BybitMinOrderSizeBybit Order Quantity Compliance Library
This library provides all utility functions required for TradingView strategies
that execute orders on Bybit via webhooks.
Problem:
Bybit enforces two strict rules on every order submitted:
Minimum Order Size – each symbol has its own minimum quantity.
Quantity Precision – each symbol requires rounding to the correct number of decimals.
TradingView does not expose this metadata, so strategies can easily submit
quantities that Bybit rejects as invalid.
Solution (This Library):
This library embeds full Bybit contract metadata, including:
A complete mapping of Bybit symbols → minimum order size
A complete mapping of Bybit symbols → allowed precision (decimal places)
A helper to normalize tickers (removing `.P` suffix for Bybit perpetuals)
It also exposes utility functions to automatically make your quantities valid:
`normalizeTicker()` — removes `.P` for consistent lookup
`getMinOrderSize()` — returns the correct minimum order size
`getPrecisionForTicker()` — returns required quantity precision
`floorQty()` — floors quantities to valid minimum increments
`roundQty()` — rounds quantities to valid decimal precision
Use Cases:
Ensuring webhook strategies never send too-small orders
Rounding limit/market orders correctly before execution
Making Pine strategies execution-accurate for Bybit
Avoiding "order rejected: qty too small / invalid precision" errors
This library is recommended for:
Live trading via TradingView → Bybit webhooks
Backtesting strategies that simulate real Bybit constraints
Source: www.bybit.com
Updated: 2025-11-25 — Bybit contract metadata
normalizeTicker(symbol)
Normalizes Bybit perpetual tickers by removing the ".P" suffix.
precisionFromMinOrder(minOrder)
Derives precision (decimal places) from minimum order size.
getMinOrderSize(symbol)
Retrieves the minimum order size for the current or given symbol.
getPrecisionForTicker(symbol)
Retrieves the required quantity precision (decimal places) for a given Bybit symbol.
floorQty(qty, symbol)
Rounds a quantity down to the nearest valid minimum order size for a given symbol.
roundQty(qty, symbol)
Rounds a quantity to the valid precision for the specified symbol.
Indicadores y estrategias
Mirpapa_Lib_4waveLibrary "Mirpapa_Lib_4wave"
Library for MACD 4Wave Trading System - Type definitions and calculation functions
_macd_calc(_src, _fast, _slow, _signal)
Parameters:
_src (float)
_fast (simple int)
_slow (simple int)
_signal (simple int)
_normalize(_value, _current_max, _current_norm)
Parameters:
_value (float)
_current_max (float)
_current_norm (float)
calc_visual(_current, _prev_val, _color_up, _color_down, _transparency_up, _transparency_down)
Parameters:
_current (float)
_prev_val (float)
_color_up (color)
_color_down (color)
_transparency_up (int)
_transparency_down (int)
_detect_line_position(_line1, _line2)
Parameters:
_line1 (float)
_line2 (float)
_get_divergence_score(_div)
Parameters:
_div (_DivergenceCounter)
calculate_score_1_HighTrend(_htfHigh_macd_norm, _htfHigh_macd_norm_prev, _max_score)
Parameters:
_htfHigh_macd_norm (float)
_htfHigh_macd_norm_prev (float)
_max_score (int)
calculate_score_1_TRIX_Align(_trix_norm, _trix_norm_prev, _max_score)
Parameters:
_trix_norm (float)
_trix_norm_prev (float)
_max_score (int)
calculate_score_2_MidAlign(_htfMid_macd_norm, _htfMid_macd_norm_prev, _htfHigh_macd_norm, _htfHigh_macd_norm_prev, _max_score)
Parameters:
_htfMid_macd_norm (float)
_htfMid_macd_norm_prev (float)
_htfHigh_macd_norm (float)
_htfHigh_macd_norm_prev (float)
_max_score (int)
calculate_score_3_LowAlign(_htfLow_macd_norm, _htfLow_macd_norm_prev, _htfMid_macd_norm, _htfMid_macd_norm_prev, _htfHigh_macd_norm, _htfHigh_macd_norm_prev, _max_score)
Parameters:
_htfLow_macd_norm (float)
_htfLow_macd_norm_prev (float)
_htfMid_macd_norm (float)
_htfMid_macd_norm_prev (float)
_htfHigh_macd_norm (float)
_htfHigh_macd_norm_prev (float)
_max_score (int)
calculate_score_4_CurrentAlign(_current_macd_norm, _htfMid_macd_norm, _htfHigh_macd_norm, _max_score)
Parameters:
_current_macd_norm (float)
_htfMid_macd_norm (float)
_htfHigh_macd_norm (float)
_max_score (int)
calculate_score_4_CurrentHistogram(_hist, _hist_prev, _max_score)
Parameters:
_hist (float)
_hist_prev (float)
_max_score (int)
calculate_score_4_MACD_Cross(_current_norm, _htfLow_norm, _htfMid_norm, _htfHigh_norm, _max_score)
Parameters:
_current_norm (float)
_htfLow_norm (float)
_htfMid_norm (float)
_htfHigh_norm (float)
_max_score (int)
calculate_score_4_MACD_Extreme(_htfMid_norm, _htfMid_norm_prev, _htfLow_norm, _htfLow_norm_prev, _htfHigh_norm, _oversold_line, _overbought_line, _max_score)
Parameters:
_htfMid_norm (float)
_htfMid_norm_prev (float)
_htfLow_norm (float)
_htfLow_norm_prev (float)
_htfHigh_norm (float)
_oversold_line (float)
_overbought_line (float)
_max_score (int)
calculate_score_4_MACD_Convergence(_htfLow_norm, _htfMid_norm, _htfHigh_norm, _max_score)
Parameters:
_htfLow_norm (float)
_htfMid_norm (float)
_htfHigh_norm (float)
_max_score (int)
calculate_score_5_RSI_Divergence(_rsiDivBull, _rsiDivBear, _max_score)
Parameters:
_rsiDivBull (_DivergenceCounter)
_rsiDivBear (_DivergenceCounter)
_max_score (int)
calculate_score_5_MACD_Divergence(_macdDivBull, _macdDivBear, _max_score)
Parameters:
_macdDivBull (_DivergenceCounter)
_macdDivBear (_DivergenceCounter)
_max_score (int)
calculate_score_5_Bonus_Divergence(_rsiDivBull, _rsiDivBear, _macdDivBull, _macdDivBear, _trixDivBull, _trixDivBear, _max_score)
Parameters:
_rsiDivBull (_DivergenceCounter)
_rsiDivBear (_DivergenceCounter)
_macdDivBull (_DivergenceCounter)
_macdDivBear (_DivergenceCounter)
_trixDivBull (_DivergenceCounter)
_trixDivBear (_DivergenceCounter)
_max_score (int)
_calculate_total_score(_score_data)
Parameters:
_score_data (_Score)
_FilterSettings
Fields:
_need_macd (series bool)
_need_rsi (series bool)
_need_trix (series bool)
_show_indicator_visual (series bool)
_show_trade_visual (series bool)
_Macd
Fields:
_macd (series float)
_signal (series float)
_hist (series float)
_hist_prev (series float)
_max (series float)
_norm (series float)
_normPrev (series float)
_Rsi
Fields:
_value (series float)
_norm (series float)
_normPrev (series float)
_Trix
Fields:
_value (series float)
_max (series float)
_norm (series float)
_normPrev (series float)
_Divergence
Fields:
_bull (series bool)
_bear (series bool)
_DivergenceCounter
Fields:
_div1 (series int)
_div2 (series int)
_div3 (series int)
_Score
Fields:
_1_MACD_HighTrend (series float)
_1_TRIX_Align (series float)
_2_MACD_MidAlign (series float)
_3_MACD_LowAlign (series float)
_4_MACD_CurrentAlign (series float)
_4_MACD_CurrentHistogram (series float)
_4_MACD_Cross (series float)
_4_MACD_Extreme (series float)
_4_MACD_Convergence (series float)
_5_RSI_Divergence (series float)
_5_MACD_Divergence (series float)
_5_Bonus_Divergence (series float)
_total (series float)
_canContinue (series bool)
_directionAligned (series bool)
_IsNewBar
Fields:
_high (series bool)
_mid (series bool)
_low (series bool)
_Visual
Fields:
_prev (series float)
_increasing (series bool)
_color (series color)
_ScoreThresholds
Fields:
_1_MACD_HighTrend_MAX (series int)
_1_TRIX_Align_MAX (series int)
_2_MACD_MidAlign_MAX (series int)
_3_MACD_LowAlign_MAX (series int)
_4_MACD_CurrentAlign_MAX (series int)
_4_MACD_CurrentHistogram_MAX (series int)
_4_MACD_Cross_MAX (series int)
