matrixCoreLibrary "matrixCore"
analyzeCandleStructure(o, h, l, c, atr, smallBodyThreshold, longWickRatio)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
smallBodyThreshold (float)
longWickRatio (float)
isRedRejectionCandle(o, l, c, redRejectionWickMin)
Parameters:
o (float)
l (float)
c (float)
redRejectionWickMin (float)
isEqual(a, b, tol)
Parameters:
a (float)
b (float)
tol (float)
detectPattern(kf, km, ks, tol)
Parameters:
kf (float)
km (float)
ks (float)
tol (float)
calculateStateHash(kp, ep, pp, comp, cType, slope, tfH, tfL, redRej)
Parameters:
kp (int)
ep (int)
pp (int)
comp (int)
cType (int)
slope (int)
tfH (bool)
tfL (bool)
redRej (bool)
createStateConfig(kp, ep, pp, comp, cType, slope, tfH, tfL, redRej, hash)
Parameters:
kp (int)
ep (int)
pp (int)
comp (int)
cType (int)
slope (int)
tfH (bool)
tfL (bool)
redRej (bool)
hash (int)
createBarSnapshot(barIdx, o, h, l, c, atr, ema, stateHash, kp, ep, pp, comp, cType, slope, tfH, tfL, redRej)
Parameters:
barIdx (int)
o (float)
h (float)
l (float)
c (float)
atr (float)
ema (float)
stateHash (int)
kp (int)
ep (int)
pp (int)
comp (int)
cType (int)
slope (int)
tfH (bool)
tfL (bool)
redRej (bool)
stateToString(cfg)
Parameters:
cfg (StateConfig)
getTablePosition(pos)
Parameters:
pos (string)
getGradientColor(value, minVal, maxVal)
Parameters:
value (float)
minVal (float)
maxVal (float)
StateConfig
Fields:
kijunPattern (series int)
emaPattern (series int)
pricePos (series int)
compression (series int)
candleType (series int)
emaSlope (series int)
tfHigherBullish (series bool)
tfLowerBullish (series bool)
redRejection (series bool)
hash (series int)
BarSnapshot
Fields:
barIndex (series int)
openPrice (series float)
highPrice (series float)
lowPrice (series float)
closePrice (series float)
atr (series float)
emaFast (series float)
stateHash (series int)
kijunPattern (series int)
emaPattern (series int)
pricePos (series int)
compression (series int)
candleType (series int)
emaSlope (series int)
tfHigherBullish (series bool)
tfLowerBullish (series bool)
redRejection (series bool)
Индикаторы и стратегии
labConfigCoreLibrary "labConfigCore"
Configuration centrale pour tous les indicateurs LAB (Kijun + EMA + S/R)
Centralise les valeurs par défaut pour éviter les désynchronisations entre indicateurs
getTFFast()
Retourne le timeframe Fast par défaut
Returns: String - Timeframe Fast ("15" par défaut)
getTFMid()
Retourne le timeframe Mid par défaut
Returns: String - Timeframe Mid ("60" par défaut)
getTFSlow()
Retourne le timeframe Slow par défaut
Returns: String - Timeframe Slow ("240" par défaut)
getTFATR()
Retourne le timeframe ATR par défaut
Returns: String - Timeframe ATR ("60" par défaut)
getKijunLength()
Retourne la longueur Kijun par défaut
Returns: Int - Longueur Kijun (26 par défaut)
getShowKijun()
Retourne l'état d'affichage Kijun par défaut
Returns: Bool - Afficher Kijun (true par défaut)
getEMALength()
Retourne la longueur EMA par défaut
Returns: Int - Longueur EMA (50 par défaut)
getChannelWidth()
Retourne la largeur des canaux EMA par défaut
Returns: Float - Largeur canal en % (2.0 par défaut)
getChannelAlpha()
Retourne la transparence des canaux par défaut
Returns: Int - Alpha channel (93 par défaut)
getShowChannelFast()
Retourne l'état d'affichage canal Fast par défaut
Returns: Bool - Afficher canal Fast (false par défaut)
getShowChannelMid()
Retourne l'état d'affichage canal Mid par défaut
Returns: Bool - Afficher canal Mid (true par défaut)
getShowChannelSlow()
Retourne l'état d'affichage canal Slow par défaut
Returns: Bool - Afficher canal Slow (true par défaut)
getChannelColorFast()
Retourne la couleur canal Fast par défaut
Returns: Color - Couleur Fast (#00BCD4 par défaut)
getChannelColorMid()
Retourne la couleur canal Mid par défaut
Returns: Color - Couleur Mid (#4CAF50 par défaut)
getChannelColorSlow()
Retourne la couleur canal Slow par défaut
Returns: Color - Couleur Slow (#FF9800 par défaut)
getSRDisplayMode()
Retourne le mode d'affichage S/R par défaut
Returns: String - Mode ('S/R Zones' par défaut)
getSRTimeframe()
Retourne le timeframe S/R par défaut
Returns: String - Timeframe S/R ('Chart' par défaut)
getSRVolMA()
Retourne la longueur Volume MA par défaut
Returns: Int - Longueur Vol MA (6 par défaut)
getSRNumZones()
Retourne le nombre de zones back par défaut
Returns: Int - Zones back (10 par défaut)
getSRExtendLines()
Retourne l'état extend lines par défaut
Returns: Bool - Extend to next zone (true par défaut)
getSRExtendActive()
Retourne l'état extend active par défaut
Returns: Bool - Extend active right (true par défaut)
getSRExtendRight()
Retourne l'état extend right par défaut
Returns: Bool - Extend right (false par défaut)
getSRShowLabels()
Retourne l'état affichage labels par défaut
Returns: Bool - Show labels (true par défaut)
getSRLabelLocation()
Retourne la position labels par défaut
Returns: String - Position ('Right' par défaut)
getSRLabelOffset()
Retourne l'offset labels par défaut
Returns: Int - Offset (15 par défaut)
getSRShowHL()
Retourne l'état affichage H/L par défaut
Returns: Bool - Show H/L lines (true par défaut)
getSRShowOC()
Retourne l'état affichage O/C par défaut
Returns: Bool - Show O/C lines (true par défaut)
getSRLineStyleHL()
Retourne le style ligne H/L par défaut
Returns: String - Style ('Solid' par défaut)
getSRLineWidthHL()
Retourne l'épaisseur ligne H/L par défaut
Returns: Int - Width (1 par défaut)
getSRLineStyleOC()
Retourne le style ligne O/C par défaut
Returns: String - Style ('Solid' par défaut)
getSRLineWidthOC()
Retourne l'épaisseur ligne O/C par défaut
Returns: Int - Width (1 par défaut)
getSRResLinesColor()
Retourne la couleur lignes résistance par défaut
Returns: Color - Couleur (rouge 20% transparent)
getSRResZoneColor()
Retourne la couleur zone résistance par défaut
Returns: Color - Couleur (rouge 90% transparent)
getSRSupLinesColor()
Retourne la couleur lignes support par défaut
Returns: Color - Couleur (lime 20% transparent)
getSRSupZoneColor()
Retourne la couleur zone support par défaut
Returns: Color - Couleur (lime 90% transparent)
getNormMode()
Retourne le mode de normalisation par défaut
Returns: String - Mode ('ATR' par défaut)
getATRLength()
Retourne la longueur ATR par défaut
Returns: Int - Longueur ATR (14 par défaut)
getTolEqual()
Retourne la tolérance égalité par défaut
Returns: Float - Tolérance (0.10 par défaut)
getThresholdTight()
Retourne le seuil compression par défaut
Returns: Float - Seuil tight (0.25 par défaut)
getThresholdWide()
Retourne le seuil extension par défaut
Returns: Float - Seuil wide (1.50 par défaut)
getShowDashboard()
Retourne l'état d'affichage dashboard par défaut
Returns: Bool - Show table (true par défaut)
getDashboardPosition()
Retourne la position dashboard par défaut
Returns: String - Position ('Top Right' par défaut)
getDashboardSize()
Retourne la taille dashboard par défaut
Returns: String - Size ('Small' par défaut)
getProbBarsForward()
Retourne le nombre de barres forward par défaut
Returns: Int - Bars forward (20 par défaut)
getProbWinThreshold()
Retourne le seuil win par défaut
Returns: Float - Win threshold % (1.5 par défaut)
getProbLossThreshold()
Retourne le seuil loss par défaut
Returns: Float - Loss threshold % (1.0 par défaut)
getProbHistorySize()
Retourne la taille historique par défaut
Returns: Int - History size (500 par défaut)
getStrategyTPSLMode()
Retourne le mode TP/SL par défaut
Returns: String - Mode ('% Fixed' ou 'ATR Multiple')
getStrategyTPPercent()
Retourne le take profit % par défaut
Returns: Float - TP % (2.0 par défaut)
getStrategySLPercent()
Retourne le stop loss % par défaut
Returns: Float - SL % (1.0 par défaut)
getStrategyTPATRMultiple()
Retourne le multiple ATR pour TP par défaut
Returns: Float - TP ATR multiple (2.0 par défaut)
getStrategySLATRMultiple()
Retourne le multiple ATR pour SL par défaut
Returns: Float - SL ATR multiple (1.5 par défaut)
getStrategyUseTrailing()
Retourne l'état trailing stop par défaut
Returns: Bool - Use trailing stop (false par défaut)
getStrategyTrailingActivation()
Retourne l'activation trailing % par défaut
Returns: Float - Trailing activation % (1.0 par défaut)
getStrategyTrailingOffset()
Retourne le trailing offset % par défaut
Returns: Float - Trailing offset % (0.5 par défaut)
getStrategyPositionSize()
Retourne la taille position par défaut
Returns: Float - Position size % equity (100 par défaut)
getStrategyInitialCapital()
Retourne le capital initial par défaut
Returns: Float - Initial capital (10000 par défaut)
getStrategyCommission()
Retourne la commission par défaut
Returns: Float - Commission % (0.1 par défaut)
getStrategySlippage()
Retourne le slippage par défaut
Returns: Int - Slippage ticks (2 par défaut)
getAllKijunDefaults()
Retourne TOUS les paramètres Kijun sous forme de tuple
Returns: - TF Fast, Mid, Slow, Length, Show
getAllEMADefaults()
Retourne TOUS les paramètres EMA sous forme de tuple
Returns: - Length, Width, Alpha, Show Fast, Mid, Slow
getAllNormDefaults()
Retourne TOUS les paramètres Normalisation sous forme de tuple
Returns: - Mode, TF ATR, Length, Tol, Tight, Wide
getAllDashboardDefaults()
Retourne TOUS les paramètres Dashboard sous forme de tuple
Returns: - Show, Position, Size
volSRCoreLibrary volSRCore
Library to compute volume-based support and resistance zones using fractal logic.
tfStringToFormat(tfInput)
Converts a timeframe string into Pine Script format.
Parameters:
tfInput (string): Timeframe string ("Chart", "1m", "5m", "1h", "D", etc.)
Returns:
string — Pine Script–formatted timeframe
resInMinutes()
Converts the current chart timeframe into minutes.
Returns:
float — number of minutes of the current timeframe
fractalUp(tfHigh, tfVol, tfVolMA)
Detects a bullish fractal (potential resistance).
Parameters:
tfHigh (float): High series of the timeframe
tfVol (float): Volume series of the timeframe
tfVolMA (float): Volume moving average series
Returns:
bool — true if a bullish fractal is detected
fractalDown(tfLow, tfVol, tfVolMA)
Detects a bearish fractal (potential support).
Parameters:
tfLow (float): Low series of the timeframe
tfVol (float): Volume series of the timeframe
tfVolMA (float): Volume moving average series
Returns:
bool — true if a bearish fractal is detected
calcFractalUpLevel(tfHigh, tfVol, tfVolMA)
Computes the resistance level from a bullish fractal.
Parameters:
tfHigh (float): High series
tfVol (float): Volume series
tfVolMA (float): Volume MA series
Returns:
float — resistance level
calcFractalDownLevel(tfLow, tfVol, tfVolMA)
Computes the support level from a bearish fractal.
Parameters:
tfLow (float): Low series
tfVol (float): Volume series
tfVolMA (float): Volume MA series
Returns:
float — support level
calcResistanceZone(tfHigh, tfOpen, tfClose, tfVol, tfVolMA)
Computes the resistance zone (between High and Open/Close).
Parameters:
tfHigh (float): High series
tfOpen (float): Open series
tfClose (float): Close series
tfVol (float): Volume series
tfVolMA (float): Volume MA series
Returns:
float — lower boundary of the resistance zone
calcSupportZone(tfLow, tfOpen, tfClose, tfVol, tfVolMA)
Computes the support zone (between Low and Open/Close).
Parameters:
tfLow (float): Low series
tfOpen (float): Open series
tfClose (float): Close series
tfVol (float): Volume series
tfVolMA (float): Volume MA series
Returns:
float — upper boundary of the support zone
tfNewBar(tfRes)
Detects a new bar on a given timeframe.
Parameters:
tfRes (simple string): Timeframe string
Returns:
bool — true if a new bar is detected
tfBarIndexBack(tfRes, barsBack)
Computes the bar_index N bars back on a target timeframe.
Parameters:
tfRes (simple string): Timeframe string
barsBack (simple int): Number of bars back (1, 3, 5, etc.)
Returns:
int — bar_index at that point in time
tfBarsRange(tfRes, startBar, endBar)
Computes the number of chart bars between two bars of a target timeframe.
