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BossExoticMAs A next-generation moving average and smoothing library by TheStopLossBoss, featuring premium adaptive, exotic, and DSP-inspired filters — optimized for Pine Script® v6 and designed for Traders who demand precision and beauty. > BossExoticMAs is a complete moving average and signal-processing toolkit built for Pine Script v6. It combines the essential trend filters (SMA, EMA, WMA, etc.) with advanced, high-performance exotic types used by quants, algo designers, and adaptive systems. Each function is precision-tuned for stability, speed, and visual clarity — perfect for building custom baselines, volatility filters, dynamic ribbons, or hybrid signal engines. Includes built-in color gradient theming powered by the exclusive BossGradient — //Key Features ✅ Full Moving Average Set SMA, EMA, ZEMA, WMA, HMA, WWMA, SMMA DEMA, TEMA, T3 (Tillson) ALMA, KAMA, LSMA VMA, VAMA, FRAMA ✅ Signal Filters One-Euro Filter (Crispin/Casiez implementation) ATR-bounded Range Filter ✅ Color Engine lerpColor() safe blending using color.from_gradient Thematic gradient palettes: STOPLOSS, VAPORWAVE, ROYAL FLAME, MATRIX FLOW Exclusive: BOSS GRADIENT ✅ Helper Functions Clamping, normalization, slope detection, tick delta Slope-based dynamic color control via slopeThemeColor() 🧠 Usage Example //@version=6 indicator("Boss Exotic MA Demo", overlay=true) import TheStopLossBoss/BossExoticMAs/1 as boss len = input.int(50, "Length") atype = input.string("T3", "MA Type", options= ) t3factor = input.float(0.7, "T3 β", step=0.05) smoothColor = boss.slopeThemeColor(close, "BOSS GRADIENT", 0.001)ma = boss.maSelect(close, len, atype, t3factor, 0.85, 14) plot(ma, "Boss Exotic MA", color=smoothColor, linewidth=2) --- 🔑 Notes Built exclusively for Pine Script® v6 Library designed for import use — all exports are prefixed cleanly (boss.functionName()) Some functions maintain internal state (var-based). Warnings are safe to ignore — adaptive design choice. Each MA output is non-repainting and mathematically stable. --- 📜 Author TheStopLossBoss Designer of precision trading systems and custom adaptive algorithms. Follow for exclusive releases, educational material, and full-stack trend solutions. movingaverage, trend, adaptive, filter, volatility, smoothing, quant, technicalanalysis, bossgradient, t3, alma, frama, vma
Biblioteca Pine Script®
por TheStopLossBoss
Mirpapa_Lib_boxLibrary "Mirpapa_Lib_box" AddFVG(boxes, htfTimeframe, htfBarIndex, top, bottom, isBull, _text)   AddFVG @description FVG 박스 데이터 추가   Parameters:      boxes (array) : array 박스 배열      htfTimeframe (string) : string HTF 시간대 ("60", "240", "D")      htfBarIndex (int) : int HTF bar_index      top (float) : float 상단 가격      bottom (float) : float 하단 가격      isBull (bool) : bool 방향 (true=상승, false=하락)      _text (string)   Returns: void AddOB(boxes, htfTimeframe, htfBarIndex, top, bottom, isBull, _text)   AddOB @description OB 박스 데이터 추가   Parameters:      boxes (array) : array 박스 배열      htfTimeframe (string) : string HTF 시간대      htfBarIndex (int) : int HTF bar_index      top (float) : float 상단 가격      bottom (float) : float 하단 가격      isBull (bool) : bool 방향      _text (string)   Returns: void AddBB(boxes, htfTimeframe, htfBarIndex, top, bottom, isBull, _text)   AddBB @description BB 박스 데이터 추가   Parameters:      boxes (array) : array 박스 배열      htfTimeframe (string) : string HTF 시간대      htfBarIndex (int) : int HTF bar_index      top (float) : float 상단 가격      bottom (float) : float 하단 가격      isBull (bool) : bool 방향      _text (string)   Returns: void AddRB(boxes, htfTimeframe, htfBarIndex, top, bottom, isBull, _text)   AddRB @description RB 박스 데이터 추가   Parameters:      boxes (array) : array 박스 배열      htfTimeframe (string) : string HTF 시간대      htfBarIndex (int) : int HTF bar_index      top (float) : float 상단 가격      bottom (float) : float 하단 가격      isBull (bool) : bool 방향      _text (string)   Returns: void ProcessBoxes(boxes, boxType, colorBull, colorBear, closeCount, useLine, textAlignH, textAlignV, closeColor)   ProcessBoxes @description 박스 배열 처리 (생성→확장→터치→종료)   Parameters:      boxes (array) : array 박스 배열      boxType (string) : string 박스 타입 ("FVG", "OB", "BB", "RB")      colorBull (color) : color 상승 색상      colorBear (color) : color 하락 색상      closeCount (int) : int 터치 종료 횟수      useLine (bool) : bool 중간라인 사용 여부      textAlignH (string) : string 수평 정렬      textAlignV (string) : string 수직 정렬      closeColor (color) : color 종료 색상   Returns: void GetActiveBoxCount(boxes)   GetActiveBoxCount @description 활성 박스 개수 반환   Parameters:      boxes (array) : array 박스 배열   Returns: int 활성 박스 개수 ClearInactiveBoxes(boxes)   ClearInactiveBoxes @description 비활성 박스 제거 (메모리 절약)   Parameters:      boxes (array) : array 박스 배열   Returns: void BoxData   BoxData   Fields:      _isActive (series bool) : 박스 활성화 상태      _isBull (series bool) : 방향 (true=상승, false=하락)      _boxTop (series float) : 상단 가격      _boxBot (series float) : 하단 가격      _basePoint (series float) : 터치 감지 기준점      _stage (series int) : 터치 횟수 카운터      _type (series string) : 박스 타입 ("FVG", "OB", "BB", "RB")      _htfTimeframe (series string) : HTF 시간대 ("60", "240", "D")      _htfBarIndex (series int) : HTF 기준 bar_index      _text (series string) : 사용자 추가 텍스트      _box (series box) : 박스 객체 (ProcessBoxes에서 생성)      _line (series line) : 라인 객체 (ProcessBoxes에서 생성)
Biblioteca Pine Script®
por goodia
mysourcetypesncsLibrary "mysourcetypes" Libreria personale per sorgenti estese (Close, Open, High, Low, Median, Typical, Weighted, Average, Average Median Body, Trend Biased, Trend Biased Extreme, Volume Body, Momentum Biased, Volatility Adjusted, Body Dominance, Shadow Biased, Gap Aware, Rejection Biased, Range Position, Adaptive Trend, Pressure Balanced, Impulse Wave) rclose()   Regular Close   Returns: Close price ropen()   Regular Open   Returns: Open price rhigh()   Regular High   Returns: High price rlow()   Regular Low   Returns: Low price rmedian()   Regular Median (HL2)   Returns: (High + Low) / 2 rtypical()   Regular Typical (HLC3)   Returns: (High + Low + Close) / 3 rweighted()   Regular Weighted (HLCC4)   Returns: (High + Low + Close + Close) / 4 raverage()   Regular Average (OHLC4)   Returns: (Open + High + Low + Close) / 4 ravemedbody()   Average Median Body   Returns: (Open + Close) / 2 rtrendb()   Trend Biased Regular   Returns: Trend-weighted price rtrendbext()   Trend Biased Extreme   Returns: Extreme trend-weighted price rvolbody()   Volume Weighted Body   Returns: Body midpoint weighted by volume intensity rmomentum()   Momentum Biased   Returns: Price biased towards momentum direction rvolatility()   Volatility Adjusted   Returns: Price adjusted by candle's volatility rbodydominance()   Body Dominance   Returns: Emphasizes body over wicks rshadowbias()   Shadow Biased   Returns: Price biased by shadow length rgapaware()   Gap Aware   Returns: Considers gap between candles rrejection()   Rejection Biased   Returns: Emphasizes price rejection levels rrangeposition()   Range Position   Returns: Where close sits within the candle range (0-100%) radaptivetrend()   Adaptive Trend   Returns: Adapts based on recent trend strength rpressure()   Pressure Balanced   Returns: Balances buying/selling pressure within candle rimpulse()   Impulse Wave   Returns: Detects impulsive moves vs corrections
Biblioteca Pine Script®
por stefanoncs