_4_MACD_Extreme_MAX (series int)
_4_MACD_Convergence_MAX (series int)
_5_RSI_Divergence_MAX (series int)
_5_MACD_Divergence_MAX (series int)
_5_Bonus_Divergence_MAX (series int)
_EntryThresholds
Fields:
_LONG (series int)
_SHORT (series int)
_StopLossThresholds
Fields:
_PCT (series float)
_SCORE_DROP (series int)
_MDDThresholds
Fields:
_DAILY_LIMIT (series float)
_PositionSizeMultipliers
Fields:
_SIZE_120_PLUS (series float)
_SIZE_100_119 (series float)
_SIZE_90_99 (series float)
_SIZE_75_89 (series float)
_SIZE_60_74 (series float)
_MDDMultipliers
Fields:
_MULT_0_1 (series float)
_MULT_1_2 (series float)
_MULT_2_25 (series float)
_MULT_25_3 (series float)
_MULT_3_PLUS (series float)
_TrixThresholds
Fields:
_STRONG_BULL (series float)
_WEAK_BULL (series float)
_STRONG_BEAR (series float)
_WEAK_BEAR (series float)
_RsiThresholds
Fields:
_OVERSOLD_STRONG (series int)
_OVERSOLD_WEAK (series int)
_OVERBOUGHT_WEAK (series int)
_OVERBOUGHT_STRONG (series int)
_MacdThresholds
Fields:
_OVERSOLD_STRONG (series int)
_OVERSOLD_WEAK (series int)
_OVERBOUGHT_WEAK (series int)
_OVERBOUGHT_STRONG (series int)
_LineThresholds
Fields:
_TOP (series int)
_MIDDLE (series int)
_BOTTOM (series int)
_HIST_BASE (series int)
_Transparency
Fields:
_MAX (series int)
_STRONG (series int)
_MEDIUM (series int)
_WEAK (series int)
_MIN (series int)
_FILL (series int)
_TimeframeNames
Fields:
_CURRENT (series string)
_HTF1 (series string)
_HTF2 (series string)
_HTF3 (series string)
_IndicatorColors
Fields:
_RSI (series color)
_TRIX (series color)
_MACD_CURRENT (series color)
_MACD_HTF1 (series color)
_MACD_HTF2 (series color)
_MACD_HTF3 (series color)
_EntryState
Fields:
_WAITING (series string)
_ENTERED (series string)
_CLOSED (series string)
_TradeState
Fields:
_state (series string)
_is_long (series bool)
_signal_bar (series int)
_signal_score (series float)
_signal_high (series float)
_signal_low (series float)
_entry_bar (series int)
_entry_price (series float)
_entry_score (series float)
_entry_reason (series string)
_exit_bar (series int)
_exit_price (series float)
_exit_reason (series string)
_h_line (series line)
_v_line (series line)
_signal_label (series label)
_entry_label (series label)
_exit_label (series label)
RLSR logreg_support_libLibrary "logreg_support_lib"
sigmoid(z)
Parameters:
z (float)
prng01(seed1, seed2)
Parameters:
seed1 (float)
seed2 (float)
normalize(value, minval, maxval)
Parameters:
value (float)
minval (float)
maxval (float)
calcpercentilefast(arr, percentile)
Parameters:
arr (array)
percentile (float)
calcpercentile_series_sampled(s, length, percentile, stride)
Parameters:
s (float)
length (int)
percentile (float)
stride (int)
calcRangeWithLog(value, minval, maxval, uselog)
Parameters:
value (float)
minval (float)
maxval (float)
uselog (bool)
calcMomentumAdvanced(src, length, momType)
Parameters:
src (float)
length (simple int)
momType (string)
normalizeMomentumByType(rawMom, momType, momMin, momMax, momNorm)
Parameters:
rawMom (float)
momType (string)
momMin (float)
momMax (float)
momNorm (float)
normalizeMomentumByTypeExt(rawMom, momType, momMin, momMax, momNorm, bouncingdecay)
Parameters:
rawMom (float)
momType (string)
momMin (float)
momMax (float)
momNorm (float)
bouncingdecay (float)
calcrollingstddev(src, length)
Parameters:
src (float)
length (int)
addlog(buffer, level, msg)
Parameters:
buffer (string)
level (string)
msg (string)
calcfeaturecorrelation(x1, x2)
Parameters:
x1 (array)
x2 (array)
calcnoiseratio(src, lookback)
Parameters:
src (float)
lookback (int)
calccompatibilityscore(x1, x2)
Parameters:
x1 (array)
x2 (array)
getfuturereturn(offset, returnlookback)
Parameters:
offset (int)
returnlookback (int)
calculatema(source, length, matype)
Parameters:
source (float)
length (simple int)
matype (string)
adaptive_trigger_for_source(src, enabled, freeze, lookback, threshold, volahistory)
Parameters:
src (float)
enabled (bool)
freeze (bool)
lookback (int)
threshold (float)
volahistory (array)
checkadaptivetrigger5(s1, enabled1, freeze1, hist1, s2, enabled2, freeze2, hist2, s3, enabled3, freeze3, hist3, s4, enabled4, freeze4, hist4, s5, enabled5, freeze5, hist5, lookback, threshold)
Parameters:
s1 (float)
enabled1 (bool)
freeze1 (bool)
hist1 (array)
s2 (float)
enabled2 (bool)
freeze2 (bool)
hist2 (array)
s3 (float)
enabled3 (bool)
freeze3 (bool)
hist3 (array)
s4 (float)
enabled4 (bool)
freeze4 (bool)
hist4 (array)
s5 (float)
enabled5 (bool)
freeze5 (bool)
hist5 (array)
lookback (int)
threshold (float)
ring_start_index(rb_write_idx, rb_count, rb_cap)
Parameters:
rb_write_idx (int)
rb_count (int)
rb_cap (int)
reversalLibrary "reversals"
psar(af_start, af_increment, af_max)
Calculates Parabolic Stop And Reverse (SAR)
Parameters:
af_start (simple float) : Initial acceleration factor (Wilder's original: 0.02)
af_increment (simple float) : Acceleration factor increment per new extreme (Wilder's original: 0.02)
af_max (simple float) : Maximum acceleration factor (Wilder's original: 0.20)
Returns: SAR value (stop level for current trend)
fractals()
Detects Williams Fractal patterns (5-bar pattern)
Returns: Tuple with fractal values (na if no fractal)
swings(lookback, source_high, source_low)
Detects swing highs and swing lows using lookback period
Parameters:
lookback (simple int) : Number of bars on each side to confirm swing point
source_high (float) : Price series for swing high detection (typically high)
source_low (float) : Price series for swing low detection (typically low)
Returns: Tuple with swing point values (na if no swing)
pivot(tf)
Calculates classic/standard/floor pivot points
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotcam(tf)
Calculates Camarilla pivot points with 8 levels for short-term trading
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotdem(tf)
Calculates d-mark pivot points with conditional open/close logic
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels (only 3 levels)
pivotext(tf)
Calculates extended traditional pivot points with R4-R5 and S4-S5 levels
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotfib(tf)
Calculates Fibonacci pivot points using Fibonacci ratios
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
pivotwood(tf)
Calculates Woodie's pivot points with weighted closing price
Parameters:
tf (simple string) : Timeframe for pivot calculation ("D", "W", "M")
Returns: Tuple with pivot levels
libpublicLibrary "libpublic"
sma(src, len)
Simple Moving Average
Parameters:
src (float) : Series to use
len (int) : Filtering length
Returns: Filtered series
ema(src, len)
Exponential Moving Average
Parameters:
src (float) : Series to use
len (int) : Filtering length
Returns: Filtered series
rma(src, len)
Wilder's Smoothing (Running Moving Average)
Parameters:
src (float) : Series to use
len (int) : Filtering length
Returns: Filtered series
hma(src, len)
Hull Moving Average
Parameters:
src (float) : Series to use
len (int) : Filtering length
Returns: Filtered series
vwma(src, len)
Volume Weighted Moving Average
Parameters:
src (float) : Series to use
len (int) : Filtering length
Returns: Filtered series
jma(src, len, phase)
Jurik MA
Parameters:
src (float) : Series to use
len (int) : Filtering length
phase (int) : JMA Phase
Returns: Filtered series
c_Ema(_src, _length)
Parameters:
_src (float)
_length (int)
c_zlema(_src, _length)
Parameters:
_src (float)
_length (int)
to_pips(_v)
Convert price to pips.