Parameters:
tfRes (simple string): Timeframe string
startBar (simple int): Start bar (e.g., 1)
endBar (simple int): End bar (e.g., 5)
Returns:
int — number of chart bars in that range
calcPivotHighBarIndex(startBarsBack, rangeSize, maxBarsBack)
Finds the exact bar_index of the highest high within a given range.
Parameters:
startBarsBack (simple int): Start of the scan (bars back)
rangeSize (simple int): Size of the scan range
maxBarsBack (simple int): max_bars_back limit (e.g., 4999)
Returns:
int — bar_index of the highest high, or maxBarsBack if out of bounds
calcPivotLowBarIndex(startBarsBack, rangeSize, maxBarsBack)
Finds the exact bar_index of the lowest low within a given range.
Parameters:
startBarsBack (simple int): Start of the scan (bars back)
rangeSize (simple int): Size of the scan range
maxBarsBack (simple int): max_bars_back limit (e.g., 4999)
Returns:
int — bar_index of the lowest low, or maxBarsBack if out of bounds
detectPriceInteraction(resLevel, resZone, supLevel, supZone)
Detects price interactions with support/resistance zones.
Parameters:
resLevel (float): Resistance level
resZone (float): Resistance zone
supLevel (float): Support level
supZone (float): Support zone
Returns:
EntersResZone
TestsResAsSupport
EntersSupZone
TestsSupAsResistance
BreaksResistance
BreaksSupport
calcSRLevelsFromData(tfOpen, tfHigh, tfLow, tfClose, tfVol, volMaLength)
Computes all support/resistance levels from already-fetched MTF data.
Parameters:
tfOpen (float): TF open
tfHigh (float): TF high
tfLow (float): TF low
tfClose (float): TF close
tfVol (float): TF volume
volMaLength (simple int): Volume MA length
Returns:
ResistanceLevel
ResistanceZone
SupportLevel
SupportZone
IsFractalUp
IsFractalDown
detectNewSR(resLevel, supLevel)
Detects a new fractal event (new support or resistance found).
Parameters:
resLevel (float): Current resistance level
supLevel (float): Current support level
Returns:
NewResistance
NewSupport
emaStackCoreLibrary "emaStackCore"
emaCalc(source, length)
Calculate EMA
Parameters:
source (float) : Price source (close, hl2, etc.)
length (simple int) : EMA period
Returns: EMA value
emaSlope(emaValue, lookback)
Calculate EMA slope (% change over lookback period)
Parameters:
emaValue (float) : Current EMA value
lookback (int) : Number of bars to look back
Returns: Slope in percentage
emaDist(ema1, ema2, mode, atrVal, price)
Calculate distance between 2 EMAs (normalized)
Parameters:
ema1 (float) : First EMA value
ema2 (float) : Second EMA value
mode (string) : Normalization mode ("ATR" or "%")
atrVal (float) : ATR value for ATR mode
price (float) : Reference price for % mode
Returns: Normalized distance
slopeClass(slope, thresholdFlat)
Classify EMA slope
Parameters:
slope (float) : EMA slope in %
thresholdFlat (float) : Threshold to consider slope as flat
Returns: +1 (rising), 0 (flat), -1 (falling)
slopeScore5(slope10, slope20, slope50, slope100, slope200, threshold)
Calculate combined slope score for 5 EMAs
Parameters:
slope10 (float) : EMA10 slope
slope20 (float) : EMA20 slope
slope50 (float) : EMA50 slope
slope100 (float) : EMA100 slope
slope200 (float) : EMA200 slope
threshold (float) : Flat threshold
Returns: Sum of slope classes (-5 to +5)
emaStack5Score(e10, e20, e50, e100, e200, tolEq)
Calculate EMA stack score (5 EMAs)
Parameters:
e10 (float) : EMA 10 value
e20 (float) : EMA 20 value
e50 (float) : EMA 50 value
e100 (float) : EMA 100 value
e200 (float) : EMA 200 value
tolEq (float) : Tolerance for equality (%)
Returns: Stack score (-4 to +4)
emaStack5ScoreWithSlope(e10, e20, e50, e100, e200, slope10, slope20, slope50, slope100, slope200, tolEq, slopeThreshold)
Enhanced EMA stack with slope
Parameters:
e10 (float)
e20 (float)
e50 (float)
e100 (float)
e200 (float)
slope10 (float)
slope20 (float)
slope50 (float)
slope100 (float)
slope200 (float)
tolEq (float)
slopeThreshold (float)
Returns:
emaRegimeChange(currentScore, previousScore, threshold)
Detect regime change in EMA stack
Parameters:
currentScore (int) : Current stack score
previousScore (int) : Previous stack score
threshold (int) : Minimum change to consider significant
Returns:
emaCompressed(e10, e20, e50, e100, e200, threshold)
Detect EMA compression (squeeze)
Parameters:
e10 (float) : EMA 10 value
e20 (float) : EMA 20 value
e50 (float) : EMA 50 value
e100 (float) : EMA 100 value
e200 (float) : EMA 200 value
threshold (float) : Max spread % to consider compressed
Returns:
emaConfluence9(price, e10_tf1, e20_tf1, e50_tf1, e10_tf2, e20_tf2, e50_tf2, e10_tf3, e20_tf3, e50_tf3, threshold)
Calculate confluence score (how many EMAs are near price)
Parameters:
price (float) : Current price
e10_tf1 (float) : EMA10 on timeframe 1
e20_tf1 (float) : EMA20 on timeframe 1
e50_tf1 (float) : EMA50 on timeframe 1
e10_tf2 (float) : EMA10 on timeframe 2
e20_tf2 (float) : EMA20 on timeframe 2
e50_tf2 (float) : EMA50 on timeframe 2
e10_tf3 (float) : EMA10 on timeframe 3
e20_tf3 (float) : EMA20 on timeframe 3
e50_tf3 (float) : EMA50 on timeframe 3
threshold (float) : Max distance % to consider "near"
Returns: Confluence count (0-9)
emaConfluence3(price, e10, e20, e50, threshold)
Simplified confluence for 3 EMAs on single TF
Parameters:
price (float)
e10 (float)
e20 (float)
e50 (float)
threshold (float)
Returns: Confluence count (0-3)
emaLeadLag(score15m, score1H, score4H, lookback)
Determine which timeframe is leading the move
Parameters:
score15m (int) : Stack score on 15m
score1H (int) : Stack score on 1H
score4H (int) : Stack score on 4H
lookback (int) : Bars to look back for change detection
Returns: Leader timeframe ("15m", "1H", "4H", "ALIGNED", "MIXED")
emaPriceDivergence(price, emaValue, lookback)
Detect divergence between price and EMA direction
Parameters:
price (float) : Current price
emaValue (float) : EMA value (typically EMA20)
lookback (int) : Bars to compare
Returns:
emaOrderStrength(e10, e20, e50, e100, e200)
Calculate EMA order strength (weighted by distance)
Parameters:
e10 (float) : EMA 10
e20 (float) : EMA 20
e50 (float) : EMA 50
e100 (float) : EMA 100
e200 (float) : EMA 200
Returns: Strength score (higher = stronger trend)
emaPriceZone(price, e10, e20, e50, e100, e200)
Determine which EMA zone price is in
Parameters:
price (float) : Current price
e10 (float) : EMA 10
e20 (float) : EMA 20
e50 (float) : EMA 50
e100 (float) : EMA 100
e200 (float) : EMA 200
Returns: Zone code (5=above all, 0=below all, -1=mixed)
kijunStackCoreLibrary "kijunStackCore"
kijun(highSeries, lowSeries, length)
Parameters:
highSeries (float)
lowSeries (float)
length (int)
distNormAtr(a, b, atrValue)
Parameters:
a (float)
b (float)
atrValue (float)
distNormPct(a, b, price)
Parameters:
a (float)
b (float)
price (float)
stack3Code(kFast, kMid, kSlow, distFastMid, distMidSlow, tolEq)
Parameters:
kFast (float)
kMid (float)
kSlow (float)
distFastMid (float)
distMidSlow (float)
tolEq (float)
stack3Compressed(distFastMid, distMidSlow, tight)
Parameters:
distFastMid (float)
distMidSlow (float)
tight (float)
stack3Stretched(distFastMid, distMidSlow, wide)
Parameters:
distFastMid (float)
distMidSlow (float)
wide (float)
QTCoreLibrary "QTCore"
qt_config_default()
qt_state_new()
qt_daily_update(st, cfg, t, tClose)
Parameters:
st (CycleState)
cfg (QTConfig)
t (int)
tClose (int)
qt_m90_update(st, cfg, t, tClose)
Parameters:
st (CycleState)
cfg (QTConfig)
t (int)
tClose (int)
qt_micro_update(st, cfg, t, tClose)
Parameters:
st (CycleState)
cfg (QTConfig)
t (int)
tClose (int)
qt_nano_update(st, cfg, t, tClose)
Parameters:
st (CycleState)
cfg (QTConfig)
t (int)
tClose (int)
QTConfig
Fields:
tz (series string)
dayStartHour (series int)
dayStartMin (series int)
keepCycles (series int)
tfDaily (series string)
tfM90 (series string)
tfMicro (series string)
tfNano (series string)
QuarterOHLC
Fields:
startTs (series int)
o (series float)
h (series float)
l (series float)
c (series float)
has (series bool)
CycleResult
Fields:
startTs (series int)
endTs (series int)
startRealized (series bool)
endRealized (series bool)
q1Ts (series int)
q2Ts (series int)
q3Ts (series int)
q4Ts (series int)
q2Realized (series bool)
q3Realized (series bool)
q4Realized (series bool)
curQuarterIndex (series int)
inWindow (series bool)
q1 (QuarterOHLC)
q2 (QuarterOHLC)
q3 (QuarterOHLC)
q4 (QuarterOHLC)
FixedCycleState
Fields:
starts (array)
lastStartTs (series int)
CycleState
Fields:
daily (FixedCycleState)
m90 (FixedCycleState)
micro (FixedCycleState)
nano (FixedCycleState)
TradingHelperLibLibrary "TradingHelperLib"
Trading Helper Library - Limit order, pip calculation and utility functions for trading bots
f_pipValue()
Calculates pip value based on symbol info
Returns: Pip value
f_pipsToPrice(pips)
Converts pip count to price difference
Parameters:
pips (float) : Number of pips
Returns: Price difference
calcExpireBarCount(minutesToExpire)
Converts minutes to bar count based on timeframe
Parameters:
minutesToExpire (float) : Duration in minutes
Returns: Bar count
calcLimitPrice(isLong, signalPrice, deviation, deviationType)
Calculates limit order price with deviation
Parameters:
isLong (bool) : True for long, false for short
signalPrice (float) : Signal price
deviation (float) : Deviation amount
deviationType (string) : Deviation type ("USDT" or "%")
Returns: Limit price
checkLimitFill(isLong, limitPrice)
Checks if limit order is filled
Parameters:
isLong (bool) : True for long, false for short
limitPrice (float) : Limit price to check
Returns: True if filled
f_multiplier(lvl, mode)
Calculates DCA multiplier based on level and mode
Parameters:
lvl (int) : DCA level
mode (string) : Multiplier mode ("Sabit", "Fibonacci", "Martingale", etc.)
Returns: Multiplier value
f_pctToPrice(pct, basePrice)
Converts percentage value to price difference
Parameters:
pct (float) : Percentage value (e.g. 2.0 = 2%)
basePrice (float) : Reference price
Returns: Price difference
f_priceChange_toPct(priceChange, basePrice)
Converts price change to percentage
Parameters:
priceChange (float) : Price difference
basePrice (float) : Reference price
Returns: Percentage value
calcMargin(notional, leverage)
Calculates margin from notional value
Parameters:
notional (float) : Trade size (e.g. $1000)
leverage (int) : Leverage value (e.g. 100)
Returns: Margin value
calcNotional(margin, leverage)
Calculates notional from margin
Parameters:
margin (float) : Collateral value
leverage (int) : Leverage value
Returns: Notional value
calcLiqPriceLongSimple(avgPrice, leverage)
Calculates simple liquidation price for Long position
Parameters:
avgPrice (float) : Average entry price
leverage (int) : Leverage value
Returns: Estimated liquidation price
calcLiqPriceShortSimple(avgPrice, leverage)
Calculates simple liquidation price for Short position
Parameters:
avgPrice (float) : Average entry price
leverage (int) : Leverage value
Returns: Estimated liquidation price
calcPnlLong(entryPrice, currentPrice, notional)
Calculates Long position PNL
Parameters:
entryPrice (float) : Entry price
currentPrice (float) : Current price
notional (float) : Position size
Returns: PNL value
calcPnlShort(entryPrice, currentPrice, notional)
Calculates Short position PNL
Parameters:
entryPrice (float) : Entry price
currentPrice (float) : Current price
notional (float) : Position size
Returns: PNL value
calcFee(notional, feeRate)
Calculates trading fee
Parameters:
notional (float) : Trade size
feeRate (float) : Fee rate in percentage (e.g. 0.1 = 0.1%)
Returns: Fee value
libhs_td6Library Overview Data for logger
This library contains test data used by library logger
import GETpacman/logger/x as logger
SpatialIndexYou can start using this now by inserthing this at the top of your indicator/strategy/library.
import ArunaReborn/SpatialIndex/1 as SI
Overview
SpatialIndex is a high-performance Pine Script library that implements price-bucketed spatial indexing for efficient proximity queries on large datasets. Instead of scanning through hundreds or thousands of items linearly (O(n)), this library provides O(k) bucket lookup where k is typically just a handful of buckets, dramatically improving performance for price-based filtering operations.