Mirpapa_Lib_DivergenceLibrary "Mirpapa_Lib_Divergence" 다이버전스 감지 및 시각화 라이브러리 (범용 설계) newPivot(bar, priceVal, indVal)   피벗 포인트 생성   Parameters:      bar (int) : 바 인덱스      priceVal (float) : 가격      indVal (float) : 지표값   Returns: PivotPoint newDivSettings(pivotLen, maxStore, maxShow)   다이버전스 설정 생성   Parameters:      pivotLen (int) : 피벗 좌우 캔들      maxStore (int) : 저장 개수      maxShow (int) : 표시 라인 개수   Returns: DivergenceSettings emptyDivResult()   빈 다이버전스 결과   Returns: 감지 안 된 DivergenceResult checkPivotHigh(length, source)   고점 피벗 감지   Parameters:      length (int) : 좌우 비교 캔들 수      source (float) : 비교할 데이터 (지표값)   Returns: 피벗 값 또는 na checkPivotLow(length, source)   저점 피벗 감지   Parameters:      length (int) : 좌우 비교 캔들 수      source (float) : 비교할 데이터 (지표값)   Returns: 피벗 값 또는 na addPivotToArray(pivotArray, pivot, maxSize)   피벗을 배열에 추가 (FIFO 방식)   Parameters:      pivotArray (array) : 피벗 배열      pivot (PivotPoint) : 추가할 피벗      maxSize (int) : 최대 크기 checkBullishDivergence(pivotArray)   상승 다이버전스 체크 (Bullish)   Parameters:      pivotArray (array) : 저점 피벗 배열   Returns: DivergenceResult checkBearishDivergence(pivotArray)   하락 다이버전스 체크 (Bearish)   Parameters:      pivotArray (array) : 고점 피벗 배열   Returns: DivergenceResult createDivLine(result, lineColor, isOverlay)   다이버전스 라인 생성   Parameters:      result (DivergenceResult) : DivergenceResult      lineColor (color) : 라인 색상      isOverlay (bool) : true면 가격 기준, false면 지표 기준   Returns: cleanupLines(lineArray, labelArray, maxLines)   오래된 라인/라벨 정리   Parameters:      lineArray (array) : 라인 배열      labelArray (array) : 라벨 배열      maxLines (int) : 최대 유지 개수 addLineAndCleanup(lineArray, labelArray, newLine, newLabel, maxLines)   라인/라벨 추가 및 자동 정리   Parameters:      lineArray (array) : 라인 배열      labelArray (array) : 라벨 배열      newLine (line) : 새 라인      newLabel (label) : 새 라벨      maxLines (int) : 최대 개수 PivotPoint   피벗 데이터 저장   Fields:      barIndex (series int) : 바 인덱스      price (series float) : 종가      indicatorValue (series float) : 지표값 DivergenceSettings   다이버전스 설정   Fields:      pivotLength (series int) : 피벗 좌우 캔들 수      maxPivotsStore (series int) : 저장할 최대 피벗 개수      maxLinesShow (series int) : 표시할 최대 라인 개수 DivergenceResult   다이버전스 감지 결과   Fields:      detected (series bool) : 다이버전스 감지 여부      isBullish (series bool) : true면 상승, false면 하락      bar1 (series int) : 첫 번째 피벗 바 인덱스      value1_price (series float) : 첫 번째 가격      value1_ind (series float) : 첫 번째 지표값      bar2 (series int) : 두 번째 피벗 바 인덱스      value2_price (series float) : 두 번째 가격      value2_ind (series float) : 두 번째 지표값
Biblioteca Pine Script®
por goodia
Mirpapa_Lib_MACDLibrary "Mirpapa_Lib_MACD" MACD 계산 및 크로스 체크를 위한 라이브러리 calc_smma(src, len)   SMMA (Smoothed Moving Average) 계산   Parameters:      src (float) : 소스 데이터      len (simple int) : 길이   Returns: SMMA 값 calc_zlema(src, length)   ZLEMA (Zero Lag EMA) 계산   Parameters:      src (float) : 소스 데이터      length (simple int) : 길이   Returns: ZLEMA 값 checkMacdCross(lengthMA, lengthSignal, src, enabled)   MACD 크로스오버 체크   Parameters:      lengthMA (simple int) : MACD 길이      lengthSignal (int) : 시그널 길이      src (float) : 소스 (기본값: hlc3)      enabled (bool) : 계산 활성화 여부 (기본값: true)   Returns:
Biblioteca Pine Script®
por goodia
Obj_XABCD_HarmonicLibrary "Obj_XABCD_Harmonic" Harmonic XABCD Pattern object and associated methods. Easily validate, draw, and get information about harmonic patterns. See example code at the end of the script for details. init_params(pct_error, pct_asym, types, w_e, w_p, w_d)   Create a harmonic parameters object (used by xabcd_harmonic object for pattern validation and scoring).   Parameters:      pct_error (float) : Allowed % error of leg retracement ratio versus the defined harmonic ratio      pct_asym (float) : Allowed leg length/period asymmetry % (a leg is considered invalid if it is this % longer or shorter than the average length of the other legs)      types (array) : Array of pattern types to validate (1=Gartley, 2=Bat, 3=Butterfly, 4=Crab, 5=Shark, 6=Cypher)      w_e (float) : Weight of ratio % error (used in score calculation, dft = 1)      w_p (float) : Weight of PRZ confluence (used in score calculation, dft = 1)      w_d (float) : Weight of Point D / PRZ confluence (used in score calculation, dft = 1)   Returns: harmonic_params object instance. It is recommended to store and reuse this object for multiple xabcd_harmonic objects rather than creating new params objects unnecessarily. init(x, a, b, c, d, params, tp, p)   Initialize an xabcd_harmonic object instance from a given set of points If the pattern is valid, an xabcd_harmonic object instance is returned. If you want to specify your own validation and scoring parameters, you can do so by passing a harmonic_params object (params). Or, if you prefer to do your own validation, you can explicitly pass the harmonic pattern type (tp) and validation will be skipped. You can also pass in an existing xabcd_harmonic instance if you wish to re-initialize it (e.g. for re-validation and/or re-scoring).   Parameters:      x (point type from dlmysolutions/Pattern/1) : Point X      a (point type from dlmysolutions/Pattern/1) : Point A      b (point type from dlmysolutions/Pattern/1) : Point B      c (point type from dlmysolutions/Pattern/1) : Point C      d (point type from dlmysolutions/Pattern/1) : Point D      params (harmonic_params) : harmonic_params used to validate and score the pattern. Validation will be skipped if a type (tp) is explicitly passed in.      tp (int) : Pattern type      p (xabcd_harmonic) : xabcd_harmonic object instance to initialize (optional, for re-validation/re-scoring)   Returns: xabcd_harmonic object instance if a valid harmonic, else na init(xX, xY, aX, aY, bX, bY, cX, cY, dX, dY, params, tp, p)   Initialize an xabcd_harmonic object instance from a given set of x and y coordinate values. If the pattern is valid, an xabcd_harmonic object instance is returned. If you want to specify your own validation and scoring parameters, you can do so by passing a harmonic_params object (params). Or, if you prefer to do your own validation, you can explicitly pass the harmonic pattern type (tp) and validation will be skipped. You can also pass in an existing xabcd_harmonic instance if you wish to re-initialize it (e.g. for re-validation and/or re-scoring).   Parameters:      xX (int) : Point X bar index (required)      xY (float) : Point X price/level (required)      aX (int) : Point A bar index (required)      aY (float) : Point A price/level (required)      bX (int) : Point B bar index (required)      bY (float) : Point B price/level (required)      cX (int) : Point C bar index (required)      cY (float) : Point C price/level (required)      dX (int) : Point D bar index      dY (float) : Point D price/level      params (harmonic_params) : harmonic_params used to validate and score the pattern. Validation will be skipped if a type (tp) is explicitly passed in.      tp (int) : Pattern type      p (xabcd_harmonic) : xabcd_harmonic object instance to initialize (optional, for re-validation/re-scoring)   Returns: xabcd_harmonic object instance if a valid harmonic, else na init(pattern, params, tp, p)   Initialize an xabcd_harmonic object instance from a given pattern If the pattern is valid, an xabcd_harmonic object instance is returned. If you want to specify your own validation and scoring parameters, you can do so by passing a harmonic_params object (params). Or, if you prefer to do your own validation, you can explicitly pass the harmonic pattern type (tp) and validation will be skipped. You can also pass in an existing xabcd_harmonic instance if you wish to re-initialize it (e.g. for re-validation and/or re-scoring).   Parameters:      pattern (pattern type from dlmysolutions/Pattern/1) : Pattern      params (harmonic_params) : harmonic_params used to validate and score the pattern. Validation will be skipped if a type (tp) is explicitly passed in.      tp (int) : Pattern type      p (xabcd_harmonic) : xabcd_harmonic object instance to initialize (optional, for re-validation/re-scoring)   Returns: xabcd_harmonic object instance if a valid harmonic, else na method get_name(p)   Get the pattern name   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object   Returns: Pattern name (string) method get_symbol(p)   Get the pattern symbol   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object   Returns: Pattern symbol (1 byte string) method get_pid(p)   Get the Pattern ID. Patterns of the same type with the same coordinates will have the same Pattern ID.   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object   Returns: Pattern ID (string) method set_target(p, target, target_lvl, calc_target)   Set value for a target. Use the calc_target parameter to automatically calculate the target for a specific harmonic ratio.   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object      target (int) : Target (1 or 2)      target_lvl (float) : Target price/level (required if calc_target is not specified)      calc_target (string) : Target to auto calculate (required if target is not specified) Options:   Returns: Target price/level (float) method erase_pattern(p)   Erase the pattern   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object   Returns: p method draw_pattern(p, clr)   Draw the pattern   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object      clr (color)   Returns: Pattern lines method erase_label(p)   Erase the pattern label   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object   Returns: p method draw_label(p, clr, txt_clr, txt, tooltip)   Draw the pattern label. Default text is the pattern name.   