Parameters:
_v (float) : Price
Returns: Pips
toPips(_v)
Parameters:
_v (float)
get_day(_n)
Get the day of the week
Parameters:
_n (int) : Number of day of week
clear_lines(_arr, _min)
Deletes the lines included in the array.
Parameters:
_arr (array) : Array of lines
_min (int) : Deletes the lines included in the array.
clear_labels(_arr, _min)
Deletes the labels included in the array.
Parameters:
_arr (array) : Array of labels
_min (int) : Deletes the labels included in the array.
clear_boxes(_arr, _min)
Deletes the boxes included in the array.
Parameters:
_arr (array) : Array of boxes
_min (int) : Deletes the boxes included in the array.
tfToInt(_timeframe)
Parameters:
_timeframe (string)
tfCurrentView(_tf)
Parameters:
_tf (float)
f_round(_val, _decimals)
Parameters:
_val (float)
_decimals (int)
getTablePos(_switch)
Parameters:
_switch (string)
getTxtSize(_switch)
Parameters:
_switch (string)
getTendChar(_trendChar)
Parameters:
_trendChar (string)
blueWaves(src, chlLen, avgLen)
Parameters:
src (float)
chlLen (int)
avgLen (int)
candleType(_candle)
Parameters:
_candle (int)
normalizeVolume(_vol, _precision)
Parameters:
_vol (float)
_precision (string)
InSession(sessionTime, sessionTimeZone)
Parameters:
sessionTime (string)
sessionTimeZone (string)
IsSessionStart(sessionTime, sessionTimeZone)
Parameters:
sessionTime (string)
sessionTimeZone (string)
createSessionTime(_timeOffsetStart, _timeOffsetEnd, _offsetTypeStart, _offsetTypeEnd, sessionTimeZone)
Parameters:
_timeOffsetStart (int)
_timeOffsetEnd (int)
_offsetTypeStart (string)
_offsetTypeEnd (string)
sessionTimeZone (string)
clrsLibrary "clrs"
Helpers for color manipulations
opacify(oldColor, opacity)
Applies opacity to color
Parameters:
oldColor (color) : color
opacity (float) : opacity
Returns: color with opacity
getColorsRange(topColor, bottomColor, numColors)
Gets two colors as parameters and number of colors you need. Returns range of colors between them
Parameters:
topColor (color) : color color
bottomColor (color) : color
numColors (int) : number of colors in range
Mirpapa_Lib_SumBoxLibrary "Mirpapa_Lib_SumBox"
CreateSumCandleStates()
CreateSumCandleStates
@desc Creates a set of sum candle state strings.
Returns (SumCandleStates): State string set (pending, confirmed, completed)
Returns: SumCandleStates state string set
CreateSumCandleData(sumOpen, sumHigh, sumLow, sumClose, sumStartTime, sumEndTime, sumHighTime, sumLowTime, state)
CreateSumCandleData
@desc Creates sum candle data (factory function).
sumOpen (float): Sum open price
sumHigh (float): Sum high price
sumLow (float): Sum low price
sumClose (float): Sum close price
sumStartTime (int): Sum start time (milliseconds)
sumEndTime (int): Sum end time (milliseconds)
sumHighTime (int): High point occurrence time (milliseconds)
sumLowTime (int): Low point occurrence time (milliseconds)
state (string): State (pending/confirmed/completed)
Returns (SumCandleData): Created sum candle data
Parameters:
sumOpen (float): (float) Sum open price
sumHigh (float): (float) Sum high price
sumLow (float): (float) Sum low price
sumClose (float): (float) Sum close price
sumStartTime (int): (int) Sum start time
sumEndTime (int): (int) Sum end time
sumHighTime (int): (int) High point occurrence time
sumLowTime (int): (int) Low point occurrence time
state (string): (string) State
Returns: SumCandleData Created sum candle data
ValidateSumCandleData(sumData, confirmedState)
ValidateSumCandleData
@desc Validates sum candle data.
sumData (SumCandleData): Sum candle data to validate
confirmedState (string): CONFIRMED state string
Returns ( ):
Parameters:
sumData (SumCandleData): (SumCandleData) Sum candle data to validate
confirmedState (string): (string) CONFIRMED state string
Returns:
CanConfirmSumCandle(sumData, pendingState, confirmedState, completedState)
CanConfirmSumCandle
@desc Validates whether a sum candle can be confirmed.
sumData (SumCandleData): Sum candle data to confirm
pendingState (string): PENDING state string
confirmedState (string): CONFIRMED state string
completedState (string): COMPLETED state string
Returns ( ):
Parameters:
sumData (SumCandleData): (SumCandleData) Sum candle data to confirm
pendingState (string): (string) PENDING state string
confirmedState (string): (string) CONFIRMED state string
completedState (string): (string) COMPLETED state string
Returns:
UpdateSumCandleState(sumData, newState, pendingState, confirmedState, completedState)
UpdateSumCandleState
@desc Updates the state of a sum candle (includes validation).
sumData (SumCandleData): Sum candle data to update
newState (string): New state
pendingState (string): PENDING state string
confirmedState (string): CONFIRMED state string
completedState (string): COMPLETED state string
Returns ( ):
Parameters:
sumData (SumCandleData): (SumCandleData) Sum candle data to update
newState (string): (string) New state
pendingState (string): (string) PENDING state string
confirmedState (string): (string) CONFIRMED state string
completedState (string): (string) COMPLETED state string
Returns:
CreateSumCandleBox(sumOpen, sumClose, sumStartTime, endTime, bullColor, bearColor, borderColor, borderWidth)
CreateSumCandleBox
@desc Creates a sum candle body box.
sumOpen (float): Sum open price
sumClose (float): Sum close price
sumStartTime (int): Sum start time (milliseconds)
endTime (int): Sum end time (milliseconds)
bullColor (color): Bullish candle color
bearColor (color): Bearish candle color
borderColor (color): Border color
borderWidth (int): Border width (1-5)
Returns (box): Created body box
Parameters:
sumOpen (float): (float) Sum open price
sumClose (float): (float) Sum close price
sumStartTime (int): (int) Sum start time
endTime (int): (int) Sum end time
bullColor (color): (color) Bullish candle color
bearColor (color): (color) Bearish candle color
borderColor (color): (color) Border color
borderWidth (int): (int) Border width
Returns: box Body box
CreateSumCandleLine(x, y1, y2, lineColor, lineWidth)
CreateSumCandleLine
@desc Creates a sum candle wick line.
x (int): Line x coordinate (time, milliseconds)
y1 (float): Line start y coordinate (price)
y2 (float): Line end y coordinate (price)
lineColor (color): Line color
lineWidth (int): Line width (1-5)
Returns (line): Created wick line
Parameters:
x (int): (int) Line x coordinate (time)
y1 (float): (float) Line start y coordinate
y2 (float): (float) Line end y coordinate
lineColor (color): (color) Line color
lineWidth (int): (int) Line width
Returns: line Wick line
DeleteSumCandleVisuals(sumData)
DeleteSumCandleVisuals
@desc Deletes visualization objects (boxes, lines) of a sum candle.
sumData (SumCandleData): Sum candle data to delete
Returns (void): No return value
Parameters:
sumData (SumCandleData): (SumCandleData) Sum candle data to delete
Returns: void
GetBodyBounds(sumOpen, sumClose)
GetBodyBounds
@desc Calculates the top and bottom boundaries of the body.