This library works with any data type through index-based references, making it universally applicable for support/resistance levels, pivot points, order zones, pattern detection points, Fair Value Gaps, and any other price-based data that needs frequent proximity queries.
Why This Library Exists
The Problem
When building advanced technical indicators that track large numbers of price levels (support/resistance zones, pivot points, order blocks, etc.), you often need to answer questions like:
- *"Which levels are within 5% of the current price?"*
- *"What zones overlap with this price range?"*
- *"Are there any significant levels near my entry point?"*
The naive approach is to loop through every single item and check its price. For 500 levels across multiple timeframes, this means 500 comparisons every bar . On instruments with thousands of historical bars, this quickly becomes a performance bottleneck that can cause scripts to time out or lag.
The Solution
SpatialIndex solves this by organizing items into price buckets —like filing cabinets organized by price range. When you query for items near $50,000, the library only looks in the relevant buckets (e.g., $49,000-$51,000 range), ignoring all other price regions entirely.
Performance Example:
- Linear scan: Check 500 items = 500 comparisons per query
- Spatial index: Check 3-5 buckets with ~10 items each = 30-50 comparisons per query
- Result: 10-16x faster queries
Key Features
Core Capabilities
- ✅ Generic Design : Works with any data type via index references
- ✅ Multiple Index Strategies : Fixed bucket size or ATR-based dynamic sizing
- ✅ Range Support : Index items that span price ranges (zones, gaps, channels)
- ✅ Efficient Queries : O(k) bucket lookup instead of O(n) linear scan
- ✅ Multiple Query Types : Proximity percentage, fixed range, exact price with tolerance
- ✅ Dynamic Updates : Add, remove, update items in O(1) time
- ✅ Batch Operations : Efficient bulk removal and reindexing
- ✅ Query Caching : Optional caching for repeated queries within same bar
- ✅ Statistics & Debugging : Built-in stats and diagnostic functions
### Advanced Features
- ATR-Based Bucketing : Automatically adjusts bucket sizes based on volatility
- Multi-Bucket Spanning : Items that span ranges are indexed in all overlapping buckets
- Reindexing Support : Handles array removals with automatic index shifting
- Cache Management : Configurable query caching with automatic invalidation
- Empty Bucket Cleanup : Automatically removes empty buckets to minimize memory
How It Works
The Bucketing Concept
Think of price space as divided into discrete buckets, like a histogram:
```
Price Range: $98-$100 $100-$102 $102-$104 $104-$106 $106-$108
Bucket Key: 49 50 51 52 53
Items:
```
When you query for items near $103:
1. Calculate which buckets overlap the $101.50-$104.50 range (keys 50, 51, 52)
2. Return items from only those buckets:
3. Never check items in buckets 49 or 53
Bucket Size Selection
Fixed Size Mode:
```pine
var SI.SpatialBucket index = SI.newSpatialBucket(2.0) // $2 per bucket
```
- Good for: Instruments with stable price ranges
- Example: For stocks trading at $100, 2.0 = 2% increments
ATR-Based Mode:
```pine
float atr = ta.atr(14)
var SI.SpatialBucket index = SI.newSpatialBucketATR(1.0, atr) // 1x ATR per bucket
SI.updateATR(index, atr) // Update each bar
```
- Good for: Instruments with varying volatility
- Adapts automatically to market conditions
- 1.0 multiplier = one bucket spans one ATR unit
Optimal Bucket Size:
The library includes a helper function to calculate optimal size:
```pine
float optimalSize = SI.calculateOptimalBucketSize(close, 5.0) // For 5% proximity queries
```
This ensures queries span approximately 3 buckets for optimal performance.
Index-Based Architecture
The library doesn't store your actual data—it only stores indices that point to your external arrays:
```pine
// Your data
var array levels = array.new()
var array types = array.new()
var array ages = array.new()
// Your index
var SI.SpatialBucket index = SI.newSpatialBucket(2.0)
// Add a level
array.push(levels, 50000.0)
array.push(types, "support")
array.push(ages, 0)
SI.add(index, array.size(levels) - 1, 50000.0) // Store index 0
// Query near current price
SI.QueryResult result = SI.queryProximity(index, close, 5.0)
for idx in result.indices
float level = array.get(levels, idx)
string type = array.get(types, idx)
// Work with your actual data
```
This design means:
- ✅ Works with any data structure you define
- ✅ No data duplication
- ✅ Minimal memory footprint
- ✅ Full control over your data
---
Usage Guide
Basic Setup
```pine
// Import library
import username/SpatialIndex/1 as SI
// Create index
var SI.SpatialBucket index = SI.newSpatialBucket(2.0)
// Your data arrays
var array supportLevels = array.new()
var array touchCounts = array.new()
```
Adding Items
Single Price Point:
```pine
// Add a support level at $50,000
array.push(supportLevels, 50000.0)
array.push(touchCounts, 1)
int levelIdx = array.size(supportLevels) - 1
SI.add(index, levelIdx, 50000.0)
```
Price Range (Zones/Gaps):
```pine
// Add a resistance zone from $51,000 to $52,000
array.push(zoneBottoms, 51000.0)
array.push(zoneTops, 52000.0)
int zoneIdx = array.size(zoneBottoms) - 1
SI.addRange(index, zoneIdx, 51000.0, 52000.0) // Indexed in all overlapping buckets
```
Querying Items
Proximity Query (Percentage):
```pine
// Find all levels within 5% of current price
SI.QueryResult result = SI.queryProximity(index, close, 5.0)
if array.size(result.indices) > 0
for idx in result.indices
float level = array.get(supportLevels, idx)
// Process nearby level
```
Fixed Range Query:
```pine
// Find all items between $49,000 and $51,000
SI.QueryResult result = SI.queryRange(index, 49000.0, 51000.0)
```
Exact Price with Tolerance:
```pine
// Find items at exactly $50,000 +/- $100
SI.QueryResult result = SI.queryAt(index, 50000.0, 100.0)
```
Removing Items
Safe Removal Pattern:
```pine
SI.QueryResult result = SI.queryProximity(index, close, 5.0)
if array.size(result.indices) > 0
// IMPORTANT: Sort descending to safely remove from arrays
array sorted = SI.sortIndicesDescending(result)
for idx in sorted
// Remove from index
SI.remove(index, idx)
// Remove from your data arrays
array.remove(supportLevels, idx)
array.remove(touchCounts, idx)
// Reindex to maintain consistency
SI.reindexAfterRemoval(index, idx)
```
Batch Removal (More Efficient):
```pine
// Collect indices to remove
array toRemove = array.new()
for i = 0 to array.size(supportLevels) - 1
if array.get(touchCounts, i) > 10 // Remove old levels
array.push(toRemove, i)
// Remove in descending order from data arrays
array sorted = array.copy(toRemove)
array.sort(sorted, order.descending)
for idx in sorted
SI.remove(index, idx)
array.remove(supportLevels, idx)
array.remove(touchCounts, idx)
// Batch reindex (much faster than individual reindexing)
SI.reindexAfterBatchRemoval(index, toRemove)
```
Updating Items
```pine
// Update a level's price (e.g., after refinement)
float newPrice = 50100.0
SI.update(index, levelIdx, newPrice)
array.set(supportLevels, levelIdx, newPrice)
// Update a zone's range
SI.updateRange(index, zoneIdx, 51000.0, 52500.0)
array.set(zoneBottoms, zoneIdx, 51000.0)
array.set(zoneTops, zoneIdx, 52500.0)
```
Query Caching
For repeated queries within the same bar:
```pine
// Create cache (persistent)
var SI.CachedQuery cache = SI.newCachedQuery()
// Cached query (returns cached result if parameters match)
SI.QueryResult result = SI.queryProximityCached(
index,
cache,
close,
5.0, // proximity%
1 // cache duration in bars
)
// Invalidate cache when index changes significantly
if bigChangeDetected
SI.invalidateCache(cache)
```
---
Practical Examples
Example 1: Support/Resistance Finder
```pine
//@version=6
indicator("S/R with Spatial Index", overlay=true)
import username/SpatialIndex/1 as SI
// Data storage
var array levels = array.new()
var array types = array.new() // "support" or "resistance"
var array touches = array.new()
var array ages = array.new()
// Spatial index
var SI.SpatialBucket index = SI.newSpatialBucket(close * 0.02) // 2% buckets
// Detect pivots
bool isPivotHigh = ta.pivothigh(high, 5, 5)
bool isPivotLow = ta.pivotlow(low, 5, 5)
// Add new levels
if isPivotHigh
array.push(levels, high )
array.push(types, "resistance")
array.push(touches, 1)
array.push(ages, 0)
SI.add(index, array.size(levels) - 1, high )
if isPivotLow
array.push(levels, low )
array.push(types, "support")
array.push(touches, 1)
array.push(ages, 0)
SI.add(index, array.size(levels) - 1, low )
// Find nearby levels (fast!)
SI.QueryResult nearby = SI.queryProximity(index, close, 3.0) // Within 3%
// Process nearby levels
for idx in nearby.indices
float level = array.get(levels, idx)
string type = array.get(types, idx)
// Check for touch
if type == "support" and low <= level and low > level
array.set(touches, idx, array.get(touches, idx) + 1)
else if type == "resistance" and high >= level and high < level
array.set(touches, idx, array.get(touches, idx) + 1)
// Age and cleanup old levels
for i = array.size(ages) - 1 to 0
array.set(ages, i, array.get(ages, i) + 1)
// Remove levels older than 500 bars or with 5+ touches
if array.get(ages, i) > 500 or array.get(touches, i) >= 5
SI.remove(index, i)
array.remove(levels, i)
array.remove(types, i)
array.remove(touches, i)
array.remove(ages, i)
SI.reindexAfterRemoval(index, i)
// Visualization
for idx in nearby.indices
line.new(bar_index, array.get(levels, idx), bar_index + 10, array.get(levels, idx),
color=array.get(types, idx) == "support" ? color.green : color.red)
```
Example 2: Multi-Timeframe Zone Detector
```pine
//@version=6
indicator("MTF Zones", overlay=true)
import username/SpatialIndex/1 as SI
// Store zones from multiple timeframes
var array zoneBottoms = array.new()
var array zoneTops = array.new()
var array zoneTimeframes = array.new()
// ATR-based spatial index for adaptive bucketing
var SI.SpatialBucket index = SI.newSpatialBucketATR(1.0, ta.atr(14))
SI.updateATR(index, ta.atr(14)) // Update bucket size with volatility
// Request higher timeframe data
= request.security(syminfo.tickerid, "240", )
// Detect HTF zones
if not na(htf_high) and not na(htf_low)
float zoneTop = htf_high
float zoneBottom = htf_low * 0.995 // 0.5% zone thickness
// Check if zone already exists nearby
SI.QueryResult existing = SI.queryRange(index, zoneBottom, zoneTop)
if array.size(existing.indices) == 0 // No overlapping zones
// Add new zone
array.push(zoneBottoms, zoneBottom)
array.push(zoneTops, zoneTop)
array.push(zoneTimeframes, "4H")
int idx = array.size(zoneBottoms) - 1
SI.addRange(index, idx, zoneBottom, zoneTop)
// Query zones near current price
SI.QueryResult nearbyZones = SI.queryProximity(index, close, 2.0) // Within 2%
// Highlight nearby zones
for idx in nearbyZones.indices
box.new(bar_index - 50, array.get(zoneBottoms, idx),
bar_index, array.get(zoneTops, idx),
bgcolor=color.new(color.blue, 90))
```
### Example 3: Performance Comparison
```pine
//@version=6
indicator("Spatial Index Performance Test")
import username/SpatialIndex/1 as SI
// Generate 500 random levels
var array levels = array.new()
var SI.SpatialBucket index = SI.newSpatialBucket(close * 0.02)
if bar_index == 0
for i = 0 to 499
float randomLevel = close * (0.9 + math.random() * 0.2) // +/- 10%
array.push(levels, randomLevel)
SI.add(index, i, randomLevel)
// Method 1: Linear scan (naive approach)
int linearCount = 0
float proximityPct = 5.0
float lowBand = close * (1 - proximityPct/100)
float highBand = close * (1 + proximityPct/100)
for i = 0 to array.size(levels) - 1
float level = array.get(levels, i)
if level >= lowBand and level <= highBand
linearCount += 1
// Method 2: Spatial index query
SI.QueryResult result = SI.queryProximity(index, close, proximityPct)
int spatialCount = array.size(result.indices)
// Compare performance
plot(result.queryCount, "Items Examined (Spatial)", color=color.green)
plot(linearCount, "Items Examined (Linear)", color=color.red)
plot(spatialCount, "Results Found", color=color.blue)
// Spatial index typically examines 10-50 items vs 500 for linear scan!