Namespace types: xabcd_harmonic   Parameters:      p (xabcd_harmonic) : Instance of xabcd_harmonic object      clr (color) : Label color      txt_clr (color) : Text color      txt (string) : Label text      tooltip (string) : Tooltip text   Returns: Label harmonic_params   Validation and scoring parameters for a Harmonic Pattern object (xabcd_harmonic)   Fields:      pct_error (series float) : Allowed % error of leg retracement ratio versus the defined harmonic ratio      pct_asym (series float)      types (array)      w_e (series float)      w_p (series float)      w_d (series float) xabcd_harmonic   Harmonic Pattern object   Fields:      bull (series bool) : Bullish pattern flag      tp (series int)      x (point type from dlmysolutions/Pattern/1)      a (point type from dlmysolutions/Pattern/1)      b (point type from dlmysolutions/Pattern/1)      c (point type from dlmysolutions/Pattern/1)      d (point type from dlmysolutions/Pattern/1)      r_xb (series float)      re_xb (series float)      r_ac (series float)      re_ac (series float)      r_bd (series float)      re_bd (series float)      r_xd (series float)      re_xd (series float)      score (series float)      score_eAvg (series float)      score_prz (series float)      score_eD (series float)      prz_bN (series float)      prz_bF (series float)      prz_xN (series float)      prz_xF (series float)      t1Hit (series bool) : Target 1 flag      t1 (series float)      t2Hit (series bool)      t2 (series float)      sHit (series bool) : Stop flag      stop (series float) : Stop level      entry (series float) : Entry level      eHit (series bool)      e (point type from dlmysolutions/Pattern/1)      invalid_d (series bool)      pLines (array)      pLabel (series label)      pid (series string)      params (harmonic_params)
Biblioteca Pine Script®
por dlmysolutions
PatternLibrary "Pattern" Pattern object definitions and functions. Easily draw and keep track of patterns, legs, and points. Supported pattern types: Type Leg validation # legs "xabcd" Direction 3 or 4 (point D not required) "zigzag" Direction >= 2 "free" None >= 2 Summary of exported types and associated methods/functions: type point A point on the chart (x,y) draw_label() Draw a point label erase_label() Erase a point label type leg A pattern leg (i.e. point A to point B) leg_init() Initialize/instantiate a leg draw() Draw a leg erase() Erase a leg leg_getLineTerms() Get the slope and y-intercept of a leg leg_getPrice() Get price (Y) at a given bar index (X) within a leg type pattern A pattern (set of at least 2 connected legs) pattern_init() Initialize/instantiate a pattern draw() Draw a pattern erase() Erase a pattern *See bottom of the script for example usage* erase_label(this)   Delete the point label   Parameters:      this (point) : Point   Returns: Void draw_label(this, position, clr, transp, txt_clr, txt, tooltip, size)   Draw the point label   Parameters:      this (point) : Point      position (string)      clr (color)      transp (float)      txt_clr (color)      txt (string)      tooltip (string)      size (string)   Returns: line leg_init(a, b, prev, next, line)   Initialize a pattern leg   Parameters:      a (point) : Point A (required)      b (point) : Point B (required)      prev (leg) : Previous leg      next (leg) : Next leg      line (line) : Line   Returns: New instance of leg object erase(this)   Delete the pattern leg   Parameters:      this (leg) : Leg   Returns: Void erase(this)   Delete the pattern lines   Parameters:      this (pattern) : Pattern   Returns: Void draw(this, clr, style, transp, width)   Draw the pattern leg   Parameters:      this (leg) : Leg      clr (color) : Color      style (string) : Style ("solid", "dotted", "dashed", "arrowleft", "arrowright")      transp (float) : Transparency      width (int) : Width   Returns: line draw(this, clr, style, transp, width)   Draw the pattern   Parameters:      this (pattern) : Pattern      clr (color) : Color      style (string) : Style ("solid", "dotted", "dashed", "arrowleft", "arrowright")      transp (float) : Transparency      width (int) : Width   Returns: line leg_getLineTerms(this)   Get the slope and y-intercept of a leg   Parameters:      this (leg) : Leg   Returns: leg_getPrice(this, index)   Get the price (Y) at a given bar index (X) within the leg   Parameters:      this (leg) : Leg      index (int) : Bar index   Returns: Price (float) pattern_init(legs, tp, name, subType, pid)   Initialize a pattern object from a given set of legs   Parameters:      legs (array) : Array of pattern legs (required)      tp (string) : Pattern type ("zigzag", "xabcd", or "free". dft = "free")      name (string) : Pattern name      subType (string) : Pattern subtype      pid (string) : Pattern Identifier string   Returns: New instance of pattern object, if one was successfully created pattern_init(points, tp, name, subType, pid)   Initialize a pattern object from a given set of points   Parameters:      points (array)      tp (string) : Pattern type ("zigzag", "xabcd", or "free". dft = "free")      name (string) : Pattern name      subType (string) : Pattern subtype      pid (string) : Pattern Identifier string   Returns: New instance of pattern object, if one was successfully created point   A point on the chart (x,y)   Fields:      x (series int) : Bar index (x coordinate)      y (series float)      label (series label) leg   A pattern leg (point A to point B)   Fields:      a (point) : Point A      b (point)      deltaX (series int)      deltaY (series float)      prev (leg)      next (leg)      retrace (series float)      line (series line) pattern   A pattern (set of at least 2 connected legs)   Fields:      legs (array)      type (series string)      subType (series string)      name (series string)      pid (series string)
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DrawLibrary "Draw" Draw patterns, lines, labels, shapes etc. pat_colors(bull, buLn, beLn, ltxt)   Parameters:      bull (bool)      buLn (color)      beLn (color)      ltxt (color) size(size)   Parameters:      size (string) label_style(style)   Parameters:      style (string) line_style(style)   Parameters:      style (string) font_size(size)   Parameters:      size (string) xabcd(xX, xY, aX, aY, bX, bY, cX, cY, dX, dY, iE, bull, bu, be)   Draw XABCD pattern   Parameters:      xX (int)      xY (float)      aX (int)      aY (float)      bX (int)      bY (float)      cX (int)      cY (float)      dX (int)      dY (float)      iE (float)      bull (bool)      bu (color)      be (color) xabcd_inProgress(bull, type, tLimit, entry, stop, t1, t2, bcNt, bcFt, xaNt, xaFt, xX, xY, aY, bX, bY, cY, dX, dY, cBu, cBe, lTxt)   draw PRZ, entry, stop, targets, and projected reversal paths for XABCD pattern   Parameters:      bull (bool)      type (int)      tLimit (int)      entry (float)      stop (float)      t1 (float)      t2 (float)      bcNt (float)      bcFt (float)      xaNt (float)      xaFt (float)      xX (int)      xY (float)      aY (float)      bX (int)      bY (float)      cY (float)      dX (int)      dY (float)      cBu (color)      cBe (color)      lTxt (color) xabcd_incInProgress(bull, type, tLimit, entry, xX, xY, aY, bX, bY, cX, cY, dY, cBu, cBe, lTxt)   Parameters:      bull (bool)      type (int)      tLimit (int)      entry (float)      xX (int)      xY (float)      aY (float)      bX (int)      bY (float)      cX (int)      cY (float)      dY (float)      cBu (color)      cBe (color)      lTxt (color) xabcd_inProgress2(bull, tLimit, entry, stop, t1, t2, xadl, bcdl, xcdl, xX, xY, bX, bY, dX, dY, cBu, cBe, lTxt)   draw PRZ, entry, stop, targets, and projected reversal paths for XABCD pattern   Parameters:      bull (bool)      tLimit (int)      entry (float)      stop (float)      t1 (float)      t2 (float)      xadl (float)      bcdl (float)      xcdl (float)      xX (int)      xY (float)      bX (int)      bY (float)      dX (int)      dY (float)      cBu (color)      cBe (color)      lTxt (color) eHitLbl(x, e, dX, dY, bull, lOnly)   Draw entry hit label   Parameters:      x (int)      e (float)      dX (int)      dY (float)      bull (bool)      lOnly (bool) tHitLbl(x, tgt, eX, eY, bull)   Draw target hit label   Parameters:      x (int)      tgt (float)      eX (int)      eY (float)      bull (bool) sHitLbl(x, s, eX, eY, bull)   Draw stop hit label   Parameters:      x (int)      s (float)      eX (int)      eY (float)      bull (bool) level(y, x, type, length, extend, padding, b_style, colr, txt_color, txt, txt_loc, txt_size)   Draw a level (box)   Parameters:      y (float)      x (int)      type (int)      length (int)      extend (string)      padding (float)      b_style (string)      colr (color)      txt_color (color)      txt (string)      txt_loc (string)      txt_size (string) incTtTxt(tp, name, xbr, xbre, acr, acre, bcN, bcF, xaN, xaF, score, e)   Parameters:      tp (int)      name (string)      xbr (float)      xbre (float)      acr (float)      acre (float)      bcN (float)      bcF (float)      xaN (float)      xaF (float)      score (float)      e (float)