sumOpen (float): Sum open price
sumClose (float): Sum close price
Returns ( ):
Parameters:
sumOpen (float): (float) Sum open price
sumClose (float): (float) Sum close price
Returns:
ManageMemory(sumArray, maxSize)
ManageMemory
@desc Manages array size and deletes old data.
sumArray (array): Sum candle array to manage
maxSize (int): Maximum size
Returns (void): No return value
Parameters:
sumArray (array): (array) Sum candle array to manage
maxSize (int): (int) Maximum size
Returns: void
IsSingleCandle(sumData)
IsSingleCandle
@desc Checks if it's a sum consisting of only one candle.
sumData (SumCandleData): Sum candle data to check
Returns (bool): true if single candle, false otherwise
Parameters:
sumData (SumCandleData): (SumCandleData) Sum candle data to check
Returns: bool Single candle status
CalculateWickCenterTime(startTime, endTime)
CalculateWickCenterTime
@desc Calculates the center time where the wick will be displayed.
startTime (int): Sum start time (milliseconds)
endTime (int): Sum end time (milliseconds)
Returns (int): Center time (milliseconds)
Parameters:
startTime (int): (int) Sum start time
endTime (int): (int) Sum end time
Returns: int Center time
SumCandleStates
Fields:
pending (series string)
confirmed (series string)
completed (series string)
SumCandleData
Fields:
_open (series float)
_high (series float)
_low (series float)
_close (series float)
_startTime (series int)
_endTime (series int)
_highTime (series int)
_lowTime (series int)
_isBull (series bool)
_state (series string)
_boxBody (series box)
_wickHigh (series line)
_wickLow (series line)
oct25libLibrary of technical indicators for use in scripts.
1) salmav3(src, length, smooth, mult, sd_len)
Parameters:
src (float)
length (int)
smooth (simple int)
mult(float)
sd_len(int)
2) boltzman(src, length, T, alpha, smoothLen)
Parameters:
src (float)
length (int)
T(float)
Alpha(float)
smoothLen (simple int)
3) shannon(src, vol, len, bc, vc, smooth)
Parameters:
src (float)
vol (float)
len (int)
bc (bool)
vc (bool)
smooth (simple int)
4) vama(src, baseLen, volLen, beta)
Parameters:
src (float)
baseLen (int)
volLen (int)
beta (float)
5) fwma(src, period, lambda, smooth)
Parameters:
src (float)
period (int)
lambda (float)
smooth (simple int)
6) savitzky(srcc, srch, srcl, length)
Parameters:
srcc (float)
srch (float)
srcl (float)
length (int)
7) butterworth(src, length, poles, smooth)
Parameters:
src (float)
length (int)
poles (int)
smooth (simple int)
8) rti(src, trend_data_count, trend_sensitivity_percentage, midline, smooth)
Parameters:
src (float)
trend_data_count (int)
trend_sensitivity_percentage (int)
midline (int)
smooth (simple int)
9) chandemo(src, length, smooth)
Parameters:
src (float)
length (int)
smooth (simple int)
10) hsma(assetClose, length, emalen, midline)
Parameters:
assetClose (float)
length (int)
emalen (simple int)
midline (float)
11) rsi(src, rsiLengthInput, rsiemalen, midline)
Parameters:
src (float)
rsiLengthInput (simple int)
rsiemalen (simple int)
midline (float)
12) lacoca(src, lookback, malen, matype)
Parameters:
src (float)
lookback (int)
malen (simple int)
matype (string)
AdjCloseLibLibrary "AdjCloseLib"
Library for producing gap-adjusted price series that removes intraday gaps at market open
get_adj_close(_gapThresholdPct)
Calculates gap-adjusted close price by detecting and removing gaps at market open (09:15)
Parameters:
_gapThresholdPct (float) : Minimum gap size (in percentage) required to trigger adjustment. Example: 0.5 for 0.5%
Returns: Adjusted close price for the current bar (always returns a numeric value, never na)
@details Detects gaps by comparing 09:15 open with previous day's close. If gap exceeds threshold,
subtracts the gap value from all bars between 09:15-15:29 inclusive. State resets after session close.
get_adj_ohlc(_gapThresholdPct)
Calculates gap-adjusted OHLC values by subtracting detected gap from all price components
Parameters:
_gapThresholdPct (float) : Minimum gap size (in percentage) required to trigger adjustment. Example: 0.5 for 0.5%
Returns: Tuple of
@details Useful for calculating indicators (ATR, Heikin-Ashi, etc.) on gap-adjusted data.
Applies the same gap adjustment logic to all OHLC components simultaneously.
NormalizedIndicatorsNormalizedIndicators Library - Comprehensive Trend Normalization & Pre-Calibrated Systems
Overview
The NormalizedIndicators Library is an advanced Pine Script™ collection that provides normalized trend-following indicators, calculation functions, and pre-calibrated consensus systems for technical analysis. This library extends beyond simple indicator normalization by offering battle-tested, optimized parameter sets for specific assets and timeframes.
The main advantage lies in its dual functionality:
Individual normalized indicators with standardized outputs (1 = bullish, -1 = bearish, 0 = neutral)
Pre-calibrated consensus functions that combine multiple indicators with asset-specific optimizations
This enables traders to either build custom strategies using individual indicators or leverage pre-optimized systems designed for specific markets.
📊 Library Structure
The library is organized into three main sections:
1. Trend-Following Indicators
Individual indicators normalized to standard output format
2. Calculation Indicators
Statistical and mathematical analysis functions
3. Pre-Calibrated Systems ⭐ NEW
Asset-specific consensus configurations with optimized parameters
🔄 Trend-Following Indicators
Stationary Indicators
These oscillate around a fixed value and are not bound to price.
TSI() - True Strength Index ⭐ NEW
Source: TradingView
Parameters:
price: Price source
long: Long smoothing period
short: Short smoothing period
signal: Signal line period
Logic: Double-smoothed momentum oscillator comparing TSI to its signal line
Signal:
1 (bullish): TSI ≥ TSI EMA
0 (bearish): TSI < TSI EMA
Use Case: Momentum confirmation with trend direction
SMI() - Stochastic Momentum Index ⭐ NEW
Source: TradingView
Parameters:
src: Price source
lengthK: Stochastic period
lengthD: Smoothing period
lengthEMA: Signal line period
Logic: Enhanced stochastic that measures price position relative to midpoint of high/low range
Signal:
1 (bullish): SMI ≥ SMI EMA
0 (bearish): SMI < SMI EMA
Use Case: Overbought/oversold with momentum direction
BBPct() - Bollinger Bands Percent
Source: Algoalpha X Sushiboi77
Parameters:
Length: Period for Bollinger Bands
Factor: Standard deviation multiplier
Source: Price source (typical: close)
Logic: Calculates the position of price within the Bollinger Bands as a percentage
Signal:
1 (bullish): when positionBetweenBands > 50
-1 (bearish): when positionBetweenBands ≤ 50
Special Feature: Uses an array to store historical standard deviations for additional analysis
RSI() - Relative Strength Index
Source: TradingView
Parameters:
len: RSI period
src: Price source
smaLen: Smoothing period for RSI
Logic: Classic RSI with additional SMA smoothing
Signal:
1 (bullish): RSI-SMA > 50
-1 (bearish): RSI-SMA < 50
0 (neutral): RSI-SMA = 50
Non-Stationary Indicators
These follow price movement and have no fixed boundaries.