```
API Reference Summary
Initialization
- `newSpatialBucket(bucketSize)` - Fixed bucket size
- `newSpatialBucketATR(atrMultiplier, atrValue)` - ATR-based buckets
- `updateATR(sb, newATR)` - Update ATR for dynamic sizing
Adding Items
- `add(sb, itemIndex, price)` - Add item at single price point
- `addRange(sb, itemIndex, priceBottom, priceTop)` - Add item spanning range
Querying
- `queryProximity(sb, refPrice, proximityPercent)` - Query by percentage
- `queryRange(sb, priceBottom, priceTop)` - Query fixed range
- `queryAt(sb, price, tolerance)` - Query exact price with tolerance
- `queryProximityCached(sb, cache, refPrice, pct, duration)` - Cached query
Removing & Updating
- `remove(sb, itemIndex)` - Remove item
- `update(sb, itemIndex, newPrice)` - Update item price
- `updateRange(sb, itemIndex, newBottom, newTop)` - Update item range
- `reindexAfterRemoval(sb, removedIndex)` - Reindex after single removal
- `reindexAfterBatchRemoval(sb, removedIndices)` - Batch reindex
- `clear(sb)` - Remove all items
Utilities
- `size(sb)` - Get item count
- `isEmpty(sb)` - Check if empty
- `contains(sb, itemIndex)` - Check if item exists
- `getStats(sb)` - Get debug statistics string
- `calculateOptimalBucketSize(price, pct)` - Calculate optimal bucket size
- `sortIndicesDescending(result)` - Sort for safe removal
- `sortIndicesAscending(result)` - Sort ascending
Performance Characteristics
Time Complexity
- Add : O(1) for single point, O(m) for range spanning m buckets
- Remove : O(1) lookup + O(b) bucket cleanup where b = buckets item spans
- Query : O(k) where k = buckets in range (typically 3-5) vs O(n) linear scan
- Update : O(1) removal + O(1) addition = O(1) total
Space Complexity
- Memory per item : ~8 bytes for index reference + map overhead
- Bucket overhead : Proportional to price range coverage
- Typical usage : For 500 items with 50 active buckets ≈ 4-8KB total
Scalability
- ✅ 100 items : ~5-10x faster than linear scan
- ✅ 500 items : ~10-15x faster
- ✅ 1000+ items : ~15-20x faster
- ⚠️ Performance degrades if bucket size is too small (too many buckets)
- ⚠️ Performance degrades if bucket size is too large (too many items per bucket)
Best Practices
Bucket Size Selection
1. Start with 2-5% of asset price for percentage-based queries
2. Use ATR-based mode for volatile assets or multi-symbol scripts
3. Test bucket size using `calculateOptimalBucketSize()` function
4. Monitor with `getStats()` to ensure reasonable bucket count
Memory Management
1. Clear old items regularly to prevent unbounded growth
2. Use age tracking to remove stale data
3. Set maximum item limits based on your needs
4. Batch removals are more efficient than individual removals
Query Optimization
1. Use caching for repeated queries within same bar
2. Invalidate cache when index changes significantly
3. Sort results descending before removal iteration
4. Batch operations when possible (reindexing, removal)
Data Consistency
1. Always reindex after removal to maintain index alignment
2. Remove from arrays in descending order to avoid index shifting issues
3. Use batch reindex for multiple simultaneous removals
4. Keep external arrays and index in sync at all times
Limitations & Caveats
Known Limitations
- Not suitable for exact price matching : Use tolerance with `queryAt()`
- Bucket size affects performance : Too small = many buckets, too large = many items per bucket
- Memory usage : Scales with price range coverage and item count
- Reindexing overhead : Removing items mid-array requires index shifting
When NOT to Use
- ❌ Datasets with < 50 items (linear scan is simpler)
- ❌ Items that change price every bar (constant reindexing overhead)
- ❌ When you need ALL items every time (no benefit over arrays)
- ❌ Exact price level matching without tolerance (use maps instead)
When TO Use
- ✅ Large datasets (100+ items) with occasional queries
- ✅ Proximity-based filtering (% of price, ATR-based ranges)
- ✅ Multi-timeframe level tracking
- ✅ Zone/range overlap detection
- ✅ Price-based spatial filtering
---
Technical Details
Bucketing Algorithm
Items are assigned to buckets using integer division:
```
bucketKey = floor((price - basePrice) / bucketSize)
```
For ATR-based mode:
```
effectiveBucketSize = atrValue × atrMultiplier
bucketKey = floor((price - basePrice) / effectiveBucketSize)
```
Range Indexing
Items spanning price ranges are indexed in all overlapping buckets to ensure accurate range queries. The midpoint bucket is designated as the "primary bucket" for removal operations.
Index Consistency
The library maintains two maps:
1. `buckets`: Maps bucket keys → IntArray wrappers containing item indices
2. `itemToBucket`: Maps item indices → primary bucket key (for O(1) removal)
This dual-mapping ensures both fast queries and fast removal while maintaining consistency.
Implementation Note: Pine Script doesn't allow nested collections (map containing arrays directly), so the library uses an `IntArray` wrapper type to hold arrays within the map structure. This is an internal implementation detail that doesn't affect usage.
---
Version History
Version 1.0
- Initial release
Credits & License
License : Mozilla Public License 2.0 (TradingView default)
Library Type : Open-source educational resource
This library is designed as a public domain utility for the Pine Script community. As per TradingView library rules, this code can be freely reused by other authors. If you use this library in your scripts, please provide appropriate credit as required by House Rules.
Summary
SpatialIndex is a specialized library that solves a specific problem: fast proximity queries on large price-based datasets . If you're building indicators that track hundreds of levels, zones, or price points and need to frequently filter by proximity to current price, this library can provide 10-20x performance improvements over naive linear scanning.
The index-based architecture makes it universally applicable to any data type, and the ATR-based bucketing ensures it adapts to market conditions automatically. Combined with query caching and batch operations, it provides a complete solution for spatial data management in Pine Script.
Use this library when speed matters and your dataset is large.
MLMatrixLibOverview
MLMatrixLib is a comprehensive Pine Script v6 library implementing machine learning algorithms using native matrix operations. This library provides traders and developers with a toolkit of statistical and ML methods for building quantitative trading systems, performing data analysis, and creating adaptive indicators.
How It Works
The library leverages Pine Script's native matrix type to perform efficient linear algebra operations. Each algorithm is implemented from first principles, using matrix decomposition, iterative optimization, and statistical estimation techniques. All functions are designed for numerical stability with careful handling of edge cases.
---
Library Contents (34 Sections)
Section 1: Utility Functions & Matrix Operations
Core building blocks including:
• identity(n) - Creates n×n identity matrix
• diagonal(values) - Creates diagonal matrix from array
• ones(rows, cols) / zeros(rows, cols) - Matrix constructors
• frobeniusNorm(m) / l1Norm(m) - Matrix norm calculations
• hadamard(m1, m2) - Element-wise multiplication
• columnMeans(m) / rowMeans(m) - Statistical aggregations
• standardize(m) - Z-score normalization (zero mean, unit variance)
• minMaxNormalize(m) - Scale values to range
• fitStandardScaler(m) / fitMinMaxScaler(m) - Reusable scaler parameters
• addBiasColumn(m) - Prepend column of ones for regression
• arrayMedian(arr) / arrayPercentile(arr, p) - Array statistics
Section 2: Activation Functions
Numerically stable implementations:
• sigmoid(x) / sigmoidMatrix(m) - Logistic function with overflow protection
• tanhActivation(x) / tanhMatrix(m) - Hyperbolic tangent
• relu(x) / reluMatrix(m) - Rectified Linear Unit
• leakyRelu(x, alpha) - Leaky ReLU with configurable slope
• elu(x, alpha) - Exponential Linear Unit
• Derivatives for backpropagation: sigmoidDerivative, tanhDerivative, reluDerivative
Section 3: Linear Regression (OLS)
Ordinary Least Squares implementation using the normal equation (X'X)⁻¹X'y:
• fitLinearRegression(X, y) - Fits model, returns coefficients, R², standard error
• fitSimpleLinearRegression(x, y) - Single-variable regression
• predictLinear(model, X) - Generate predictions
• predictionInterval(model, X, confidence) - Confidence intervals using t-distribution
• Model type stores: coefficients, R-squared, residuals, standard error
Section 4: Weighted Linear Regression
Generalized least squares with observation weights:
• fitWeightedLinearRegression(X, y, weights) - Solves (X'WX)⁻¹X'Wy
• Useful for downweighting outliers or emphasizing recent data
Section 5: Polynomial Regression
Fits polynomials of arbitrary degree:
• fitPolynomialRegression(x, y, degree) - Constructs Vandermonde matrix
• predictPolynomial(model, x) - Evaluate polynomial at points
Section 6: Ridge Regression (L2 Regularization)
Adds penalty term λ||β||² to prevent overfitting:
• fitRidgeRegression(X, y, lambda) - Solves (X'X + λI)⁻¹X'y
• Lambda parameter controls regularization strength
Section 7: LASSO Regression (L1 Regularization)
Coordinate descent algorithm for sparse solutions:
• fitLassoRegression(X, y, lambda, maxIter, tolerance) - Iterative soft-thresholding
• Produces sparse coefficients by driving some to exactly zero
• softThreshold(x, lambda) - Core shrinkage operator
Section 8: Elastic Net (L1 + L2 Regularization)
Combines LASSO and Ridge penalties:
• fitElasticNet(X, y, lambda, alpha, maxIter, tolerance)
• Alpha balances L1 vs L2: alpha=1 is LASSO, alpha=0 is Ridge
Section 9: Huber Robust Regression
Iteratively Reweighted Least Squares (IRLS) for outlier resistance:
• fitHuberRegression(X, y, delta, maxIter, tolerance)
• Delta parameter defines transition between L1 and L2 loss
• Downweights observations with large residuals
Section 10: Quantile Regression
Estimates conditional quantiles using linear programming approximation:
• fitQuantileRegression(X, y, tau, maxIter, tolerance)
• Tau specifies quantile (0.5 = median, 0.25 = lower quartile, etc.)
Section 11: Logistic Regression (Binary Classification)
Gradient descent optimization of cross-entropy loss:
• fitLogisticRegression(X, y, learningRate, maxIter, tolerance)
• predictProbability(model, X) - Returns probabilities
• predictClass(model, X, threshold) - Returns binary predictions
Section 12: Linear SVM (Support Vector Machine)
Sub-gradient descent with hinge loss:
• fitLinearSVM(X, y, C, learningRate, maxIter)
• C parameter controls regularization (higher = harder margin)
• predictSVM(model, X) - Returns class predictions
Section 13: Recursive Least Squares (RLS)
Online learning with exponential forgetting:
• createRLSState(nFeatures, lambda, delta) - Initialize state
• updateRLS(state, x, y) - Update with new observation
• Lambda is forgetting factor (0.95-0.99 typical)
• Useful for adaptive indicators that update incrementally
Section 14: Covariance and Correlation
Matrix statistics:
• covarianceMatrix(m) - Sample covariance
• correlationMatrix(m) - Pearson correlations
• pearsonCorrelation(x, y) - Single correlation coefficient
• spearmanCorrelation(x, y) - Rank-based correlation
Section 15: Principal Component Analysis (PCA)
Dimensionality reduction via eigendecomposition:
• fitPCA(X, nComponents) - Power iteration method
• transformPCA(X, model) - Project data onto principal components
• Returns components, explained variance, and mean
Section 16: K-Means Clustering
Lloyd's algorithm with k-means++ initialization:
• fitKMeans(X, k, maxIter, tolerance) - Cluster data points
• predictCluster(model, X) - Assign new points to clusters
• withinClusterVariance(model) - Measure cluster compactness
Section 17: Gaussian Mixture Model (GMM)
Expectation-Maximization algorithm:
• fitGMM(X, k, maxIter, tolerance) - Soft clustering with probabilities
• predictProbaGMM(model, X) - Returns membership probabilities
• Models data as mixture of Gaussian distributions
Section 18: Kalman Filter
Linear state estimation:
• createKalman1D(processNoise, measurementNoise, ...) - 1D filter
• createKalman2D(processNoise, measurementNoise) - Position + velocity tracking
• kalmanStep(state, measurement) - Predict-update cycle
• Optimal filtering for noisy measurements
Section 19: K-Nearest Neighbors (KNN)
Instance-based learning:
• fitKNN(X, y) - Store training data
• predictKNN(model, X, k) - Classify by majority vote
• predictKNNRegression(model, X, k) - Average of k neighbors
• predictKNNWeighted(model, X, k) - Distance-weighted voting
Section 20: Neural Network (Feedforward)
Multi-layer perceptron:
• createNeuralNetwork(architecture) - Define layer sizes
• trainNeuralNetwork(nn, X, y, learningRate, epochs) - Backpropagation
• predictNN(nn, X) - Forward pass
• Supports configurable hidden layers
Section 21: Naive Bayes Classifier
Gaussian Naive Bayes:
• fitNaiveBayes(X, y) - Estimate class-conditional distributions
• predictNaiveBayes(model, X) - Maximum a posteriori classification
• Assumes feature independence given class
Section 22: Anomaly Detection
Statistical outlier detection:
• fitAnomalyDetector(X, contamination) - Mahalanobis distance-based
• detectAnomalies(model, X) - Returns anomaly scores
• isAnomaly(model, X, threshold) - Binary classification
Section 23: Dynamic Time Warping (DTW)
Time series similarity:
• dtw(series1, series2) - Compute DTW distance
• Handles sequences of different lengths
• Useful for pattern matching
Section 24: Markov Chain / Regime Detection
Discrete state transitions:
• fitMarkovChain(states, nStates) - Estimate transition matrix
• predictNextState(transitionMatrix, currentState) - Most likely next state
• stationaryDistribution(transitionMatrix) - Long-run probabilities
Section 25: Hidden Markov Model (Simple)
Baum-Welch algorithm:
• fitHMM(observations, nStates, maxIter) - EM training
• viterbi(model, observations) - Most likely state sequence
• Useful for regime detection
Section 26: Exponential Smoothing & Holt-Winters
Time series smoothing:
• exponentialSmooth(data, alpha) - Simple exponential smoothing
• holtWinters(data, alpha, beta, gamma, seasonLength) - Triple smoothing
• Captures trend and seasonality
Section 27: Entropy and Information Theory
Information measures:
• entropy(probabilities) - Shannon entropy in bits
• conditionalEntropy(jointProbs, marginalProbs) - H(X|Y)
• mutualInformation(probsX, probsY, jointProbs) - I(X;Y)
• kldivergence(p, q) - Kullback-Leibler divergence
Section 28: Hurst Exponent
Long-range dependence measure:
• hurstExponent(data) - R/S analysis
• H < 0.5: mean-reverting, H = 0.5: random walk, H > 0.5: trending
Section 29: Change Detection (CUSUM)
Cumulative sum control chart:
• cusumChangeDetection(data, threshold, drift) - Detect regime changes
• cusumOnline(value, prevCusumPos, prevCusumNeg, target, drift) - Streaming version
Section 30: Autocorrelation
Serial dependence analysis:
• autocorrelation(data, maxLag) - ACF for all lags
• partialAutocorrelation(data, maxLag) - PACF via Durbin-Levinson
• Useful for time series model identification
Section 31: Ensemble Methods
Model combination:
• baggingPredict(models, X) - Average predictions
• votingClassify(models, X) - Majority vote
• Improves robustness through aggregation
Section 32: Model Evaluation Metrics
Performance assessment:
• mse(actual, predicted) / rmse / mae / mape - Regression metrics
• accuracy(actual, predicted) - Classification accuracy
• precision / recall / f1Score - Binary classification metrics
• confusionMatrix(actual, predicted, nClasses) - Multi-class evaluation
• rSquared(actual, predicted) / adjustedRSquared - Goodness of fit
Section 33: Cross-Validation
Model validation:
• trainTestSplit(X, y, trainRatio) - Random split
• Foundation for walk-forward validation
Section 34: Trading Convenience Functions
Trading-specific utilities:
• priceMatrix(length) - OHLC data as matrix
• logReturns(length) - Log return series
• rollingSlope(src, length) - Linear trend strength
• kalmanFilter(src, processNoise, measurementNoise) - Filtered price
• kalmanFilter2D(src, ...) - Price with velocity estimate
• adaptiveMA(src, sensitivity) - Kalman-based adaptive moving average
• volAdjMomentum(src, length) - Volatility-normalized momentum
• detectSRLevels(length, nLevels) - K-means based S/R detection
• buildFeatures(src, lengths) - Multi-timeframe feature construction
• technicalFeatures(length) - Standard indicator feature set (RSI, MACD, BB, ATR, etc.)
