload - Place value precision definition   Returns: Percent size of given expression as decimal.
Biblioteca Pine Script®
por kurtsmock
1
honest personal libraryLibrary "honestpersonallibrary" thestratnumber() this will return the number 1,2 or 3 using the logic from Rob Smiths #thestrat which uses these type of bars for setups getBodySize() Gets the current candle's body size (in POINTS, divide by 10 to get pips)   Returns: The current candle's body size in POINTS getTopWickSize() Gets the current candle's top wick size (in POINTS, divide by 10 to get pips)   Returns: The current candle's top wick size in POINTS getBottomWickSize() Gets the current candle's bottom wick size (in POINTS, divide by 10 to get pips)   Returns: The current candle's bottom wick size in POINTS getBodyPercent() Gets the current candle's body size as a percentage of its entire size including its wicks   Returns: The current candle's body size percentage strictBearPinBar(float, float) This it to find pinbars with a very long wick compared to the body that are bearish   Parameters:      float : minTopMulitplier (default=4) The minimum number of times that the top wick has to be bigger than the candle body size      float : maxBottomMultiplier (default=2) The maximum number of times that the bottom wick can be bigger than the candle body size   Returns: a bool function true if current candle is withing the parameters strictBullPinBar(float, float) This it to find pinbars with a very long wick compared to the body that are bearish   Parameters:      float : minTopMulitplier (default=4) The minimum number of times that the top wick has to be bigger than the candle body size      float : maxBottomMultiplier (default=2) The maximum number of times that the bottom wick can be bigger than the candle body size   Returns: a bool function true if current candle is withing the parameters
Biblioteca Pine Script®
por Honestcowboy
OrdinaryLeastSquaresLibrary "OrdinaryLeastSquares" One of the most common ways to estimate the coefficients for a linear regression is to use the Ordinary Least Squares (OLS) method. This library implements OLS in pine. This implementation can be used to fit a linear regression of multiple independent variables onto one dependent variable, as long as the assumptions behind OLS hold. solve_xtx_inv(x, y) Solve a linear system of equations using the Ordinary Least Squares method. This function returns both the estimated OLS solution and a matrix that essentially measures the model stability (linear dependence between the columns of 'x'). NOTE: The latter is an intermediate step when estimating the OLS solution but is useful when calculating the covariance matrix and is returned here to save computation time so that this step doesn't have to be calculated again when things like standard errors should be calculated.   Parameters:      x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.      y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.   Returns: Returns both the estimated OLS solution and a matrix that essentially measures the model stability (xtx_inv is equal to (X'X)^-1). solve(x, y) Solve a linear system of equations using the Ordinary Least Squares method.   Parameters:      x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.      y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.   Returns: Returns the estimated OLS solution. standard_errors(x, y, beta_hat, xtx_inv) Calculate the standard errors.   Parameters:      x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.      y : The matrix containing the dependent variable. This matrix can only contain one dependent variable and can therefore only contain one column. The row count of 'x' and 'y' must match.      beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().      xtx_inv : This is (X'X)^-1, which means we take the transpose of the X matrix, multiply that the X matrix and then take the inverse of the result. This essentially measures the linear dependence between the columns of the X matrix.   Returns: The standard errors. estimate(x, beta_hat) Estimate the next step of a linear model.   Parameters:      x : The matrix containing the independent variables. Each column is regarded by the algorithm as one independent variable. The row count of 'x' and 'y' must match.      beta_hat : The Ordinary Least Squares (OLS) solution provided by solve_xtx_inv() or solve().   Returns: Returns the new estimate of Y based on the linear model.
Biblioteca Pine Script®
por lejmer
1