NorosTrendRibbonSMA() & NorosTrendRibbonEMA()
Source: ROBO_Trading
Parameters:
Length: Moving average and channel period
Source: Price source
Logic: Creates a price channel based on the highest/lowest MA value over a specified period
Signal:
1 (bullish): Price breaks above upper band
-1 (bearish): Price breaks below lower band
0 (neutral): Price within channel (maintains last state)
Difference: SMA version uses simple moving averages, EMA version uses exponential
TrendBands()
Source: starlord_xrp
Parameters: src (price source)
Logic: Uses 12 EMAs (9-30 period) and checks if all are rising or falling simultaneously
Signal:
1 (bullish): All 12 EMAs are rising
-1 (bearish): All 12 EMAs are falling
0 (neutral): Mixed signals
Special Feature: Very strict conditions - extremely strong trend filter
Vidya() - Variable Index Dynamic Average
Source: loxx
Parameters:
source: Price source
length: Main period
histLength: Historical period for volatility calculation
Logic: Adaptive moving average that adjusts to volatility
Signal:
1 (bullish): VIDYA is rising
-1 (bearish): VIDYA is falling
VZO() - Volume Zone Oscillator
Parameters:
source: Price source
length: Smoothing period
volumesource: Volume data source
Logic: Combines price and volume direction, calculates the ratio of directional volume to total volume
Signal:
1 (bullish): VZO > 14.9
-1 (bearish): VZO < -14.9
0 (neutral): VZO between -14.9 and 14.9
TrendContinuation()
Source: AlgoAlpha
Parameters:
malen: First HMA period
malen1: Second HMA period
theclose: Price source
Logic: Uses two Hull Moving Averages for trend assessment with neutrality detection
Signal:
1 (bullish): Uptrend without divergence
-1 (bearish): Downtrend without divergence
0 (neutral): Trend and longer MA diverge
LeonidasTrendFollowingSystem()
Source: LeonidasCrypto
Parameters:
src: Price source
shortlen: Short EMA period
keylen: Long EMA period
Logic: Simple dual EMA crossover system
Signal:
1 (bullish): Short EMA < Key EMA
-1 (bearish): Short EMA ≥ Key EMA
ysanturtrendfollower()
Source: ysantur
Parameters:
src: Price source
depth: Depth of Fibonacci weighting
smooth: Smoothing period
bias: Percentage bias adjustment
Logic: Complex system with Fibonacci-weighted moving averages and bias bands
Signal:
1 (bullish): Weighted MA > smoothed MA (with upward bias)
-1 (bearish): Weighted MA < smoothed MA (with downward bias)
0 (neutral): Within bias zone
TRAMA() - Trend Regularity Adaptive Moving Average
Source: LuxAlgo
Parameters:
src: Price source
length: Adaptation period
Logic: Adapts to trend regularity - accelerates in stable trends, slows in consolidations
Signal:
1 (bullish): Price > TRAMA
-1 (bearish): Price < TRAMA
0 (neutral): Price = TRAMA
HullSuite()
Source: InSilico
Parameters:
_length: Base period
src: Price source
_lengthMult: Length multiplier
Logic: Uses Hull Moving Average with lagged comparisons for trend determination
Signal:
1 (bullish): Current Hull > Hull 2 bars ago
-1 (bearish): Current Hull < Hull 2 bars ago
0 (neutral): No change
STC() - Schaff Trend Cycle
Source: shayankm (described as "Better MACD")
Parameters:
length: Cycle period
fastLength: Fast MACD period
slowLength: Slow MACD period
src: Price source
Logic: Combines MACD concepts with stochastic normalization for early trend signals
Signal:
1 (bullish): STC is rising
-1 (bearish): STC is falling
🧮 Calculation Indicators
These functions provide specialized mathematical calculations for advanced analysis.
LCorrelation() - Long-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (30, 60, 90, 120, 150, 180)
Returns: Correlation value between -1 and 1
Application: Long-term relationship analysis between assets, markets, or indicators
MCorrelation() - Medium-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (15, 30, 45, 60, 75, 90)
Returns: Correlation value between -1 and 1
Application: Medium-term relationship analysis with higher sensitivity
assetBeta() - Beta Coefficient
Creator: unicorpusstocks
Parameters:
measuredSymbol: The asset to be measured
baseSymbol: The reference asset (e.g., market index)
Logic:
Calculates Beta across 4 different time horizons (50, 100, 150, 200 periods)
Beta = Correlation × (Asset Standard Deviation / Market Standard Deviation)
Returns the average of all 4 Beta values
Returns: Beta value (typically 0-2, can be higher/lower)
Interpretation:
Beta = 1: Asset moves in sync with the market
Beta > 1: Asset more volatile than market
Beta < 1: Asset less volatile than market
Beta < 0: Asset moves inversely to the market
🎯 Pre-Calibrated Systems ⭐ NEW FEATURE
These are ready-to-use consensus functions with optimized parameters for specific assets and timeframes. Each calibration has been fine-tuned through extensive backtesting to provide optimal performance for its target market.
Universal Calibrations
virtual_4d_cal(src) - Virtual/General 4-Day Timeframe
Use Case: General purpose 4-day chart analysis
Optimized For: Broad crypto market on 4D timeframe
Indicators Used: BBPct, Noro's, RSI, VIDYA, HullSuite, TrendContinuation, Leonidas, TRAMA
Characteristics: Balanced sensitivity for swing trading
virtual_1d_cal(src) - Virtual/General 1-Day Timeframe
Use Case: General purpose daily chart analysis
Optimized For: Broad crypto market on 1D timeframe
Indicators Used: BBPct, Noro's, RSI, VIDYA, HullSuite, TrendContinuation, Leonidas, TRAMA
Characteristics: Standard daily trading parameters
Cryptocurrency Specific
sui_cal(src) - SUI Ecosystem Tokens
Use Case: Tokens in the SUI blockchain ecosystem
Timeframe: 1D
Characteristics: Fast-response parameters for high volatility projects
deep_1d_cal(src) - DEEP Token Daily
Use Case: Deepbook (DEEP) token analysis
Timeframe: 1D
Characteristics: Tuned for liquidity protocol token behavior
wal_1d_cal(src) - WAL Token Daily
Use Case: Specific for WAL token
Timeframe: 1D
Characteristics: Mid-range sensitivity parameters
sns_1d_cal(src) - SNS Token Daily
Use Case: Specific for SNS token
Timeframe: 1D
Characteristics: Balanced parameters for DeFi tokens
meme_cal(src) - Meme Coin Calibration
Use Case: Highly volatile meme coins
Timeframe: Various
Characteristics: Wider parameters to handle extreme volatility
Warning: Meme coins carry extreme risk
base_cal(src) - BASE Ecosystem Tokens
Use Case: Tokens on the BASE blockchain
Timeframe: Various
Characteristics: Optimized for L2 ecosystem tokens
Solana Ecosystem
sol_4d_cal(src) - Solana 4-Day
Use Case: SOL token on 4-day charts
Characteristics: Responsive parameters for major L1 blockchain
sol_meme_4d_cal(src) - Solana Meme Coins 4-Day
Use Case: Meme coins on Solana blockchain
Timeframe: 4D
Characteristics: Handles high volatility of Solana meme sector
Ethereum Ecosystem
eth_4d_cal(src) - Ethereum 4-Day
Use Case: ETH and major ERC-20 tokens
Timeframe: 4D
Indicators Used: BBPct, Noro's, RSI, TSI, HullSuite, TrendContinuation, Leonidas, SMI
Special: Uses TSI and SMI instead of VIDYA and TRAMA
Characteristics: Tuned for Ethereum's market cycles
Bitcoin
btc_4d_cal(src) - Bitcoin 4-Day
Use Case: Bitcoin on 4-day charts
Timeframe: 4D
Characteristics: Slower, smoother parameters for the most established crypto asset
Notes: Conservative parameters suitable for position trading
Traditional Markets
qqq_4d_cal(src) - QQQ (Nasdaq-100 ETF) 4-Day
Use Case: QQQ ETF and tech-heavy indices
Timeframe: 4D
Characteristics: Largest parameter sets reflecting lower volatility of traditional markets
Notes: Can be adapted for similar large-cap tech indices
💡 Usage Examples
Example 1: Using Pre-Calibrated System
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Simple one-line implementation for Bitcoin
btcSignal = lib.btc_4d_cal(close)
// Trading logic
longCondition = btcSignal > 0.5
shortCondition = btcSignal < -0.5
// Plot
plot(btcSignal, "BTC 4D Consensus", color.orange)
Example 2: Custom Multi-Indicator Consensus
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Build your own combination
signal1 = lib.BBPct(20, 2.0, close)
signal2 = lib.RSI(14, close, 5)
signal3 = lib.TRAMA(close, 50)
signal4 = lib.TSI(close, 25, 13, 13)
// Custom consensus
customConsensus = math.avg(signal1, signal2, signal3, signal4)
plot(customConsensus, "Custom Consensus", color.blue)
Example 3: Asset-Specific Strategy Switching
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Automatically use the right calibration
signal = switch syminfo.ticker
"BTCUSD" => lib.btc_4d_cal(close)
"ETHUSD" => lib.eth_4d_cal(close)
"SOLUSD" => lib.sol_4d_cal(close)
"QQQ" => lib.qqq_4d_cal(close)
=> lib.virtual_4d_cal(close) // Default
plot(signal, "Auto-Calibrated Signal", color.orange)
Example 4: Correlation-Filtered Trading
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Only trade when strong correlation with market exists
spy = request.security("SPY", timeframe.period, close)
correlation = lib.MCorrelation(close, spy)
trendSignal = lib.virtual_1d_cal(close)
// Only signals with positive market correlation
tradeBuy = trendSignal > 0.5 and correlation > 0.5
tradeSell = trendSignal < -0.5 and correlation > 0.5
Example 5: Beta-Adjusted Position Sizing
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
spy = request.security("SPY", timeframe.period, close)
beta = lib.assetBeta(close, spy)
// Adjust position size based on Beta
basePositionSize = 100
adjustedSize = basePositionSize / beta // Less size with high Beta
// Use with calibrated signal
signal = lib.qqq_4d_cal(close)