• lagFeatures(src, lags) - Time-lagged features
• sharpeRatio(returns) - Risk-adjusted return measure
• sortinoRatio(returns) - Downside risk-adjusted return
• maxDrawdown(equity) - Maximum peak-to-trough decline
• calmarRatio(returns, equity) - Return/drawdown ratio
• kellyCriterion(winRate, avgWin, avgLoss) - Optimal position sizing
• fractionalKelly(...) - Conservative Kelly sizing
• rollingBeta(assetReturns, benchmarkReturns) - Market exposure
• fractalDimension(data) - Market complexity measure
---
Usage Example
```
import YourUsername/MLMatrixLib/1 as ml
// Create feature matrix
matrix X = ml.priceMatrix(50)
X := ml.standardize(X)
// Fit linear regression
ml.LinearRegressionModel model = ml.fitLinearRegression(X, y)
float prediction = ml.predictLinear(model, X_new)
// Kalman filter for smoothing
float smoothedPrice = ml.kalmanFilter(close, 0.01, 1.0)
// Detect support/resistance levels
array levels = ml.detectSRLevels(100, 3)
// K-means clustering for regime detection
ml.KMeansModel km = ml.fitKMeans(features, 3)
int cluster = ml.predictCluster(km, newFeature)
```
---
Technical Notes
• All matrix operations use Pine Script's native matrix type
• Numerical stability ensured through:
- Clamping exponential arguments to prevent overflow
- Division by zero protection with epsilon thresholds
- Iterative algorithms with convergence tolerance
• Designed for bar-by-bar execution in Pine Script's event-driven model
• Compatible with Pine Script v6
---
Disclaimer
This library provides mathematical tools for quantitative analysis. It does not constitute financial advice. Past performance of any algorithm does not guarantee future results. Users are responsible for validating models on their specific use cases and understanding the limitations of each method.
InfinityCandlePatternsLibrary "InfinityCandlePatterns"
isMorningStar(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isEveningStar(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isThreeWhiteSoldiers(o, h, c, atr)
Parameters:
o (float)
h (float)
c (float)
atr (float)
isThreeBlackCrows(o, l, c, atr)
Parameters:
o (float)
l (float)
c (float)
atr (float)
isBullishHarami(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isBearishHarami(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isBullishEngulfing(o, c)
Parameters:
o (float)
c (float)
isBearishEngulfing(o, c)
Parameters:
o (float)
c (float)
isThreeInsideUp(o, h, c, atr)
Parameters:
o (float)
h (float)
c (float)
atr (float)
isThreeInsideDown(o, l, c, atr)
Parameters:
o (float)
l (float)
c (float)
atr (float)
isTweezerBottom(o, l, c, atr)
Parameters:
o (float)
l (float)
c (float)
atr (float)
isTweezerTop(o, h, c, atr)
Parameters:
o (float)
h (float)
c (float)
atr (float)
isBullishKicker(o, c)
Parameters:
o (float)
c (float)
isBearishKicker(o, c)
Parameters:
o (float)
c (float)
isBullishBreakaway(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isBearishBreakaway(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isHammer(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isShootingStar(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isStandardDoji(o, c, atr)
Parameters:
o (float)
c (float)
atr (float)
isDragonflyDoji(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isGravestoneDoji(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isBullishMarubozu(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isBearishMarubozu(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
isSpinningTop(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
bullAny(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
bearAny(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
neutralAny(o, h, l, c, atr)
Parameters:
o (float)
h (float)
l (float)
c (float)
atr (float)
bullStrong(o, h, c, atr)
Parameters:
o (float)
h (float)
c (float)
atr (float)
bearStrong(o, l, c, atr)
Parameters:
o (float)
l (float)
c (float)
atr (float)
TimeframeAlignTHE PROBLEM THIS LIBRARY SOLVES
When you use `request.security()` to get data from a Higher Timeframe (HTF) and try to draw objects like boxes, lines, or labels, they appear at the wrong horizontal position . This is the "floating in space" problem.
Why does this happen?
The `bar_index` in Pine Script refers to where data was RECEIVED , not where the event OCCURRED .
Consider this scenario:
• You're on a 5-minute chart
• You request 1-hour data for drawing an FVG (Fair Value Gap)
• A 1H candle spans 12 chart bars (60min / 5min = 12)
• But your code draws at `bar_index - 1` or `bar_index - 3`
• The result: your FVG box is only 2-3 bars wide instead of spanning the correct 12-36 bars
This library solves that by tracking where HTF bars actually start and end on your chart timeframe.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HOW TO USE THIS LIBRARY
Step 1: Import the Library
```
import ArunaReborn/TimeframeAlign/1 as tfa
```
Step 2: Create a Tracker for Each HTF
```
var tfa.HTFTracker tracker1H = tfa.createTracker("60")
```
Step 3: Update the Tracker Every Bar
```
tfa.updateTracker(tracker1H, "60")
```
Step 4: Use Synced Drawing Functions
```
if tfa.htfBarChanged(tracker1H)
tfa.syncedBox(tracker1H, 3, 1, topPrice, bottomPrice, color.new(color.green, 80))
```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
EXPORTED TYPES
TimeframePair
Stores metadata about the relationship between source and chart timeframes.
• sourceTimeframe - The HTF/LTF being compared
• chartTimeframe - Current chart timeframe
• isHTF - True if source is higher than chart
• isLTF - True if source is lower than chart
• barRatio - Chart bars per source bar
• secondsRatio - Time ratio between timeframes
MTFEventData
Stores synchronized event data with correct bar positions.
• price - Price level of the event
• eventTime - Unix timestamp of the event
• chartBarStart - Chart bar_index where event's TF bar started
• chartBarEnd - Chart bar_index where event's TF bar ended
• htfOffset - The HTF offset used
• isValid - True if synchronization succeeded
HTFTracker
Tracks HTF bar boundaries. Create one per timeframe you need to track.
• htfTimeframe - The timeframe being tracked
• currentStartBar - Where current HTF bar started
• currentEndBar - Where current HTF bar ends (provisional)
• startHistory - Array of historical start positions
• endHistory - Array of historical end positions
• lastUpdateBar - Last bar_index when updated
• barJustChanged - True if HTF bar changed on this chart bar (set by updateTracker)
SyncedBox
Managed box with synchronization metadata.
• bx - The Pine Script box object
• htfTimeframe - Source timeframe
• leftHtfOffset / rightHtfOffset - HTF offsets for edges
• topPrice / bottomPrice - Price boundaries
• extendRight - Auto-extend flag
SyncedLine
Managed line with synchronization metadata.
• ln - The Pine Script line object
• htfTimeframe - Source timeframe
• htfOffset - Anchor offset
• price - Price level (horizontal lines)
• isHorizontal - Line orientation
• extendRight - Auto-extend flag
SyncedLabel
Managed label with synchronization metadata.
• lbl - The Pine Script label object
• htfTimeframe - Source timeframe
• htfOffset - Anchor offset
• price - Price level
• anchorPoint - "start", "end", or "middle"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
EXPORTED FUNCTIONS
━━ CORE FUNCTIONS ━━
getTimeframeInfo(sourceTimeframe)
Analyzes relationship between a source TF and chart TF.
Returns: TimeframePair with comparison metadata
createTracker(htfTimeframe)
Creates a new HTF tracker. Call once per timeframe, store with `var`.
Returns: HTFTracker instance
updateTracker(tracker, htfTimeframe, historyDepth)
Updates tracker with current bar data. Call on every bar.
• htfTimeframe: The timeframe string (must match createTracker)
• historyDepth: Max HTF bars to track (default 500)
Returns: Updated tracker
getStartBar(tracker, htfOffset)
Gets chart bar_index where a specific HTF bar started.
• htfOffset: 0=current, 1=previous, 2=two bars ago, etc.
Returns: bar_index or na
getEndBar(tracker, htfOffset)
Gets chart bar_index where a specific HTF bar ended.
Returns: bar_index or na
htfBarChanged(tracker)
Detects when HTF bar just changed.
Returns: True on first chart bar of new HTF bar
findBarAtTime(timestamp, maxLookback)
Searches backward to find chart bar containing a timestamp.
• maxLookback: How far back to search (default 500)
Returns: bar_index or na
syncEventToChart(tracker, eventPrice, eventTime, anchorPoint)
Generic sync function mapping any event to correct chart position.
• anchorPoint: "start", "end", or "middle"
Returns: MTFEventData
━━ DRAWING CREATION FUNCTIONS ━━
syncedBox(tracker, leftHtfOffset, rightHtfOffset, topPrice, bottomPrice, bgcolor, ...)
Creates a box at correct HTF-aligned position.
• leftHtfOffset: HTF bars back for left edge
• rightHtfOffset: HTF bars back for right edge
• extendRight: Auto-extend to current bar
Returns: SyncedBox or na
syncedHLine(tracker, htfOffset, price, lineColor, lineStyle, lineWidth, extendRight)
Creates horizontal line anchored to HTF bar start.
• extendRight: If true, extends to current bar (default true)
Returns: SyncedLine or na
syncedVLine(tracker, htfOffset, atStart, lineColor, lineStyle, lineWidth)
Creates vertical line at HTF bar boundary.
• atStart: True=start of HTF bar, False=end
Returns: SyncedLine or na
syncedLabel(tracker, htfOffset, price, labelText, anchorPoint, ...)
Creates label at correct HTF-aligned position.
• anchorPoint: "start", "end", or "middle"
Returns: SyncedLabel or na
syncedPlotValue(tracker, value, htfOffset)
Returns value for plotting only at synced positions.
Returns: value if current bar is within HTF range, otherwise na
━━ UPDATE FUNCTIONS ━━
updateSyncedBox(syncedBox, extendToCurrentBar)
Extends existing box's right edge to current bar.
Returns: Updated SyncedBox
updateSyncedLine(syncedLine, extendToCurrentBar)
Extends existing horizontal line to current bar.
Returns: Updated SyncedLine
updateSyncedLabel(syncedLabel, tracker, newText, newPrice)
Updates label text/price while maintaining sync.
Returns: Updated SyncedLabel
━━ CONVENIENCE FUNCTIONS ━━
htfBarStartIndex(htfTimeframe, htfOffset, historyDepth)
Simple function to get HTF bar start without explicit tracker.
⚠️ Only tracks ONE timeframe. For multiple TFs, use createTracker pattern.
Returns: bar_index or na
htfBarEndIndex(htfTimeframe, htfOffset, historyDepth)
Simple function to get HTF bar end without explicit tracker.
⚠️ Only tracks ONE timeframe. For multiple TFs, use createTracker pattern.