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TALibrary "TA" General technical analysis functions div_bull(pS, iS, cp_length_after, cp_length_before, pivot_length, lookback, lookback_pivs, no_broken, pW, iW, hidW, regW)   Test for bullish divergence   Parameters:      pS (float) : Price series (float)      iS (float) : Indicator series (float)      cp_length_after (simple int) : Bars after current (divergent) pivot low to be considered a valid pivot (optional int)      cp_length_before (simple int) : Bars before current (divergent) pivot low to be considered a valid pivot (optional int)      pivot_length (simple int) : Bars before and after prior pivot low to be considered valid pivot (optional int)      lookback (simple int) : Bars back to search for prior pivot low (optional int)      lookback_pivs (simple int) : Pivots back to search for prior pivot low (optional int)      no_broken (simple bool) : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)      pW (simple float) : Weight of change in price, used in degree of divergence calculation (optional float)      iW (simple float) : Weight of change in indicator, used in degree of divergence calculation (optional float)      hidW (simple float) : Weight of hidden divergence, used in degree of divergence calculation (optional float)      regW (simple float) : 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, lookback_pivs, no_broken, pW, iW, hidW, regW)   Test for bearish divergence   Parameters:      pS (float) : Price series (float)      iS (float) : Indicator series (float)      cp_length_after (simple int) : Bars after current (divergent) pivot high to be considered a valid pivot (optional int)      cp_length_before (simple int) : Bars before current (divergent) pivot highto be considered a valid pivot (optional int)      pivot_length (simple int) : Bars before and after prior pivot high to be considered valid pivot (optional int)      lookback (simple int) : Bars back to search for prior pivot high (optional int)      lookback_pivs (simple int) : Pivots back to search for prior pivot high (optional int)      no_broken (simple bool) : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)      pW (simple float) : Weight of change in price, used in degree of divergence calculation (optional float)      iW (simple float) : Weight of change in indicator, used in degree of divergence calculation (optional float)      hidW (simple float) : Weight of hidden divergence, used in degree of divergence calculation (optional float)      regW (simple float) : 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) test_cd(cd, bc, xa, xc, ad, pErr, p_types)   Validate CD leg of XABCD   Parameters:      cd (float)      bc (float)      xa (float)      xc (float)      ad (float)      pErr (float)      p_types (array) pat_xabcd_testSym(xax, abx, bcx, cdx, pAsym)   Validate ΔX symmetry of XABCD pattern   Parameters:      xax (int)      abx (int)      bcx (int)      cdx (int)      pAsym (float) harmonic_xabcd_validate(xX, xY, aX, aY, bX, bY, cX, cY, dX, dY, pErr, pAsym, gart, bat, bfly, crab, shark, cyph)   Validate harmonic XABCD pattern   Parameters:      xX (int) : X coordinate of point X (int)      xY (float) : Y coordinate of point X (float)      aX (int) : X coordinate of point A (int)      aY (float) : Y coordinate of point A (float)      bX (int) : X coordinate of point B (int)      bY (float) : Y coordinate of point B (float)      cX (int) : X coordinate of point C (int)      cY (float) : Y coordinate of point C (float)      dX (int) : X coordinate of point D (int)      dY (float) : Y coordinate of point D (float)      pErr (float) : Acceptable percent error of leg ratios (does not apply to legs defined within a range) (float)      pAsym (float) : Acceptable percent asymmetry of leg ΔX (each leg tested against average ΔX of prior legs) (float)      gart (bool) : Flag to validate Gartley pattern (bool)      bat (bool) : Flag to validate Bat pattern (bool)      bfly (bool) : Flag to validate Butterfly pattern (bool)      crab (bool) : Flag to validate Crab pattern (bool)      shark (bool) : Flag to validate Shark pattern (bool)      cyph (bool) : Flag to validate Cypher pattern (bool)   Returns: flag = true if valid harmonic t1 = true if valid gartley t2 = true if valid bat t3 = true if valid butterfly t4 = true if valid crab t5 = true if valid shark t6 = true if valid cypher harmonic_xabcd_validateIncomplete(xX, xY, aX, aY, bX, bY, cX, cY, pErr, pAsym, gart, bat, bfly, crab, shark, cyph)   Validate the first 3 legs of a harmonic XABCD pattern   Parameters:      xX (int) : X coordinate of point X (int)      xY (float) : Y coordinate of point X (float)      aX (int) : X coordinate of point A (int)      aY (float) : Y coordinate of point A (float)      bX (int) : X coordinate of point B (int)      bY (float) : Y coordinate of point B (float)      cX (int) : X coordinate of point C (int)      cY (float) : Y coordinate of point C (float)      pErr (float) : Acceptable percent error of leg ratios (does not apply to legs defined within a range) (float)      pAsym (float) : Acceptable percent asymmetry of leg ΔX (each leg tested against average ΔX of prior legs) (float)      gart (bool) : Flag to validate Gartley pattern (bool)      bat (bool) : Flag to validate Bat pattern (bool)      bfly (bool) : Flag to validate Butterfly pattern (bool)      crab (bool) : Flag to validate Crab pattern (bool)      shark (bool) : Flag to validate Shark pattern (bool)      cyph (bool) : Flag to validate Cypher pattern (bool)   Returns: flag = true if valid harmonic t1 = true if valid gartley t2 = true if valid bat t3 = true if valid butterfly t4 = true if valid crab t5 = true if valid shark t6 = true if valid cypher harmonic_xabcd_prz(type, xY, aY, bY, cY)   Get the potential reversal zone (PRZ) levels of a harmonic XABCD pattern   Parameters:      type (int) : Harmonic pattern type (int - 1 = Gartley, 2 = Bat, 3 = Butterfly, 4 = Crab, 5 = Shark, 6 = Cypher)      xY (float) : Y coordinate of point X (float)      aY (float) : Y coordinate of point A (float)      bY (float) : Y coordinate of point B (float)      cY (float) : Y coordinate of point C (float)   Returns: bc_u = nearest BC retracement/extension level (nearest to point C) bc_l = farthest BC retracement/extension level (nearest to point C) xa_u = nearest XA retracement/extension level (or the only XA level, if applicable) xa_l = farthest XA retracement/extension level (or na if not applicable) harmonic_xabcd_przClosest(l1, l2, l3, l4)   Get the confluent PRZ levels (i.e. the two closest PRZ levels) Order of arguments does not matter   Parameters:      l1 (float) : level 1 (float)      l2 (float) : level 2 (float)      l3 (float) : level 3 (float)      l4 (float) : level 4 (optional, float)   Returns: lL = lower confluent PRZ level lH = higher confluent PRZ level harmonic_xabcd_przRange(l1, l2, l3, l4)   Get upper and lower PRZ levels   Parameters:      l1 (float)      l2 (float)      l3 (float)      l4 (float) harmonic_xabcd_eD(cpl1, cpl2, xY, aY, dY)   Measure closeness of D to either of the two closest PRZ levels, relative to height of the XA leg   Parameters:      cpl1 (float)      cpl2 (float)      xY (float)      aY (float)      dY (float) harmonic_xabcd_przScore(xY, aY, l1, l2, l3, l4)   Measure the closeness of the two closest PRZ levels, relative to the height of the XA leg   Parameters:      xY (float)      aY (float)      l1 (float)      l2 (float)      l3 (float)      l4 (float) harmonic_xabcd_rAndE(type, l, l1, l2)   Get the ratio of two pattern legs, and the percent error from the theoretical harmonic ratio Order of arguments does not matter   Parameters:      type (int) : Harmonic pattern type (int - 1 = Gartley, 2 = Bat, 3 = Butterfly, 4 = Crab)      l (string) : Leg ID ("xab", "abc", "bcd", or "xad") (string)      l1 (float) : Line 1 height (float)      l2 (float) : Line 2 height (float)   Returns: harmonic_xabcd_eAvg(xbre, acre, bdre, xdre, xcdre)   Get the avg retracement ratio % error   Parameters:      xbre (float)      acre (float)      bdre (float)      xdre (float)      xcdre (float) pat_xabcd_asym(xX, aX, bX, cX, dX)   Get the avg asymmetry %   Parameters:      xX (int)      aX (int)      bX (int)      cX (int)      dX (int) harmonic_xabcd_entry(t, tp, xY, aY, bY, cY, dY, e_afterC, e_lvlc, e_afterD, e_lvldPct)   Get potential entry levels for a harmonic XABCD pattern   Parameters:      t (bool)      tp (int)      xY (float)      aY (float)      bY (float)      cY (float)      dY (float)      e_afterC (bool)      e_lvlc (string)      e_afterD (bool)      e_lvldPct (float) xabcd_entryHit(t, afterC, afterD, dX, e_afterC, e_afterD, dValBars)   Determine if entry level was reached. Assumes pattern is active/not timed out.   