🎯 Choosing the Right Calibration
Decision Tree
1. What asset are you trading?
Bitcoin → btc_4d_cal()
Ethereum/ERC-20 → eth_4d_cal()
Solana → sol_4d_cal()
Solana memes → sol_meme_4d_cal()
SUI ecosystem → sui_cal()
BASE ecosystem → base_cal()
Meme coins (any chain) → meme_cal()
QQQ/Tech indices → qqq_4d_cal()
Other/General → virtual_4d_cal() or virtual_1d_cal()
2. What timeframe?
Most calibrations are optimized for 4D (4-day) or 1D (daily)
For other timeframes, start with virtual calibrations and adjust
3. What's the asset's volatility?
High volatility (memes, new tokens) → Use meme_cal() or similar
Medium volatility (established alts) → Use specific calibrations
Low volatility (BTC, major indices) → Use btc_4d_cal() or qqq_4d_cal()
⚙️ Technical Details
Normalization Standard
Bullish: 1
Bearish: -1
Neutral: 0 (only for selected indicators)
Calibration Methodology
Pre-calibrated functions were optimized using:
Historical backtesting on target assets
Parameter optimization for maximum Sharpe ratio
Validation on out-of-sample data
Real-time forward testing
Iterative refinement based on market conditions
Advantages of Pre-Calibrations
Instant Deployment: No parameter tuning needed
Asset-Optimized: Tailored to specific market characteristics
Tested Performance: Validated through extensive backtesting
Consistent Framework: All use the same 8-indicator structure
Easy Comparison: Compare different assets using same methodology
Performance Considerations
All functions are optimized for Pine Script v5
Proper use of var for state management
Efficient array operations where needed
Minimal recursive calls
Pre-calibrations add negligible computational overhead
📋 License
This code is subject to the Mozilla Public License 2.0 at mozilla.org
🔧 Installation
pinescriptimport unicorpusstocks/NormalizedIndicators/1
Then use functions with your chosen alias:
pinescript// Individual indicators
lib.BBPct(20, 2.0, close)
lib.RSI(14, close, 5)
lib.TSI(close, 25, 13, 13)
// Pre-calibrated systems
lib.btc_4d_cal(close)
lib.eth_4d_cal(close)
lib.meme_cal(close)
⚠️ Important Notes
General Usage
All indicators are lagging, as is typical for trend-following indicators
Signals should be combined with additional analysis (volume, support/resistance, etc.)
Backtesting is recommended before starting live trading with these signals
Different assets and timeframes may require different parameter optimizations
Pre-Calibrated Systems
Calibrations are optimized for specific timeframes - using them on different timeframes may reduce effectiveness
Market conditions change - what worked historically may need adjustment
Pre-calibrations are starting points, not guaranteed solutions
Always validate performance on your specific use case
Consider current market regime (trending vs. ranging)
Risk Management
Meme coin calibrations are designed for extremely volatile assets - use appropriate position sizing
Pre-calibrated systems do not eliminate risk
Always use stop losses and proper risk management
Past performance does not guarantee future results
Customization
Pre-calibrations can serve as templates for your own optimizations
Feel free to adjust individual parameters within calibration functions
Test modifications thoroughly before live deployment
🎓 Advanced Use Cases
Multi-Asset Portfolio Dashboard
Create a dashboard showing consensus across different assets:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
btc = request.security("BTCUSD", "4D", close)
eth = request.security("ETHUSD", "4D", close)
sol = request.security("SOLUSD", "4D", close)
btcSignal = lib.btc_4d_cal(btc)
ethSignal = lib.eth_4d_cal(eth)
solSignal = lib.sol_4d_cal(sol)
// Plot all three for comparison
plot(btcSignal, "BTC", color.orange)
plot(ethSignal, "ETH", color.blue)
plot(solSignal, "SOL", color.purple)
Regime Detection
Use correlation and calibrations together:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
// Detect market regime
btc = request.security("BTCUSD", timeframe.period, close)
correlation = lib.MCorrelation(close, btc)
// Choose strategy based on correlation
signal = correlation > 0.7 ? lib.btc_4d_cal(close) : lib.virtual_4d_cal(close)
Comparative Analysis
Compare asset-specific vs. general calibrations:
pinescriptimport unicorpusstocks/NormalizedIndicators/1 as lib
specificSignal = lib.btc_4d_cal(close) // BTC-specific
generalSignal = lib.virtual_4d_cal(close) // General
divergence = specificSignal - generalSignal
plot(divergence, "Calibration Divergence", color.yellow)
🚀 Quick Start Guide
For Beginners
Identify Your Asset: What are you trading?
Find the Calibration: Use the decision tree above
One-Line Implementation: signal = lib.btc_4d_cal(close)
Set Thresholds: Buy when > 0.5, sell when < -0.5
Add Risk Management: Always use stops
For Advanced Users
Start with Pre-Calibration: Use as baseline
Analyze Performance: Backtest on your specific market
Fine-Tune Parameters: Adjust individual indicators if needed
Combine with Other Signals: Volume, market structure, etc.
Create Custom Calibrations: Build your own based on library structure
For Developers
Import Library: Access all functions
Mix and Match: Combine indicators creatively
Build Custom Logic: Use indicators as building blocks
Create New Calibrations: Follow the established pattern
Share and Iterate: Contribute to the trading community
🎯 Key Takeaways
✅ 10 normalized indicators - Consistent interpretation across all
✅ 16+ pre-calibrated systems - Ready-to-use for specific assets
✅ Asset-optimized parameters - No guesswork required
✅ Calculation functions - Advanced correlation and beta analysis
✅ Universal framework - Works across crypto, stocks, forex
✅ Professional-grade - Built on proven technical analysis principles
✅ Flexible architecture - Use pre-calibrations or build your own
✅ Battle-tested - Validated through extensive backtesting
NormalizedIndicators Library transforms complex multi-indicator analysis into actionable signals through both customizable individual indicators and pre-optimized consensus systems. Whether you're a beginner looking for plug-and-play solutions or an advanced trader building sophisticated strategies, this library provides the foundation for data-driven trading decisions.WiederholenClaude kann Fehler machen. Bitte überprüfen Sie die Antworten. Sonnet 4.5
NormalizedIndicatorsNormalizedIndicators - Comprehensive Trend Normalization Library
Overview
This Pine Script™ library provides an extensive collection of normalized trend-following indicators and calculation functions for technical analysis. The main advantage of this library lies in its unified signal output: All trend indicators are normalized to a standardized format where 1 represents a bullish signal, -1 represents a bearish signal, and 0 (where applicable) represents a neutral signal.
This normalization enables traders to seamlessly combine different indicators, create consensus signals, and develop complex multi-indicator strategies without worrying about different scales and interpretations.
📊 Categories
The library is divided into two main categories:
1. Trend-Following Indicators
2. Calculation Indicators
🔄 Trend-Following Indicators
Stationary Indicators
These oscillate around a fixed value and are not bound to price.