Returns: bar_index or na
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
COMPLETE USAGE EXAMPLES
Example 1: FVG Box with Auto-Extend
```
//@version=6
indicator("FVG with Synced Drawing", overlay=true)
import ArunaReborn/TimeframeAlign/1 as tfa
htfInput = input.timeframe("60", "HTF for FVG")
// Create tracker for chosen timeframe
var tfa.HTFTracker fvgTracker = tfa.createTracker(htfInput)
tfa.updateTracker(fvgTracker, htfInput)
// Get FVG data from HTF (confirmed bars with offset)
= request.security(syminfo.tickerid, htfInput,
[low , high , low > high ],
lookahead=barmerge.lookahead_off)
// Store managed box
var tfa.SyncedBox fvgBox = na
// Create synced box when FVG detected
if fvgDetected and tfa.htfBarChanged(fvgTracker)
fvgBox := tfa.syncedBox(fvgTracker, 3, 1, fvgTop, fvgBot,
color.new(color.green, 85), color.green, 1, "FVG", color.white, true)
// Extend box to current bar each tick
if not na(fvgBox)
tfa.updateSyncedBox(fvgBox, true)
```
Example 2: HTF Support/Resistance Lines
```
//@version=6
indicator("HTF S/R Lines", overlay=true)
import ArunaReborn/TimeframeAlign/1 as tfa
htfInput = input.timeframe("240", "HTF for S/R")
// Create and update tracker
var tfa.HTFTracker srTracker = tfa.createTracker(htfInput)
tfa.updateTracker(srTracker, htfInput)
// Get HTF high/low (confirmed with offset)
= request.security(syminfo.tickerid, htfInput,
[high , low ], lookahead=barmerge.lookahead_off)
// Track lines
var tfa.SyncedLine resistanceLine = na
var tfa.SyncedLine supportLine = na
// Create new lines when HTF bar changes
if tfa.htfBarChanged(srTracker)
resistanceLine := tfa.syncedHLine(srTracker, 1, htfHigh, color.red, line.style_solid, 2, true)
supportLine := tfa.syncedHLine(srTracker, 1, htfLow, color.green, line.style_solid, 2, true)
// Auto-extend lines each bar
if not na(resistanceLine)
tfa.updateSyncedLine(resistanceLine, true)
if not na(supportLine)
tfa.updateSyncedLine(supportLine, true)
```
Example 3: Multiple Timeframes
```
//@version=6
indicator("Multi-TF Boxes", overlay=true)
import ArunaReborn/TimeframeAlign/1 as tfa
// Create separate tracker for each timeframe
var tfa.HTFTracker tracker1H = tfa.createTracker("60")
var tfa.HTFTracker tracker4H = tfa.createTracker("240")
var tfa.HTFTracker trackerD = tfa.createTracker("1D")
// Update ALL trackers every bar (pass the same TF string)
tfa.updateTracker(tracker1H, "60")
tfa.updateTracker(tracker4H, "240")
tfa.updateTracker(trackerD, "1D")
// Now use each tracker independently for drawing
// Each tracker maintains its own separate boundary history
```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
NON-REPAINTING COMPLIANCE
To ensure non-repainting behavior, always use this pattern with request.security:
```
= request.security(syminfo.tickerid, htfTimeframe,
[value1 , value2 ], // Use offset for confirmed data
lookahead=barmerge.lookahead_off) // Never use lookahead_on
```
The ` ` offset ensures you're using the previous completed HTF bar, not the current forming bar.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
HISTORY DEPTH PARAMETER
The `historyDepth` parameter controls how many HTF bars are tracked:
• Default: 500 HTF bars
• Maximum: Limited by Pine Script's array constraints
• Higher values = more historical accuracy but more memory usage
• Lower values = less memory but may return `na` for older offsets
Adjust based on your needs:
```
tfa.updateTracker(tracker, 100) // Track 100 HTF bars (light)
tfa.updateTracker(tracker, 1000) // Track 1000 HTF bars (heavier)
```
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
IMPORTANT NOTES
1. One Tracker Per Timeframe : If you need multiple HTFs, create separate trackers for each. The convenience functions (htfBarStartIndex, htfBarEndIndex) only track one TF.
2. Update Every Bar : Always call updateTracker() unconditionally on every bar, not inside conditionals.
3. HTF Only : This library is designed for Higher Timeframe data. For LTF aggregation, use findBarAtTime() for time-based lookups.
4. Drawing Limits : Pine Script has limits on drawing objects. Use box.delete(), line.delete(), label.delete() to clean up old objects.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
TROUBLESHOOTING
Q: My boxes/lines still appear at wrong positions
A: Make sure you're calling updateTracker() on every bar (not inside an if statement) and using the correct htfOffset values.
Q: Functions return na
A: The htfOffset might be larger than available history. Increase historyDepth or use a smaller offset.
Q: Multiple timeframes don't work correctly
A: Don't use the convenience functions for multiple TFs. Create separate HTFTracker instances with createTracker() for each timeframe.
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
CHANGELOG
v1 - Initial release
• HTFTracker pattern for reliable multi-TF tracking
• Synced drawing functions for boxes, lines, labels
• Update functions for extending drawings
• Convenience functions for simple single-TF use cases
Plan Limit TimerA Pine Script library that helps developers monitor script execution time against TradingView's plan-specific timeout limits. Displays a visual debug table with runtime metrics, percentage of limit consumed, and color-coded status warnings.
WHAT THIS LIBRARY DOES
TradingView enforces different script timeout limits based on subscription tier:
• Basic : 20 seconds
• Essential / Plus / Premium : 40 seconds
• Ultimate : 100 seconds
This library measures your script's total execution time and displays it relative to these limits, helping you optimize indicators for users on different plans.
THE DEBUG TABLE
When enabled, a table appears on your chart showing:
• Plan : The selected TradingView subscription tier
• Limit : Maximum allowed execution time for that plan
• Runtime : Measured script execution time
• Per Bar : Average time spent per bar
• Bars : Number of bars processed
• % Used : Percentage of timeout limit consumed (color-coded)
• Status : OK (green), WARNING (yellow), DANGER (orange), or EXCEEDED (red)
HOW IT WORKS
The library captures a timestamp at the start of your script using timenow, then calculates the elapsed time at the end. It compares this against the selected plan's timeout limit to determine percentage used and status.
Technical Note : Pine Script's timenow variable has approximately 1-second precision. Scripts that execute in under 1 second may display 0ms. This is a platform limitation, not a library issue. For detailed per-function profiling, use TradingView's built-in Pine Profiler (More → Profiler mode in the Editor).
EXPORTED FUNCTIONS
startTimer()
Call at the very beginning of your script. Returns a timestamp.
getStats(startTime, plan)
Calculates timing statistics. Returns a TimingStats object with all metrics.
showTimingTable(stats, plan, tablePosition, showOnlyOnLast)
Renders the debug table on the chart.
debugTiming(startTime, plan, tablePosition)
Convenience function combining getStats() and showTimingTable() in one call.
isApproachingLimit(stats, threshold)
Returns true if execution time has reached the specified percentage of the limit.
getRemainingMs(stats)
Returns milliseconds remaining before timeout.
formatSummary(stats)
Returns a compact single-line string for labels or tooltips.
addTimingLabel(stats, barIdx, price, labelStyle, textSize)
Creates a color-coded chart label displaying timing statistics. Useful for visual debugging without the full table. Returns the label object for further customization.
EXPORTED CONSTANTS
• LIMIT_BASIC = 20
• LIMIT_ESSENTIAL = 40
• LIMIT_PLUS = 40
• LIMIT_PREMIUM = 40
• LIMIT_ULTIMATE = 100
EXPORTED TYPE: TimingStats
Object containing:
• totalTimeMs (float): Total execution time in milliseconds
• timePerBarMs (float): Average time per bar
• barsTimed (int): Number of bars measured
• barsSkipped (int): Bars excluded from measurement
• planLimitMs (int): Plan timeout in milliseconds
• percentUsed (float): Percentage of limit consumed
• status (string): "OK", "WARNING", "DANGER", or "EXCEEDED"
HOW TO USE IN YOUR INDICATOR
//@version=6
indicator("My Indicator", overlay = true)
import YourUsername/PlanLimitTimer/1 as timer
// User selects their TradingView plan
planInput = input.string("basic", "Your Plan", options = )
// START TIMING - must be first
startTime = timer.startTimer()
// Your indicator calculations here
sma20 = ta.sma(close, 20)
rsi14 = ta.rsi(close, 14)
plot(sma20)
// END TIMING - must be last
timer.debugTiming(startTime, planInput)
ADVANCED USAGE EXAMPLE
//@version=6
indicator("Advanced Example", overlay = true)
import YourUsername/PlanLimitTimer/1 as timer
planInput = input.string("basic", "Plan", options = )
startTime = timer.startTimer()
// Your calculations...
sma = ta.sma(close, 200)
plot(sma)
// Get stats for programmatic use
stats = timer.getStats(startTime, planInput)
// Option 1: Use addTimingLabel for a quick visual indicator
if barstate.islast
timer.addTimingLabel(stats, bar_index, high)
// Option 2: Show custom warning label if approaching limit
if timer.isApproachingLimit(stats, 70.0) and barstate.islast
label.new(bar_index, low, "Warning: " + timer.formatSummary(stats),
color = color.orange, textcolor = color.white, style = label.style_label_up)
// Display the debug table
timer.showTimingTable(stats, planInput, position.bottom_right)
IMPORTANT LIMITATIONS
1. Precision : Timing precision is approximately 1 second due to timenow behavior. Fast scripts show 0ms.
2. Variability : Results vary based on TradingView server load. The same script may show different times across runs.
3. Total Time Only : This library measures total script execution time, not individual function timing. For per-function analysis, use the Pine Profiler in the Editor.
4. Historical Bars : On historical bars, timenow reflects when the script loaded, not individual bar processing times.
USE CASES
• Optimization Debugging : See how close your script is to timeout limits
• Multi-Plan Support : Help users select appropriate settings for their subscription tier
• Performance Regression : Detect when changes increase execution time
• Documentation : Show users the performance characteristics of your indicator
News2026H2Library "News2026H2" - 2026 News Events Support Lib
f_loadNewsRows()
f_loadExcSevByTypeId()
f_loadExcTagByTypeId()
f_loadExcDelayAfterNewsMins()
Dhan_libLibrary "Dhan_lib"
Overview
Dhan_lib is a Pine Script v6 library designed to help traders automate trading orders via TradingView alerts and webhook integration with the Dhan broker API.
This library generates JSON-formatted alert messages for the following instruments.
Equity (Intraday and Delivery)
Options (CE and PE Buy and Sell)
Futures (Buy and Sell)
These alert strings can be directly used inside TradingView alerts to place live orders through an external webhook setup.
🔹 Supported Instruments
Equity
Intraday Buy and Sell
Delivery Buy and Sell
Options
Call (CE) Buy and Sell
Put (PE) Buy and Sell
ATM, ITM, and OTM strike selection
Intraday and Carry Forward
Futures
Buy and Sell
Intraday and Carry Forward
🔹 Key Features
✅ Pine Script v6 compatible
✅ Clean and reusable library functions
✅ Automatic ATM, ITM, and OTM strike calculation
✅ Expiry date handled via string format YYYY-MM-DD
✅ Fully webhook-ready JSON alert structure
✅ Supports multi-leg order format
✅ Designed for TradingView to Dhan automation
🔹 How to Use
Import the library in your strategy or indicator.
import Shivam_Mandrai/Dhan_lib/1
Call the required function.
order_msg = buy_CE_option("YOUR_SECRET_KEY", "NIFTY", 1)
Use the returned string as the alert message.
alert(order_msg, alert.freq_once_per_bar)
Connect TradingView alerts to your Dhan webhook receiver.
---
🔹 Important Notes
Strike prices are calculated dynamically based on the current chart price (close).
Futures symbols use TradingView continuous contract format such as NIFTY1!.
Quantity refers to the number of lots, not the lot size.
Expiry date must be provided in YYYY-MM-DD format.
⚠️ DISCLAIMER (PLEASE READ CAREFULLY)
This library is provided strictly for educational and automation purposes only.
I am not a SEBI-registered advisor.
I do not guarantee any profit or accuracy of orders.
I am not responsible for any financial loss, missed trades, execution errors, or broker-side issues.
Trading in stocks, options, and futures involves significant risk.
Automated trading can fail due to internet issues, broker API downtime, incorrect webhook configuration, slippage, or market volatility.
👉 Use this library entirely at your own risk.
👉 Always test thoroughly using paper trading or simulation before deploying with real capital.
If you want, I can also:
* Shrink this further for TradingView character limits
* Convert it into a single-paragraph version
* Localize it for Indian retail traders
buy_stock_intraday(secret_key, symbol, qty, exchange)
to buy the stock Intraday
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "TATASTEEL".
qty (int) : int quantity for the order eg-> 1.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
sell_stock_intraday(secret_key, symbol, qty, exchange)
to sell the stock Intraday
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "TATASTEEL".
qty (int) : int quantity for the order eg-> 1.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
buy_stock_delivery(secret_key, symbol, qty, exchange)
to buy the stock delivery
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "TATASTEEL".
qty (int) : int quantity for the order eg-> 1.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
sell_stock_delivery(secret_key, symbol, qty, exchange)
to sell the stock delivery
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "TATASTEEL".
qty (int) : int quantity for the order eg-> 1.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
buy_CE_option(secret_key, symbol, lots, expiry_date, intraday, strike_price_base, ITM_points, OTM_points, exchange)
to buy CE option
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Index / Stock symbol eg-> "NIFTY", "BANKNIFTY".
lots (int) : int Number of lots eg-> 1.
expiry_date (string) : string Option expiry date in YYYY-MM-DD format eg-> "2026-01-20".
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
strike_price_base (float) : float Strike price step size eg-> 50, 100 (default is 100).
ITM_points (float) : float Points below CMP to select ITM strike eg-> 100 (default is 0).
OTM_points (float) : float Points above CMP to select OTM strike eg-> 100 (default is 0).
exchange (string) : string Trading Exchange eg-> "NSE" (default is NSE).