Parameters:      t (bool)      afterC (float)      afterD (float)      dX (int)      e_afterC (bool)      e_afterD (bool)      dValBars (int) pat_xabcd_validate(xX, xY, aX, aY, bX, bY, cX, cY, dX, dY, xab, abc, bcd, xad, xcd, pErr, pAsym)   Validate custom XABCD pattern   Parameters:      xX (int) : X coordinate of point X (int)      xY (float) : Y coordinate of point X (float)      aX (int) : X coordinate of point A (int)      aY (float) : Y coordinate of point A (float)      bX (int) : X coordinate of point B (int)      bY (float) : Y coordinate of point B (float)      cX (int) : X coordinate of point C (int)      cY (float) : Y coordinate of point C (float)      dX (int) : X coordinate of point D (int)      dY (float) : Y coordinate of point D (float)      xab (float)      abc (float)      bcd (float)      xad (float)      xcd (float)      pErr (float) : Acceptable percent error of leg ratios (does not apply to legs defined within a range) (float)      pAsym (float) : Acceptable percent asymmetry of leg ΔX (each leg tested against average ΔX of prior legs) (float)   Returns: TRUE if pattern is valid pat_xabcd_validateIncomplete(xX, xY, aX, aY, bX, bY, cX, cY, xab, abc, pErr, pAsym)   Validate the first 3 legs of a custom XABCD pattern   Parameters:      xX (int) : X coordinate of point X (int)      xY (float) : Y coordinate of point X (float)      aX (int) : X coordinate of point A (int)      aY (float) : Y coordinate of point A (float)      bX (int) : X coordinate of point B (int)      bY (float) : Y coordinate of point B (float)      cX (int) : X coordinate of point C (int)      cY (float) : Y coordinate of point C (float)      xab (float)      abc (float)      pErr (float) : Acceptable percent error of leg ratios (does not apply to legs defined within a range) (float)      pAsym (float) : Acceptable percent asymmetry of leg ΔX (each leg tested against average ΔX of prior legs) (float)   Returns: TRUE if first 3 legs are valid pat_xabcd_prz(xY, aY, bY, cY, xad, bcd, xcd)   Get the potential reversal zone (PRZ) levels of a custom XABCD pattern   Parameters:      xY (float) : Y coordinate of point X (float)      aY (float) : Y coordinate of point A (float)      bY (float) : Y coordinate of point B (float)      cY (float) : Y coordinate of point C (float)      xad (float)      bcd (float)      xcd (float)   Returns: pat_xabcd_avgDev(xX, xY, aX, aY, bX, bY, cX, cY, dX, dY)   Get the average deviation of an XABCD pattern   Parameters:      xX (int)      xY (float)      aX (int)      aY (float)      bX (int)      bY (float)      cX (int)      cY (float)      dX (int)      dY (float) harmonic_xabcd_score(tp, xX, xY, aX, aY, bX, bY, cX, cY, dX, dY)   Get score values for a pattern   Parameters:      tp (int)      xX (int)      xY (float)      aX (int)      aY (float)      bX (int)      bY (float)      cX (int)      cY (float)      dX (int)      dY (float) harmonic_xabcd_scoreTot(asym, eavg, przscore, eD, tp, w_a, w_e, w_p, w_d)   Get total weighted score value for a pattern   Parameters:      asym (float)      eavg (float)      przscore (float)      eD (float)      tp (int)      w_a (float)      w_e (float)      w_p (float)      w_d (float) harmonic_xabcd_targets(xY, aY, bY, cY, dY, tgt1, tgt2, tgt3)   Get target level   Parameters:      xY (float)      aY (float)      bY (float)      cY (float)      dY (float)      tgt1 (string)      tgt2 (string)      tgt3 (string) harmonic_xabcd_stop(stop, stopPct, bull, xY, dY, upper, lower, t1, eY)   Get stop level   Parameters:      stop (string)      stopPct (float)      bull (bool)      xY (float)      dY (float)      upper (float)      lower (float)      t1 (float)      eY (float) harmonic_xabcd_fibDispTxt(tp)   Get fib ratio display text   Parameters:      tp (int) harmonic_xabcd_symbol(tp)   Get pattern symbol   Parameters:      tp (int) pat_xabcd(x_is_low, pivot_length, source, conf_length, incomplete)   Determine if an XABCD pattern has just completed (i.e. point D is on the previous bar)   Parameters:      x_is_low (bool) : Flag to determine if point X is a low pivot, i.e. bullish pattern (bool, dft = true)      pivot_length (int) : Number of bars before and after a valid pivot (int, dft = 5)      source (float) : Source series (float, dft = na, will use high and low series)      conf_length (int) : Number of trailing bars after pivot point D to confirm a valid pattern (int, dft = 1)      incomplete (bool) : Flag to return an incomplete XABC pattern (bool, dft = false)   Returns: flag = true if valid XABCD pattern completed on previous bar xx = X coordinate of point X (int) xy = Y coordinate of point X (float) ax = X coordinate of point A (int) ay = Y coordinate of point A (float) bx = X coordinate of point B (int) by = Y coordinate of point B (float) cx = X coordinate of point C (int) cy = Y coordinate of point C (float) dx = X coordinate of point D (int) dy = Y coordinate of point D (float) pat_xabcdIncomplete(x_is_low, pivot_length, source, conf_length)   Determine if an XABCD pattern is in progress (point C was just confirmed)   Parameters:      x_is_low (bool) : Flag to determine if point X is a low pivot, i.e. bullish M pattern (bool, dft = true)      pivot_length (int) : Number of bars before and after a valid pivot (int, dft = 5)      source (float) : Source series (float, dft = na, will use high and low series)      conf_length (int) : Number of trailing bars after pivot point D to confirm a valid pattern (int, dft = 1)   Returns: flag = true if valid XABC pattern completed on bar_index xx = X coordinate of point X (int) xy = Y coordinate of point X (float) ax = X coordinate of point A (int) ay = Y coordinate of point A (float) bx = X coordinate of point B (int) by = Y coordinate of point B (float) cx = X coordinate of point C (int) cy = Y coordinate of point C (float) dx = X coordinate of point D (int) dy = Y coordinate of point D (float) success(eX, stop, t1, t2)   Determine if trade is successful   Parameters:      eX (int) : Entry bar index (int)      stop (float) : Stop level (float)      t1 (float) : Target 1 level (float)      t2 (float) : Target 2 level (float)   Returns: tradeClosed(eX, eY, stop, t1h, t2h, t1, t2)   Determine if Target or Stop was hit on the current bar   Parameters:      eX (int)      eY (float)      stop (float)      t1h (bool)      t2h (bool)      t1 (float)      t2 (float)
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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 (float) : length of leg a (float)      b (float) : length of leg b (float)      c (float) : length of hypotenuse (float)      deg (simple bool) : 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 (int) : x coordinate 1 (int - optional, required if argument l is not specified)      y1 (float) : y coordinate 1 (float - optional, required if argument l is not specified)      x2 (int) : x coordinate 2 (int - optional, required if argument l is not specified)      y2 (float) : y coordinate 2 (float - optional, required if argument l is not specified)      l (line) : line object (line - optional, required if x1, y1, x2, and y2 agruments are not specified)      deg (simple bool) : 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 (float) : length of leg a (float)      b (float) : length of leg b (float)      c (float) : length of hypotenuse (float)      deg (simple bool) : 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 (int) : x coordinate 1 (int - optional, required if argument l is not specified)      y1 (float) : y coordinate 1 (float - optional, required if argument l is not specified)      x2 (int) : x coordinate 2 (int - optional, required if argument l is not specified)      y2 (float) : y coordinate 2 (float - optional, required if argument l is not specified)      l (line) : line object (line - optional, required if x1, y1, x2, and y2 agruments are not specified)      deg (simple bool) : 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" line_fromXy(x1, y1, x2, y2)   Get line slope and y-intercept from coordinates   Parameters:      x1 (int) : x coordinate 1 (int - bar index)      y1 (float) : y coordinate 1 (float - price/value)      x2 (int) : x coordinate 2 (int - bar index)      y2 (float) : y coordinate 2 (float - price/value)   Returns: of line line_getPrice(x, slope, yInt)   Get price at X