BBPct() - Bollinger Bands Percent
Source: Algoalpha X Sushiboi77
Parameters:
Length: Period for Bollinger Bands
Factor: Standard deviation multiplier
Source: Price source (typical: close)
Logic: Calculates the position of price within the Bollinger Bands as a percentage
Signal:
1 (bullish): when positionBetweenBands > 50
-1 (bearish): when positionBetweenBands ≤ 50
Special Feature: Uses an array to store historical standard deviations for additional analysis
RSI() - Relative Strength Index
Source: TradingView
Parameters:
len: RSI period
src: Price source
smaLen: Smoothing period for RSI
Logic: Classic RSI with additional SMA smoothing
Signal:
1 (bullish): RSI-SMA > 50
-1 (bearish): RSI-SMA < 50
0 (neutral): RSI-SMA = 50
Non-Stationary Indicators
These follow price movement and have no fixed boundaries.
NorosTrendRibbonSMA() & NorosTrendRibbonEMA()
Source: ROBO_Trading
Parameters:
Length: Moving average and channel period
Source: Price source
Logic: Creates a price channel based on the highest/lowest MA value over a specified period
Signal:
1 (bullish): Price breaks above upper band
-1 (bearish): Price breaks below lower band
0 (neutral): Price within channel (maintains last state)
Difference: SMA version uses simple moving averages, EMA version uses exponential
TrendBands()
Source: starlord_xrp
Parameters: src (price source)
Logic: Uses 12 EMAs (9-30 period) and checks if all are rising or falling simultaneously
Signal:
1 (bullish): All 12 EMAs are rising
-1 (bearish): All 12 EMAs are falling
0 (neutral): Mixed signals
Special Feature: Very strict conditions - extremely strong trend filter
Vidya() - Variable Index Dynamic Average
Source: loxx
Parameters:
source: Price source
length: Main period
histLength: Historical period for volatility calculation
Logic: Adaptive moving average that adjusts to volatility
Signal:
1 (bullish): VIDYA is rising
-1 (bearish): VIDYA is falling
VZO() - Volume Zone Oscillator
Parameters:
source: Price source
length: Smoothing period
volumesource: Volume data source
Logic: Combines price and volume direction, calculates the ratio of directional volume to total volume
Signal:
1 (bullish): VZO > 14.9
-1 (bearish): VZO < -14.9
0 (neutral): VZO between -14.9 and 14.9
TrendContinuation()
Source: AlgoAlpha
Parameters:
malen: First HMA period
malen1: Second HMA period
theclose: Price source
Logic: Uses two Hull Moving Averages for trend assessment with neutrality detection
Signal:
1 (bullish): Uptrend without divergence
-1 (bearish): Downtrend without divergence
0 (neutral): Trend and longer MA diverge
LeonidasTrendFollowingSystem()
Source: LeonidasCrypto
Parameters:
src: Price source
shortlen: Short EMA period
keylen: Long EMA period
Logic: Simple dual EMA crossover system
Signal:
1 (bullish): Short EMA < Key EMA
-1 (bearish): Short EMA ≥ Key EMA
ysanturtrendfollower()
Source: ysantur
Parameters:
src: Price source
depth: Depth of Fibonacci weighting
smooth: Smoothing period
bias: Percentage bias adjustment
Logic: Complex system with Fibonacci-weighted moving averages and bias bands
Signal:
1 (bullish): Weighted MA > smoothed MA (with upward bias)
-1 (bearish): Weighted MA < smoothed MA (with downward bias)
0 (neutral): Within bias zone
TRAMA() - Trend Regularity Adaptive Moving Average
Source: LuxAlgo
Parameters:
src: Price source
length: Adaptation period
Logic: Adapts to trend regularity - accelerates in stable trends, slows in consolidations
Signal:
1 (bullish): Price > TRAMA
-1 (bearish): Price < TRAMA
0 (neutral): Price = TRAMA
HullSuite()
Source: InSilico
Parameters:
_length: Base period
src: Price source
_lengthMult: Length multiplier
Logic: Uses Hull Moving Average with lagged comparisons for trend determination
Signal:
1 (bullish): Current Hull > Hull 2 bars ago
-1 (bearish): Current Hull < Hull 2 bars ago
0 (neutral): No change
STC() - Schaff Trend Cycle
Source: shayankm (described as "Better MACD")
Parameters:
length: Cycle period
fastLength: Fast MACD period
slowLength: Slow MACD period
src: Price source
Logic: Combines MACD concepts with stochastic normalization for early trend signals
Signal:
1 (bullish): STC is rising
-1 (bearish): STC is falling
🧮 Calculation Indicators
These functions provide specialized mathematical calculations for advanced analysis.
LCorrelation() - Long-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (30, 60, 90, 120, 150, 180)
Returns: Correlation value between -1 and 1
Application: Long-term relationship analysis between assets, markets, or indicators
MCorrelation() - Medium-term Correlation
Creator: unicorpusstocks
Parameters:
Input: First time series
Compare: Second time series
Logic: Calculates the average of correlations across 6 different periods (15, 30, 45, 60, 75, 90)
Returns: Correlation value between -1 and 1
Application: Medium-term relationship analysis with higher sensitivity
assetBeta() - Beta Coefficient
Creator: unicorpusstocks
Parameters:
measuredSymbol: The asset to be measured
baseSymbol: The reference asset (e.g., market index)
Logic:
Calculates Beta across 4 different time horizons (50, 100, 150, 200 periods)
Beta = Correlation × (Asset Standard Deviation / Market Standard Deviation)
Returns the average of all 4 Beta values
Returns: Beta value (typically 0-2, can be higher/lower)
Interpretation:
Beta = 1: Asset moves in sync with the market
Beta > 1: Asset more volatile than market
Beta < 1: Asset less volatile than market
Beta < 0: Asset moves inversely to the market
💡 Usage Examples
Example 1: Multi-Indicator Consensus
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
// Combine multiple indicators
signal1 = lib.BBPct(20, 2.0, close)
signal2 = lib.RSI(14, close, 5)
signal3 = lib.TRAMA(close, 50)
// Consensus signal: At least 2 of 3 must agree
consensus = (signal1 + signal2 + signal3)
strongBuy = consensus >= 2
strongSell = consensus <= -2
Example 2: Correlation-Filtered Trading
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
// Only trade when strong correlation with market exists
spy = request.security("SPY", timeframe.period, close)
correlation = lib.MCorrelation(close, spy)
trendSignal = lib.NorosTrendRibbonEMA(50, close)
// Only bullish signals with positive correlation
tradeBuy = trendSignal == 1 and correlation > 0.5
tradeSell = trendSignal == -1 and correlation > 0.5
Example 3: Beta-Adjusted Position Sizing
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1 as lib
spy = request.security("SPY", timeframe.period, close)
beta = lib.assetBeta(close, spy)
// Adjust position size based on Beta
basePositionSize = 100
adjustedSize = basePositionSize / beta // Less size with high Beta
⚙️ Technical Details
Normalization Standard
Bullish: 1
Bearish: -1
Neutral: 0 (only for selected indicators)
Advantages of Normalization
Simple Aggregation: Signals can be added/averaged
Consistent Interpretation: No confusion about different scales
Strategy Development: Simplified logic for backtesting
Combinability: Seamlessly mix different indicator types
Performance Considerations
All functions are optimized for Pine Script v5
Proper use of var for state management
Efficient array operations where needed
Minimal recursive calls
📋 License
This code is subject to the Mozilla Public License 2.0. More details at: mozilla.org
🎯 Use Cases
This library is ideal for:
Quantitative Traders: Systematic strategy development with unified signals
Multi-Timeframe Analysis: Consensus across different timeframes
Portfolio Managers: Beta and correlation analysis for diversification
Algo Traders: Machine learning with standardized features
Retail Traders: Simplified signal interpretation without deep technical knowledge
🔧 Installation
pinescriptimport unicorpusstocks/MyIndicatorLibrary/1
Then use the functions with your chosen alias:
pinescriptlib.BBPct(20, 2.0, close)
lib.RSI(14, close, 5)
// etc.
⚠️ Important Notes
All indicators are lagging, as is typical for trend-following indicators
Signals should be combined with additional analysis (volume, support/resistance, etc.)