Returns: order string.
buy_PE_option(secret_key, symbol, lots, expiry_date, intraday, strike_price_base, ITM_points, OTM_points, exchange)
to buy PE option
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Index / Stock symbol eg-> "NIFTY", "BANKNIFTY".
lots (int) : int Number of lots eg-> 1.
expiry_date (string) : string Option expiry date in YYYY-MM-DD format eg-> "2026-01-20".
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
strike_price_base (float) : float Strike price step size eg-> 50, 100 (default is 100).
ITM_points (float) : float Points below CMP to select ITM strike eg-> 100 (default is 0).
OTM_points (float) : float Points above CMP to select OTM strike eg-> 100 (default is 0).
exchange (string) : string Trading Exchange eg-> "NSE" (default is NSE).
Returns: order string.
sell_CE_option(secret_key, symbol, lots, expiry_date, intraday, strike_price_base, ITM_points, OTM_points, exchange)
to Sell CE option
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Index / Stock symbol eg-> "NIFTY", "BANKNIFTY".
lots (int) : int Number of lots eg-> 1.
expiry_date (string) : string Option expiry date in YYYY-MM-DD format eg-> "2026-01-20".
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
strike_price_base (float) : float Strike price step size eg-> 50, 100 (default is 100).
ITM_points (float) : float Points below CMP to select ITM strike eg-> 100 (default is 0).
OTM_points (float) : float Points above CMP to select OTM strike eg-> 100 (default is 0).
exchange (string) : string Trading Exchange eg-> "NSE" (default is NSE).
Returns: order string.
sell_PE_option(secret_key, symbol, lots, expiry_date, intraday, strike_price_base, ITM_points, OTM_points, exchange)
to sell PE option
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Index / Stock symbol eg-> "NIFTY", "BANKNIFTY".
lots (int) : int Number of lots eg-> 1.
expiry_date (string) : string Option expiry date in YYYY-MM-DD format eg-> "2026-01-20".
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
strike_price_base (float) : float Strike price step size eg-> 50, 100 (default is 100).
ITM_points (float) : float Points below CMP to select ITM strike eg-> 100 (default is 0).
OTM_points (float) : float Points above CMP to select OTM strike eg-> 100 (default is 0).
exchange (string) : string Trading Exchange eg-> "NSE" (default is NSE).
Returns: order string.
buy_future(secret_key, symbol, lot, intraday, exchange)
to buy the Future
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "NIFTY".
lot (int) : int quantity for the order eg-> 1.
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
sell_future(secret_key, symbol, lot, intraday, exchange)
to sell the Future
Parameters:
secret_key (string) : string Secret Key of the Dhan Account eg-> "S1HgS".
symbol (string) : string Stock symbol eg-> "NIFTY".
lot (int) : int quantity for the order eg-> 1.
intraday (bool) : bool Set true for intraday order, set false for delivery order eg-> true.
exchange (string) : string Trading Exchange eg-> "NSE".
Returns: order string.
RVOL_Core_NSELibrary "RVOL_Core_NSE"
f_rvol(lookbackDays, isNewDay, msSinceSessionStart, volume)
Parameters:
lookbackDays (int)
isNewDay (bool)
msSinceSessionStart (int)
volume (float)
T5_TradeEngineLibrary "T5_TradeEngine"
tick(close_, high_, low_, ema21, ema50, ema200, atrPct, emaGapPct, btcEma50, btcEma200, btcFilterEffective, isBarClose, crossUp21_50, crossDown21_50, allowEntries, exitOnOppositeCross, feeBps, useSR_TPSL, srLeft, srRight, srLookbackPivots, srBufferPct, srMinDistPct, srMinNetAfterFeesPct, srFallbackToATR, tp1CapPct, slCapPct, useTP2Trail, trailExitOnCloseOnly, tp2CapPct, trailCapPct, holdBars)
Parameters:
close_ (float)
high_ (float)
low_ (float)
ema21 (float)
ema50 (float)
ema200 (float)
atrPct (float)
emaGapPct (float)
btcEma50 (float)
btcEma200 (float)
btcFilterEffective (bool)
isBarClose (bool)
crossUp21_50 (bool)
crossDown21_50 (bool)
allowEntries (bool)
exitOnOppositeCross (bool)
feeBps (float)
useSR_TPSL (bool)
srLeft (int)
srRight (int)
srLookbackPivots (int)
srBufferPct (float)
srMinDistPct (float)
srMinNetAfterFeesPct (float)
srFallbackToATR (bool)
tp1CapPct (float)
slCapPct (float)
useTP2Trail (bool)
trailExitOnCloseOnly (bool)
tp2CapPct (float)
trailCapPct (float)
holdBars (int)
ZigZag ATR PctZigZag ATR % Library
A PineScript v6 library for detecting price pivots based on ATR percentage change (volatility shifts) rather than fixed ATR multiples.
How It Works
Traditional ZigZag indicators use a fixed price threshold to detect pivots. This library takes a different approach: pivots are detected when volatility is changing significantly .
The ATR % change measures how much the Average True Range has shifted over a lookback period:
atrPct = 100 * (atr / atr - 1)
Positive ATR % = Volatility expanding (market becoming more volatile)
Negative ATR % = Volatility contracting (market calming down)
Pivots form when |ATR %| exceeds your threshold, capturing turning points during volatility transitions.
Exported Types
Settings - Configuration (ATR length, lookback, threshold, display options)
Pivot - Pivot point data (price, time, direction, volume, ATR %)
ZigZag - Main state container
Exported Functions
newInstance(settings) - Create a new ZigZag instance
update(zz, atr, atrPct) - Update on each bar
getLastPivot(zz) - Get the most recent pivot
getPivot(zz, index) - Get pivot at specific index
getPivotCount(zz) - Get total number of pivots
calcTR() - Calculate True Range
calcATR(length) - Calculate ATR using EMA
calcATRPct(atr, atrPrev) - Calculate ATR % change
calcPricePct(startPrice, endPrice) - Calculate price % change
Usage Example
//@version=6
indicator("My ZigZag", overlay = true)
import DeepEntropy/ZigZagATRPct/1 as zz
// Settings
var zz.Settings settings = zz.Settings.new(
atrLength = 14,
atrLookback = 14,
atrPctThreshold = 5.0,
depth = 10
)
var zz.ZigZag zigZag = zz.newInstance(settings)
// Calculate ATR %
float atr = zz.calcATR(14)
float atrPct = zz.calcATRPct(atr, atr )
// Update
zigZag := zz.update(zigZag, atr, atrPct)
// Access pivots
int count = zz.getPivotCount(zigZag)
if count > 0
zz.Pivot last = zz.getLastPivot(zigZag)
label.new(last.point, text = str.tostring(last.atrPct, "#.##") + "%")
Parameters
ATR Length - Period for ATR calculation (default: 14)
ATR Lookback - Bars to look back for ATR % change (default: 14)
ATR % Threshold - Minimum |ATR %| to trigger pivot detection (default: 5.0)
Depth - Minimum bars between pivots (default: 10)
Use Cases
Identify reversals during volatility regime changes
Filter noise during low-volatility consolidation
Detect breakout pivots when volatility expands
Build volatility-aware trading systems
This library detects when the market's behavior is changing, not just how much price has moved.
ZigZag ATRZigZag ATR Library
A volatility-adaptive ZigZag indicator that uses Average True Range (ATR) instead of fixed percentage deviation to detect pivot points. This makes the ZigZag dynamically adjust to market conditions — tighter during low volatility, wider during high volatility.
Why ATR instead of Percentage?
The standard ZigZag uses a fixed percentage threshold (e.g., 5%) to determine when price has reversed enough to form a new pivot. This approach has limitations:
A 5% move means very different things for a $10 stock vs a $500 stock
During high volatility, fixed percentages create too many pivots (noise)
During low volatility, fixed percentages may miss significant structure
ATR-based deviation solves these issues by measuring reversals in terms of actual volatility , not arbitrary percentages.
Key Features
Volatility-adaptive pivot detection using ATR × multiplier threshold
Automatic adjustment to changing market conditions
Full customization of ATR length and multiplier
Optional line extension to current price
Pivot labels showing price, volume, and price change
Clean library structure for easy integration
Settings
ATR Length — Period for ATR calculation (default: 14)
ATR Multiplier — How many ATRs price must move to confirm a new pivot (default: 2.0)
Depth — Bars required for pivot detection (default: 10)
Extend to Last Bar — Draw provisional line to current price
Display options — Toggle price, volume, and change labels
How to Use
import YourUsername/ZigZagATR/1 as zz
// Create settings
var zz.Settings settings = zz.Settings.new(
14, // ATR length
2.0, // ATR multiplier
10 // Depth
)
// Create ZigZag instance
var zz.ZigZag zigZag = zz.newInstance(settings)
// Calculate ATR and update on each bar
float atrValue = ta.atr(14)
zigZag.update(atrValue)
Exported Types
Settings — Configuration for calculation and display
Pivot — Stores pivot point data, lines, and labels
ZigZag — Main object maintaining state and pivot history
Exported Functions
newInstance(settings) — Creates a new ZigZag object
update(atrValue) — Updates the ZigZag with current ATR (call once per bar)
lastPivot() — Returns the most recent pivot point
Recommended Multiplier Values
1.0 - 1.5 → More sensitive, more pivots, better for scalping
2.0 - 2.5 → Balanced, good for swing trading (default)
3.0+ → Less sensitive, major pivots only, better for position trading
Based on TradingView's official ZigZag library, modified to use ATR-based deviation threshold.
BarCoreLibrary "BarCore"
BarCore is a foundational library for technical analysis, providing essential functions for evaluating the structural properties of candlesticks and inter-bar relationships.
It prioritizes ratio-based metrics (0.0 to 1.0) over absolute prices, making it asset-agnostic and ideal for robust pattern recognition, momentum analysis, and volume-weighted pressure evaluation.
Key modules:
- Structure & Range: High-precision bar and body metrics with relative positioning.
- Wick Dynamics: Absolute and relative wick analysis for identifying price rejection.
- Inter-bar Logic: Containment, coverage, and quantitative price overlap (Ratio-based).
- Gap Intelligence: Real body and price gaps with customizable significance thresholds.
- Flow & Pressure: Volume-weighted buying/selling pressure and Money Flow metrics.
isBuyingBar()
Checks if the bar is a bullish (up) bar, where close is greater than open.
Returns: bool True if the bar closed higher than it opened.
isSellingBar()
Checks if the bar is a bearish (down) bar, where close is less than open.
Returns: bool True if the bar closed lower than it opened.
barMidpoint()
Calculates the absolute midpoint of the bar's total range (High + Low) / 2.
Returns: float The midpoint price of the bar.
barRange()
Calculates the absolute size of the bar's total range (High to Low).
Returns: float The absolute difference between high and low.
barRangeMidpoint()
Calculates half of the bar's total range size.
Returns: float Half the bar's range size.
realBodyHigh()
Returns the higher price between the open and close.
Returns: float The top of the real body.
realBodyLow()
Returns the lower price between the open and close.
Returns: float The bottom of the real body.
realBodyMidpoint()
Calculates the absolute midpoint of the bar's real body.
Returns: float The midpoint price of the real body.
realBodyRange()
Calculates the absolute size of the bar's real body.
Returns: float The absolute difference between open and close.
realBodyRangeMidpoint()
Calculates half of the bar's real body size.
Returns: float Half the real body size.
upperWickRange()
Calculates the absolute size of the upper wick.
Returns: float The range from high to the real body high.
lowerWickRange()
Calculates the absolute size of the lower wick.
Returns: float The range from the real body low to low.
openRatio()
Returns the location of the open price relative to the bar's total range (0.0 at low to 1.0 at high).
Returns: float The ratio of the distance from low to open, divided by the total range.
closeRatio()
Returns the location of the close price relative to the bar's total range (0.0 at low to 1.0 at high).
Returns: float The ratio of the distance from low to close, divided by the total range.
realBodyRatio()
Calculates the ratio of the real body size to the total bar range.
Returns: float The real body size divided by the bar range. Returns 0 if barRange is 0.
upperWickRatio()
Calculates the ratio of the upper wick size to the total bar range.
Returns: float The upper wick size divided by the bar range. Returns 0 if barRange is 0.
lowerWickRatio()
Calculates the ratio of the lower wick size to the total bar range.
Returns: float The lower wick size divided by the bar range. Returns 0 if barRange is 0.
upperWickToBodyRatio()
Calculates the ratio of the upper wick size to the real body size.
Returns: float The upper wick size divided by the real body size. Returns 0 if realBodyRange is 0.
lowerWickToBodyRatio()
Calculates the ratio of the lower wick size to the real body size.
Returns: float The lower wick size divided by the real body size. Returns 0 if realBodyRange is 0.
totalWickRatio()
Calculates the ratio of the total wick range (Upper Wick + Lower Wick) to the total bar range.
Returns: float The total wick range expressed as a ratio of the bar's total range. Returns 0 if barRange is 0.
isBodyExpansion()
Checks if the current bar's real body range is larger than the previous bar's real body range (body expansion).
Returns: bool True if realBodyRange() > realBodyRange() .
isBodyContraction()
Checks if the current bar's real body range is smaller than the previous bar's real body range (body contraction).