coordinate, given line slope and y-intercept   Parameters:      x (int) : x coordinate to solve for y (int - bar index)      slope (float) : slope of line (float)      yInt (float) : y-intercept of line (float)   Returns: y (price/value) line_getPrice_fromXy(x, x1, y1, x2, y2)   Get price at X coordinate, given two points on a line   Parameters:      x (int) : x coordinate to solve for y (int - bar index)      x1 (int) : x coordinate 1 (int - bar index)      y1 (float) : y coordinate 1 (float - price/value)      x2 (int) : x coordinate 2 (int - bar index)      y2 (float) : y coordinate 2 (float - price/value)   Returns: y (price/value) line_getRtSides(x1, y1, x2, y2, l)   Get length of sides of a right triangle formed by a given line   Parameters:      x1 (int) : x coordinate 1 (int - optional, required if argument l is not specified)      y1 (float) : y coordinate 1 (float - optional, required if argument l is not specified)      x2 (int) : x coordinate 2 (int - optional, required if argument l is not specified)      y2 (float) : y coordinate 2 (float - optional, required if argument l is not specified)      l (line) : line object (line - optional, required if x1, y1, x2, y2 agruments are not specified)   Returns: line_length(x1, y1, x2, y2, l)   Get length of line, given a line object or two sets of coordinates   Parameters:      x1 (int) : x coordinate 1 (int - optional, required if argument l is not specified)      y1 (float) : y coordinate 1 (float - optional, required if argument l is not specified)      x2 (int) : x coordinate 2 (int - optional, required if argument l is not specified)      y2 (float) : y coordinate 2 (float - optional, required if argument l is not specified)      l (line) : line object (line - optional, required if x1, y1, x2, y2 agruments are not specified)   Returns: length of line (float)
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FibonacciLibrary "Fibonacci" General Fibonacci functions. Get fib numbers, ratios, etc. fib_derived(f, precision)   Get the precise Fibonacci ratio, to the specified number of decimal places   Parameters:      f (float) : Fibonacci ratio (string, in form #.###)      precision (simple int) : Number of decimal places (optional int, dft = 16, max = 32)   Returns: Precise Fibonacci ratio (float) * Deprecated (use fib_precise() instead), but keeping it here for science / experimenting with derivations fib_precise(f, precision)   Get the precise Fibonacci ratio, to the specified number of decimal places   Parameters:      f (float) : Fibonacci ratio (string, in form #.###)      precision (simple int) : Number of decimal places (optional int, dft = 16, max = 16)   Returns: Precise Fibonacci ratio (float) fib_from_string(r)   Get fib ratio value from string   Parameters:      r (string) : Fib ratio string (e.g. ".618")   Returns: Fibonacci ratio value (float) fib_n(n)   Calculate the Nth number in the Fibonacci sequence   Parameters:      n (int) : Index/number in sequence (int)   Returns: Fibonacci number (int)
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UtilitiesLibrary "Utilities" General utilities print_series(s, skip_na, position, show_index, from_index, to_index)   Print series values   Parameters:      s (string) : Series (string)      skip_na (simple bool) : Flag to skip na values (optional bool, dft = false)      position (simple string) : Position to print the Table (optional string, dft = position.bottom_center)      show_index (simple bool) : Flag to show series indices (optional bool, dft = true)      from_index (int) : First index to print (optional int, dft = 0)      to_index (int) : Last index to print (optional int, dft = last_bar_index)   Returns: Table object, if series was printed print(v, position, at_index)   Print value   Parameters:      v (string) : Value (string)      position (simple string) : Position to print the Table (optional string, dft = position.bottom_center)      at_index (int) : Index at which to print (optional int, dft = bar_index)   Returns: Table object, if value was printed print(v, position, at_index)   Print value   Parameters:      v (int) : Value (int)      position (simple string) : Position to print the Table (optional string, dft = position.bottom_center)      at_index (int) : Index at which to print (optional int, dft = bar_index)   Returns: Table object, if value was printed print(v, position, at_index)   Print value   Parameters:      v (float) : Value (float)      position (simple string) : Position to print the Table (optional string, dft = position.bottom_center)      at_index (int) : Index at which to print (optional int, dft = bar_index)   Returns: Table object, if value was printed print(v, position, at_index)   Print value   Parameters:      v (bool) : Value (bool)      position (simple string) : Position to print the Table (optional string, dft = position.bottom_center)      at_index (int) : Index at which to print (optional int, dft = bar_index)   Returns: Table object, if value was printed boolToIntArr(a)   return array of offsets (int) of true values   Parameters:      a (array) intToBoolArr(a, n)   Parameters:      a (array)      n (int)
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LogNormalLibrary "LogNormal" A collection of functions used to model skewed distributions as log-normal. Prices are commonly modeled using log-normal distributions (ie. Black-Scholes) because they exhibit multiplicative changes with long tails; skewed exponential growth and high variance. This approach is particularly useful for understanding price behavior and estimating risk, assuming continuously compounding returns are normally distributed. Because log space analysis is not as direct as using math.log(price) , this library extends the Error Functions library to make working with log-normally distributed data as simple as possible. - - - QUICK START Import library into your project Initialize model with a mean and standard deviation Pass model params between methods to compute various properties var LogNorm model = LN.init(arr.avg(), arr.stdev()) // Assumes the library is imported as LN var mode = model.mode() Outputs from the model can be adjusted to better fit the data. var Quantile data = arr.quantiles() var more_accurate_mode = mode.fit(model, data) // Fits value from model to data Inputs to the model can also be adjusted to better fit the data. datum = 123.45 model_equivalent_datum = datum.fit(data, model) // Fits value from data to the model area_from_zero_to_datum = model.cdf(model_equivalent_datum) - - - TYPES There are two requisite UDTs: LogNorm and Quantile . They are used to pass parameters between functions and are set automatically (see Type Management ). LogNorm   Object for log space parameters and linear space quantiles .   Fields:      mu (float) : Log space mu ( µ ).      sigma (float) : Log space sigma ( σ ).      variance (float) : Log space variance ( σ² ).      quantiles (Quantile) : Linear space quantiles. Quantile   Object for linear quantiles, most similar to a seven-number summary .   Fields:      Q0 (float) : Smallest Value      LW (float) : Lower Whisker Endpoint      LC (float) : Lower Whisker Crosshatch      Q1 (float) : First Quartile      Q2 (float) : Second Quartile      Q3 (float) : Third Quartile      UC (float) : Upper Whisker Crosshatch      UW (float) : Upper Whisker Endpoint      Q4 (float) : Largest Value      IQR (float) : Interquartile Range      MH (float) : Midhinge      TM (float) : Trimean      MR (float) : Mid-Range - - - TYPE MANAGEMENT These functions reliably initialize and update the UDTs. Because parameterization is interdependent, avoid setting the LogNorm and Quantile fields directly . init(mean, stdev, variance)   Initializes a LogNorm object.   Parameters:      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object set(ln, mean, stdev, variance)   Transforms linear measurements into log space parameters for a LogNorm object.   Parameters:      ln (LogNorm) : Object containing log space parameters.      mean (float) : Linearly measured mean.      stdev (float) : Linearly measured standard deviation.      variance (float) : Linearly measured variance.   Returns: LogNorm Object quantiles(arr)   Gets empirical quantiles from an array of floats.   Parameters:      arr (array) : Float array object.   Returns: Quantile Object - - - DESCRIPTIVE STATISTICS Using only the initialized LogNorm parameters, these functions compute a model's central tendency and standardized moments. mean(ln)   Computes the linear mean from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ median(ln)   Computes the linear median from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ mode(ln)   Computes the linear mode from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ variance(ln)   Computes the linear variance from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ skewness(ln)   Computes the linear skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ kurtosis(ln, excess)   Computes the linear kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Kurtosis (true) or regular Kurtosis (false).   Returns: Between 0 and ∞ hyper_skewness(ln)   Computes the linear hyper skewness from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.   Returns: Between 0 and ∞ hyper_kurtosis(ln, excess)   Computes the linear hyper kurtosis from log space parameters.   Parameters:      ln (LogNorm) : Object containing log space parameters.      excess (bool) : Excess Hyper Kurtosis (true) or regular Hyper Kurtosis (false).   Returns: Between 0 and ∞ - - - DISTRIBUTION FUNCTIONS These wrap Gaussian functions to make working with model space more direct. Because they are contained within a log-normal library, they describe estimations relative to a log-normal curve, even though they fundamentally measure a Gaussian curve. pdf(ln, x, empirical_quantiles)   A Probability Density Function estimates the probability density . For clarity, density is not a probability .   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate for which a density will be estimated.      empirical_quantiles (Quantile) : Quantiles as observed in the data (optional).   Returns: Between 0 and ∞ cdf(ln, x, precise)   A Cumulative Distribution Function estimates the area under a Log-Normal curve between Zero and a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ccdf(ln, x, precise)   A Complementary Cumulative Distribution Function estimates the area under a Log-Normal curve between a linear X coordinate and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 cdfinv(ln, a, precise)   An Inverse Cumulative Distribution Function reverses the Log-Normal cdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ ccdfinv(ln, a, precise)   An Inverse Complementary Cumulative Distribution Function reverses the Log-Normal ccdf() by estimating the linear X coordinate from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ cdfab(ln, x1, x2, precise)   A Cumulative Distribution Function from A to B estimates the area under a Log-Normal curve between two linear X coordinates (A and B).   Parameters:      ln (LogNorm) : Object of log space parameters.      x1 (float) : First linear X coordinate .      x2 (float) : Second linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ott(ln, x, precise)   A One-Tailed Test transforms a linear X coordinate into an absolute Z Score before estimating the area under a Log-Normal curve between Z and Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 0.5 ttt(ln, x, precise)    A Two-Tailed Test transforms a linear X coordinate into symmetrical ± Z Scores before estimating the area under a Log-Normal curve from Zero to -Z, and +Z to Infinity.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 ottinv(ln, a, precise)   An Inverse One-Tailed Test reverses the Log-Normal ott() by estimating a linear X coordinate for the right tail from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Half a normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ tttinv(ln, a, precise)   An Inverse Two-Tailed Test reverses the Log-Normal ttt() by estimating two linear X coordinates from an area.   Parameters:      ln (LogNorm) : Object of log space parameters.      a (float) : Normalized area .      precise (bool) : Double precision (true) or single precision (false).   Returns: Linear space tuple : - - - UNCERTAINTY Model-based measures of uncertainty, information, and risk. sterr(sample_size, fisher_info)   The standard error of a sample statistic.   Parameters:      sample_size (float) : Number of observations.      fisher_info (float) : Fisher information.   Returns: Between 0 and ∞ surprisal(p, base)   Quantifies the information content of a single event.   Parameters:      p (float) : Probability of the event .      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ entropy(ln, base)   Computes the differential entropy (average surprisal).   Parameters:      ln (LogNorm) : Object of log space parameters.      base (float) : Logarithmic base (optional).   Returns: Between 0 and ∞ perplexity(ln, base)   Computes the average number of distinguishable outcomes from the entropy.     Parameters:      ln (LogNorm)      base (float) : Logarithmic base used for Entropy (optional).   Returns: Between 0 and ∞ value_at_risk(ln, p, precise)   Estimates a risk threshold under normal market conditions for a given confidence level.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ value_at_risk_inv(ln, value_at_risk, precise)   Reverses the value_at_risk() by estimating the confidence level from the risk threshold.   Parameters:      ln (LogNorm) : Object of log space parameters.      value_at_risk (float) : Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 conditional_value_at_risk(ln, p, precise)   Estimates the average loss beyond a confidence level, aka. expected shortfall.   Parameters:      ln (LogNorm) : Object of log space parameters.      p (float) : Probability threshold, aka. the confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_value_at_risk_inv(ln, conditional_value_at_risk, precise)   Reverses the conditional_value_at_risk() by estimating the confidence level of an average loss.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_value_at_risk (float) : Conditional Value at Risk.      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and 1 partial_expectation(ln, x, precise)   Estimates the partial expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and µ partial_expectation_inv(ln, partial_expectation, precise)   Reverses the partial_expectation() by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      partial_expectation (float) : Partial Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ conditional_expectation(ln, x, precise)   Estimates the conditional expectation of a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between X and ∞ conditional_expectation_inv(ln, conditional_expectation, precise)   Reverses the conditional_expectation by estimating a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      conditional_expectation (float) : Conditional Expectation .      precise (bool) : Double precision (true) or single precision (false).   Returns: Between 0 and ∞ fisher(ln, log)   Computes the Fisher Information Matrix for the distribution, not a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the distribution fisher(ln, x, log)   Computes the Fisher Information Matrix for a linear X coordinate, not the distribution itself.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      log (bool) : Sets if the matrix should be in log (true) or linear (false) space.   Returns: FIM for the linear X coordinate confidence_interval(ln, x, sample_size, confidence, precise)   Estimates a confidence interval for a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate .      sample_size (float) : Number of observations.      confidence (float) : Confidence level .      precise (bool) : Double precision (true) or single precision (false).   Returns: CI for the linear X coordinate - - - CURVE FITTING An overloaded function that helps transform values between spaces. The primary function uses quantiles, and the overloads wrap the primary function to make working with LogNorm more direct. fit(x, a, b)   Transforms X coordinate between spaces A and B.   Parameters:      x (float) : Linear X coordinate from space A .      a (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.      b (LogNorm | Quantile | array) : LogNorm, Quantile, or float array.   Returns: Adjusted X coordinate - - - EXPORTED HELPERS Small utilities to simplify extensibility. z_score(ln, x)   Converts a linear X coordinate into a Z Score.   Parameters:      ln (LogNorm) : Object of log space parameters.      x (float) : Linear X coordinate.   Returns: Between -∞ and +∞ x_coord(ln, z)   Converts a Z Score into a linear X coordinate.   Parameters:      ln (LogNorm) : Object of log space parameters.      z (float) : Standard normal Z Score.   Returns: Between 0 and ∞ iget(arr, index)   Gets an interpolated value of a pseudo -element (fictional element between real array elements). Useful for quantile mapping.   Parameters:      arr (array) : Float array object.      index (float) : Index of the pseudo element.   Returns: Interpolated value of the arrays pseudo element.