Returns: bool True if realBodyRange() < realBodyRange() .
isWithinPrevBar(inclusive)
Checks if the current bar's range is entirely within the previous bar's range.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if High < High AND Low > Low .
isCoveringPrevBar(inclusive)
Checks if the current bar's range fully covers the entire previous bar's range.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if High > High AND Low < Low .
isWithinPrevBody(inclusive)
Checks if the current bar's real body is entirely inside the previous bar's real body.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if the current body is contained inside the previous body.
isCoveringPrevBody(inclusive)
Checks if the current bar's real body fully covers the previous bar's real body.
Parameters:
inclusive (bool) : If true, allows equality (<=, >=). Default is false.
Returns: bool True if the current body fully covers the previous body.
isOpenWithinPrevBody(inclusive)
Checks if the current bar's open price falls within the real body range of the previous bar.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if the open price is between the previous bar's real body high and real body low.
isCloseWithinPrevBody(inclusive)
Checks if the current bar's close price falls within the real body range of the previous bar.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if the close price is between the previous bar's real body high and real body low.
isPrevOpenWithinBody(inclusive)
Checks if the previous bar's open price falls within the current bar's real body range.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if open is between the current bar's real body high and real body low.
isPrevCloseWithinBody(inclusive)
Checks if the previous bar's closing price falls within the current bar's real body range.
Parameters:
inclusive (bool) : If true, includes the boundary prices. Default is false.
Returns: bool True if close is between the current bar's real body high and real body low.
isOverlappingPrevBar()
Checks if there is any price overlap between the current bar's range and the previous bar's range.
Returns: bool True if the current bar's range has any intersection with the previous bar's range.
bodyOverlapRatio()
Calculates the percentage of the current real body that overlaps with the previous real body.
Returns: float The overlap ratio (0.0 to 1.0). 1.0 means the current body is entirely within the previous body's price range.
isCompletePriceGapUp()
Checks for a complete price gap up where the current bar's low is strictly above the previous bar's high, meaning there is zero price overlap between the two bars.
Returns: bool True if the current low is greater than the previous high.
isCompletePriceGapDown()
Checks for a complete price gap down where the current bar's high is strictly below the previous bar's low, meaning there is zero price overlap between the two bars.
Returns: bool True if the current high is less than the previous low.
isRealBodyGapUp()
Checks for a gap between the current and previous real bodies.
Returns: bool True if the current body is completely above the previous body.
isRealBodyGapDown()
Checks for a gap between the current and previous real bodies.
Returns: bool True if the current body is completely below the previous body.
gapRatio()
Calculates the percentage difference between the current open and the previous close, expressed as a decimal ratio.
Returns: float The gap ratio (positive for gap up, negative for gap down). Returns 0 if the previous close is 0.
gapPercentage()
Calculates the percentage difference between the current open and the previous close.
Returns: float The gap percentage (positive for gap up, negative for gap down). Returns 0 if previous close is 0.
isGapUp()
Checks for a basic gap up, where the current bar's open is strictly higher than the previous bar's close. This is the minimum condition for a gap up.
Returns: bool True if the current open is greater than the previous close (i.e., gapRatio is positive).
isGapDown()
Checks for a basic gap down, where the current bar's open is strictly lower than the previous bar's close. This is the minimum condition for a gap down.
Returns: bool True if the current open is less than the previous close (i.e., gapRatio is negative).
isSignificantGapUp(minRatio)
Checks if the current bar opened significantly higher than the previous close, as defined by a minimum percentage ratio.
Parameters:
minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).
Returns: bool True if the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio.
isSignificantGapDown(minRatio)
Checks if the current bar opened significantly lower than the previous close, as defined by a minimum percentage ratio.
Parameters:
minRatio (float) : The minimum required gap percentage ratio. Default is 0.03 (3%).
Returns: bool True if the absolute value of the gap ratio (open vs. previous close) is greater than or equal to the minimum ratio.
trueRangeComponentHigh()
Calculates the absolute distance from the current bar's High to the previous bar's Close, representing one of the components of the True Range.
Returns: float The absolute difference: |High - Close |.
trueRangeComponentLow()
Calculates the absolute distance from the current bar's Low to the previous bar's Close, representing one of the components of the True Range.
Returns: float The absolute difference: |Low - Close |.
isUpperWickDominant(minRatio)
Checks if the upper wick is significantly long relative to the total range.
Parameters:
minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).
Returns: bool True if the upper wick dominates the bar's range.
isUpperWickNegligible(maxRatio)
Checks if the upper wick is very small relative to the total range.
Parameters:
maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).
Returns: bool True if the upper wick is negligible.
isLowerWickDominant(minRatio)
Checks if the lower wick is significantly long relative to the total range.
Parameters:
minRatio (float) : Minimum ratio of the wick to the total bar range. Default is 0.7 (70%).
Returns: bool True if the lower wick dominates the bar's range.
isLowerWickNegligible(maxRatio)
Checks if the lower wick is very small relative to the total range.
Parameters:
maxRatio (float) : Maximum ratio of the wick to the total bar range. Default is 0.05 (5%).
Returns: bool True if the lower wick is negligible.
isSymmetric(maxTolerance)
Checks if the upper and lower wicks are roughly equal in length.
Parameters:
maxTolerance (float) : Maximum allowable percentage difference between the two wicks. Default is 0.15 (15%).
Returns: bool True if wicks are symmetric within the tolerance level.
isMarubozuBody(minRatio)
Candle with a very large body relative to the total range (minimal wicks).
Parameters:
minRatio (float) : Minimum body size ratio. Default is 0.9 (90%).
Returns: bool True if the bar has minimal wicks (Marubozu body).
isLargeBody(minRatio)
Candle with a large body relative to the total range.
Parameters:
minRatio (float) : Minimum body size ratio. Default is 0.6 (60%).
Returns: bool True if the bar has a large body.
isSmallBody(maxRatio)
Candle with a small body relative to the total range.
Parameters:
maxRatio (float) : Maximum body size ratio. Default is 0.4 (40%).
Returns: bool True if the bar has small body.
isDojiBody(maxRatio)
Candle with a very small body relative to the total range (indecision).
Parameters:
maxRatio (float) : Maximum body size ratio. Default is 0.1 (10%).
Returns: bool True if the bar has a very small body.
isLowerWickExtended(minRatio)
Checks if the lower wick is significantly extended relative to the real body size.
Parameters:
minRatio (float) : Minimum required ratio of the lower wick length to the real body size. Default is 2.0 (Lower wick must be at least twice the body's size).
Returns: bool True if the lower wick's length is at least `minRatio` times the size of the real body.
isUpperWickExtended(minRatio)
Checks if the upper wick is significantly extended relative to the real body size.
Parameters:
minRatio (float) : Minimum required ratio of the upper wick length to the real body size. Default is 2.0 (Upper wick must be at least twice the body's size).
Returns: bool True if the upper wick's length is at least `minRatio` times the size of the real body.
isStrongBuyingBar(minCloseRatio, maxOpenRatio)
Checks for a bar with strong bullish momentum (open near low, close near high), indicating high conviction.
Parameters:
minCloseRatio (float) : Minimum required ratio for the close location (relative to range, e.g., 0.7 means close must be in the top 30%). Default is 0.7 (70%).
maxOpenRatio (float) : Maximum allowed ratio for the open location (relative to range, e.g., 0.3 means open must be in the bottom 30%). Default is 0.3 (30%).
Returns: bool True if the bar is bullish, opened in the low extreme, and closed in the high extreme.
isStrongSellingBar(maxCloseRatio, minOpenRatio)
Checks for a bar with strong bearish momentum (open near high, close near low), indicating high conviction.
Parameters:
maxCloseRatio (float) : Maximum allowed ratio for the close location (relative to range, e.g., 0.3 means close must be in the bottom 30%). Default is 0.3 (30%).
minOpenRatio (float) : Minimum required ratio for the open location (relative to range, e.g., 0.7 means open must be in the top 30%). Default is 0.7 (70%).
Returns: bool True if the bar is bearish, opened in the high extreme, and closed in the low extreme.
isWeakBuyingBar(maxCloseRatio, maxBodyRatio)
Identifies a bar that is technically bullish but shows significant weakness, characterized by a failure to close near the high and a small body size.
Parameters:
maxCloseRatio (float) : Maximum allowed ratio for the close location relative to the range (e.g., 0.6 means the close must be in the bottom 60% of the bar's range). Default is 0.6 (60%).
maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).
Returns: bool True if the bar is bullish, but its close is weak and its body is small.
isWeakSellingBar(minCloseRatio, maxBodyRatio)
Identifies a bar that is technically bearish but shows significant weakness, characterized by a failure to close near the low and a small body size.
Parameters:
minCloseRatio (float) : Minimum required ratio for the close location relative to the range (e.g., 0.4 means the close must be in the top 60% of the bar's range). Default is 0.4 (40%).
maxBodyRatio (float) : Maximum allowed ratio for the real body size relative to the bar's range (e.g., 0.4 means the body is small). Default is 0.4 (40%).
Returns: bool True if the bar is bearish, but its close is weak and its body is small.
balanceOfPower()
Measures the net pressure of buyers vs. sellers within the bar, normalized to the bar's range.
Returns: float A value between -1.0 (strong selling) and +1.0 (strong buying), representing the strength and direction of the close relative to the open.
buyingPressure()
Measures the net buying volume pressure based on the close location and volume.
Returns: float A numerical value representing the volume weighted buying pressure.
sellingPressure()
Measures the net selling volume pressure based on the close location and volume.
Returns: float A numerical value representing the volume weighted selling pressure.
moneyFlowMultiplier()
Calculates the Money Flow Multiplier (MFM), which is the price component of Money Flow and CMF.
Returns: float A normalized value from -1.0 (strong selling) to +1.0 (strong buying), representing the net directional pressure.
moneyFlowVolume()
Calculates the Money Flow Volume (MFV), which is the Money Flow Multiplier weighted by the bar's volume.
Returns: float A numerical value representing the volume-weighted money flow. Positive = buying dominance; negative = selling dominance.
isAccumulationBar()
Checks for basic accumulation on the current bar, requiring both positive Money Flow Volume and a buying bar (closing higher than opening).
Returns: bool True if the bar exhibits buying dominance through its internal range location and is a buying bar.
isDistributionBar()
Checks for basic distribution on the current bar, requiring both negative Money Flow Volume and a selling bar (closing lower than opening).
Returns: bool True if the bar exhibits selling dominance through its internal range location and is a selling bar.
CausalityLib - granger casuality and transfer entropy helpersLibrary "CausalityLib"
Causality Analysis Library - Transfer Entropy, Granger Causality, and Causality Filtering
f_shannon_entropy(data, num_bins)
Calculate Shannon entropy of data distribution
Parameters:
data (array) : Array of continuous values
num_bins (int) : Number of bins for discretization
Returns: Entropy value (higher = more randomness)
f_calculate_te_score(primary_arr, ticker_arr, window, bins, lag)
Calculate Transfer Entropy from source to target
Parameters:
primary_arr (array) : Target series (e.g., primary ticker returns)
ticker_arr (array) : Source series (e.g., basket ticker returns)
window (int) : Window size for TE calculation
bins (int) : Number of bins for discretization
lag (int) : Lag for source series
Returns: - TE score and direction (-1 or 1)
f_correlation_at_lag(primary_arr, ticker_arr, lag, window, correlation_method)
Calculate Pearson correlation at specific lag
Parameters:
primary_arr (array) : Primary series
ticker_arr (array) : Ticker series
lag (int) : Lag value (positive = ticker lags primary)
window (int) : Window size for correlation
correlation_method (string) : Correlation method to use ("Pearson", "Spearman", "Kendall")
Returns: Correlation coefficient
f_calculate_granger_score(primary_arr, ticker_arr, window, max_lag, correlation_method)
Calculate Granger causality score with lag testing
Parameters:
primary_arr (array) : Primary series
ticker_arr (array) : Ticker series
window (int) : Window size for correlation
max_lag (int) : Maximum lag to test
correlation_method (string) : Correlation method to use
Returns: - Granger score and directional beta
f_partial_correlation(x_arr, y_arr, z_arr, window)
Calculate partial correlation between X and Y controlling for Z
Parameters:
x_arr (array) : First series
y_arr (array) : Second series
z_arr (array) : Mediator series
window (int) : Window size for correlation
Returns: Partial correlation coefficient
f_pcmci_filter_score(raw_score, primary_arr, ticker_arr, mediator1, mediator2, mediator3, mediator4, window)
PCMCI Filter: Adjust Granger score by checking for mediating tickers
Parameters:
raw_score (float) : Original Granger score
primary_arr (array) : Primary series
ticker_arr (array) : Ticker series
mediator1 (array) : First potential mediator series
mediator2 (array) : Second potential mediator series
mediator3 (array) : Third potential mediator series
mediator4 (array) : Fourth potential mediator series
window (int) : Window size for correlation
Returns: Filtered score (reduced if causality is indirect/spurious)
T5_EngineLibrary "T5_Engine"
run(ema50, ema200, atrPct, emaGapPct, btcEma50, btcEma200, isBarClose, crossUp21_50, crossDown21_50, useBTCFilter, useSpreadFilter, minSpreadPctFixed, useAdaptiveSpread, spreadBaseMinPct, spreadAtrK, atrLowTh, atrHighTh)
Parameters:
ema50 (float)
ema200 (float)
atrPct (float)
emaGapPct (float)
btcEma50 (float)
btcEma200 (float)
isBarClose (bool)
crossUp21_50 (bool)
crossDown21_50 (bool)
useBTCFilter (bool)
useSpreadFilter (bool)
minSpreadPctFixed (float)
useAdaptiveSpread (bool)
spreadBaseMinPct (float)
spreadAtrK (float)
atrLowTh (float)
atrHighTh (float)