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ICOptimizerLibrary "ICOptimizer" Library for IC-based parameter optimization findOptimalParam(testParams, icValues, currentParam, smoothing)   Find optimal parameter from array of IC values   Parameters:      testParams (array) : Array of parameter values being tested      icValues (array) : Array of IC values for each parameter (same size as testParams)      currentParam (float) : Current parameter value (for smoothing)      smoothing (simple float) : Smoothing factor (0-1, e.g., 0.2 means 20% new, 80% old)   Returns: New parameter value, its IC, and array index adaptiveParamWithStarvation(opt, testParams, icValues, smoothing, starvationThreshold, starvationJumpSize)   Adaptive parameter selection with starvation handling   Parameters:      opt (ICOptimizer) : ICOptimizer object      testParams (array) : Array of parameter values      icValues (array) : Array of IC values for each parameter      smoothing (simple float) : Normal smoothing factor      starvationThreshold (simple int) : Number of updates before triggering starvation mode      starvationJumpSize (simple float) : Jump size when in starvation (as fraction of range)   Returns: Updated parameter and IC detectAndAdjustDomination(longCount, shortCount, currentLongLevel, currentShortLevel, dominationRatio, jumpSize, minLevel, maxLevel)   Detect signal imbalance and adjust parameters   Parameters:      longCount (int) : Number of long signals in period      shortCount (int) : Number of short signals in period      currentLongLevel (float) : Current long threshold      currentShortLevel (float) : Current short threshold      dominationRatio (simple int) : Ratio threshold (e.g., 4 = 4:1 imbalance)      jumpSize (simple float) : Size of adjustment      minLevel (simple float) : Minimum allowed level      maxLevel (simple float) : Maximum allowed level   Returns: calcIC(signals, returns, lookback)   Parameters:      signals (float)      returns (float)      lookback (simple int) classifyIC(currentIC, icWindow, goodPercentile, badPercentile)   Parameters:      currentIC (float)      icWindow (simple int)      goodPercentile (simple int)      badPercentile (simple int) evaluateSignal(signal, forwardReturn)   Parameters:      signal (float)      forwardReturn (float) updateOptimizerState(opt, signal, forwardReturn, currentIC, metaICPeriod)   Parameters:      opt (ICOptimizer)      signal (float)      forwardReturn (float)      currentIC (float)      metaICPeriod (simple int) calcSuccessRate(successful, total)   Parameters:      successful (int)      total (int) createICStatsTable(opt, paramName, normalSuccess, normalTotal)   Parameters:      opt (ICOptimizer)      paramName (string)      normalSuccess (int)      normalTotal (int) initOptimizer(initialParam)   Parameters:      initialParam (float) ICOptimizer   Fields:      currentParam (series float)      currentIC (series float)      metaIC (series float)      totalSignals (series int)      successfulSignals (series int)      goodICSignals (series int)      goodICSuccess (series int)      nonBadICSignals (series int)      nonBadICSuccess (series int)      goodICThreshold (series float)      badICThreshold (series float)      updateCounter (series int)
Biblioteca Pine Script®
por cybermediaboy
IC optimiser libLibrary "IC optimiser lib" Library for IC-based parameter optimization findOptimalParam(testParams, icValues, currentParam, smoothing)   Find optimal parameter from array of IC values   Parameters:      testParams (array) : Array of parameter values being tested      icValues (array) : Array of IC values for each parameter (same size as testParams)      currentParam (float) : Current parameter value (for smoothing)      smoothing (simple float) : Smoothing factor (0-1, e.g., 0.2 means 20% new, 80% old)   Returns: New parameter value, its IC, and array index adaptiveParamWithStarvation(opt, testParams, icValues, smoothing, starvationThreshold, starvationJumpSize)   Adaptive parameter selection with starvation handling   Parameters:      opt (ICOptimizer) : ICOptimizer object      testParams (array) : Array of parameter values      icValues (array) : Array of IC values for each parameter      smoothing (simple float) : Normal smoothing factor      starvationThreshold (simple int) : Number of updates before triggering starvation mode      starvationJumpSize (simple float) : Jump size when in starvation (as fraction of range)   Returns: Updated parameter and IC detectAndAdjustDomination(longCount, shortCount, currentLongLevel, currentShortLevel, dominationRatio, jumpSize, minLevel, maxLevel)   Detect signal imbalance and adjust parameters   Parameters:      longCount (int) : Number of long signals in period      shortCount (int) : Number of short signals in period      currentLongLevel (float) : Current long threshold      currentShortLevel (float) : Current short threshold      dominationRatio (simple int) : Ratio threshold (e.g., 4 = 4:1 imbalance)      jumpSize (simple float) : Size of adjustment      minLevel (simple float) : Minimum allowed level      maxLevel (simple float) : Maximum allowed level   Returns: calcIC(signals, returns, lookback)   Parameters:      signals (float)      returns (float)      lookback (simple int) classifyIC(currentIC, icWindow, goodPercentile, badPercentile)   Parameters:      currentIC (float)      icWindow (simple int)      goodPercentile (simple int)      badPercentile (simple int) evaluateSignal(signal, forwardReturn)   Parameters:      signal (float)      forwardReturn (float) updateOptimizerState(opt, signal, forwardReturn, currentIC, metaICPeriod)   Parameters:      opt (ICOptimizer)      signal (float)      forwardReturn (float)      currentIC (float)      metaICPeriod (simple int) calcSuccessRate(successful, total)   Parameters:      successful (int)      total (int) createICStatsTable(opt, paramName, normalSuccess, normalTotal)   Parameters:      opt (ICOptimizer)      paramName (string)      normalSuccess (int)      normalTotal (int) initOptimizer(initialParam)   Parameters:      initialParam (float) ICOptimizer   Fields:      currentParam (series float)      currentIC (series float)      metaIC (series float)      totalSignals (series int)      successfulSignals (series int)      goodICSignals (series int)      goodICSuccess (series int)      nonBadICSignals (series int)      nonBadICSuccess (series int)      goodICThreshold (series float)      badICThreshold (series float)      updateCounter (series int)
Biblioteca Pine Script®
por cybermediaboy