Backtesting is recommended before starting live trading with these signals
Different assets and timeframes may require different parameter optimizations
This library provides a solid foundation for professional trading system design with the flexibility to develop your own complex strategies while abstracting away technical complexity.
smaemarvwapClaireLibrary "smaemarvwapClaire"
repeat_character(count)
Parameters:
count (int)
f_1_k_line_width()
is_price_in_merge_range(p1, p2, label_merge_range)
Parameters:
p1 (float)
p2 (float)
label_merge_range (float)
get_pre_label_string(kc, t, is_every)
Parameters:
kc (VWAP_key_levels_draw_settings)
t (int)
is_every (bool)
f_is_new_period_from_str(str)
Parameters:
str (string)
total_for_time_when(source, days, ma_set)
Parameters:
source (float)
days (int)
ma_set (ma_setting)
f_calculate_sma_ema_rolling_vwap(src, length, ma_settings)
Parameters:
src (float)
length (simple int)
ma_settings (ma_setting)
f_calculate_sma_ema_rvwap(ma_settings)
Parameters:
ma_settings (ma_setting)
f_get_ma_pre_label(ma_settings, sma, ema, rolling_vwap)
Parameters:
ma_settings (ma_setting)
sma (float)
ema (float)
rolling_vwap (float)
f_smart_ma_calculation(ma_settings2)
Parameters:
ma_settings2 (ma_setting)
f_calculate_endpoint(start_time, kc, is_every, endp, extend1, extend2, line_label_extend_length)
Parameters:
start_time (int)
kc (VWAP_key_levels_draw_settings)
is_every (bool)
endp (int)
extend1 (bool)
extend2 (bool)
line_label_extend_length (int)
f_single_line_label_fatory(left_point, right_point, line_col, line_width, lines_style_select, labeltext_col, label_text_size, label_array, line_array, label_col, label_text, l1, label1)
根据两个点创建线段和/或标签，并将其添加到对应的数组中
Parameters:
left_point (chart.point) : 左侧起点坐标
right_point (chart.point) : 右侧终点坐标
line_col (color) : 线段颜色
line_width (int) : 线段宽度
lines_style_select (string) : 线段样式（实线、虚线等）
labeltext_col (color) : 标签文字颜色
label_text_size (string) : 标签文字大小
label_array (array) : 存储标签对象的数组
line_array (array) : 存储线段对象的数组
label_col (color) : 标签背景颜色（默认：半透明色）
label_text (string) : 标签文字内容（默认：空字符串）
l1 (bool) : 是否创建线段（默认：false）
label1 (bool) : 是否创建标签（默认：false）
Returns: void
f_line_and_label_merge_func(t, data, l_text, kc, is_every, endp, merge_str_map, label_array, line_array, extend1, extend2, line_label_extend_length, label_merge_control, line_width, lines_style_select, label_text_size)
Parameters:
t (int)
data (float)
l_text (string)
kc (VWAP_key_levels_draw_settings)
is_every (bool)
endp (int)
merge_str_map (map)
label_array (array)
line_array (array)
extend1 (bool)
extend2 (bool)
line_label_extend_length (int)
label_merge_control (bool)
line_width (int)
lines_style_select (string)
label_text_size (string)
plot_ohlc(kc, ohlc_data, extend1, extend2, merge_str_map, label_array, line_array, is_every, line_label_extend_length, label_merge_control, line_width, lines_style_select, label_text_size)
Parameters:
kc (VWAP_key_levels_draw_settings)
ohlc_data (bardata)
extend1 (bool)
extend2 (bool)
merge_str_map (map)
label_array (array)
line_array (array)
is_every (bool)
line_label_extend_length (int)
label_merge_control (bool)
line_width (int)
lines_style_select (string)
label_text_size (string)
plot_vwap_keylevels(kc, vwap_data, extend1, extend2, merge_str_map, label_array, line_array, is_every, line_label_extend_length, label_merge_control, line_width, lines_style_select, label_text_size)
Parameters:
kc (VWAP_key_levels_draw_settings)
vwap_data (vwap_snapshot)
extend1 (bool)
extend2 (bool)
merge_str_map (map)
label_array (array)
line_array (array)
is_every (bool)
line_label_extend_length (int)
label_merge_control (bool)
line_width (int)
lines_style_select (string)
label_text_size (string)
plot_vwap_bardata(kc, ohlc_data, vwap_data, extend1, extend2, merge_str_map, label_array, line_array, is_every, line_label_extend_length, label_merge_control, line_width, lines_style_select, label_text_size)
Parameters:
kc (VWAP_key_levels_draw_settings)
ohlc_data (bardata)
vwap_data (vwap_snapshot)
extend1 (bool)
extend2 (bool)
merge_str_map (map)
label_array (array)
line_array (array)
is_every (bool)
line_label_extend_length (int)
label_merge_control (bool)
line_width (int)
lines_style_select (string)
label_text_size (string)
f_start_end_total_min(session)
Parameters:
session (string)
f_get_vwap_array(anchor1, data_manager, is_historical)
Parameters:
anchor1 (string)
data_manager (data_manager)
is_historical (bool)
f_get_bardata_array(anchorh, data_manager, is_historical)
Parameters:
anchorh (string)
data_manager (data_manager)
is_historical (bool)
vwap_snapshot
Fields:
t (series int)
vwap (series float)
upper1 (series float)
lower1 (series float)
upper2 (series float)
lower2 (series float)
upper3 (series float)
lower3 (series float)
VWAP_key_levels_draw_settings
Fields:
enable (series bool)
index (series int)
anchor (series string)
session (series string)
vwap_col (series color)
bands_col (series color)
bg_color (series color)
text_color (series color)
val (series bool)
poc (series bool)
vah (series bool)
enable2x (series bool)
enable3x (series bool)
o_control (series bool)
h_control (series bool)
l_control (series bool)
c_control (series bool)
extend_control (series bool)
only_show_the_lastone_control (series bool)
bg_control (series bool)
line_col_labeltext_col (series color)
bardata
Fields:
o (series float)
h (series float)
l (series float)
c (series float)
v (series float)
start_time (series int)
end_time (series int)
ma_setting
Fields:
day_control (series bool)
kline_numbers (series int)
ma_color (series color)
ema_color (series color)
rvwap_color (series color)
ma_control (series bool)
ema_control (series bool)
rvwap_control (series bool)
session (series string)
merge_label_template
Fields:
left_point (chart.point)
right_point (chart.point)
label_text (series string)
p (series float)
label_color (series color)
merge_init_false (series bool)
anchor_snapshots
Fields:
vwap_current (array)
vwap_historical (array)
bardata_current (array)
bardata_historical (array)
data_manager
Fields:
snapshots_map (map)
draw_settings_map (map)
Count█ OVERVIEW
A library of functions for counting the number of times (frequency) that elements occur in an array or matrix.
█ USAGE
Import the Count library.
import joebaus/count/1 as c
Create an array or matrix that is a `float`, `int`, `string`, or `bool` type to count elements from, then call the count function on the array or matrix.
id = array.from(1.00, 1.50, 1.25, 1.00, 0.75, 1.25, 1.75, 1.25)
countMap = id.count() // Alternatively: countMap = c.count(id)
The "count map" will return a map with keys for each unique element in the array or matrix, and with respective values representing the number of times the unique element was counted. The keys will be the same type as the array or matrix counted. The values will always be an `int` type.
array mapKeys = countMap.keys() // Returns unique keys
array mapValues = countMap.values() // Returns counts
If an array is in ascending or descending order, then the keys of the map will also generate in the same order.
intArray = array.from(2, 2, 2, 3, 4, 4, 4, 4, 4, 6, 6) // Ascending order
map countMap = intArray.count() // Creates a "count map" of all unique elements
array mapKeys = countMap.keys() // Returns // Ascending order
array mapValues = countMap.values() // Returns count
Include a value to get the count of only that value in an array or matrix.
floatMatrix = matrix.new(3, 3, 0.0)
floatMatrix.set(0, 0, 1.0), floatMatrix.set(1, 0, 1.0), floatMatrix.set(2, 0, 1.0)
floatMatrix.set(0, 1, 1.5), floatMatrix.set(1, 1, 2.0), floatMatrix.set(2, 1, 2.5)
floatMatrix.set(0, 2, 1.0), floatMatrix.set(1, 2, 2.5), floatMatrix.set(2, 2, 1.5)
int countFloatMatrix = floatMatrix.count(1.0) // Counts all 1.0 elements, returns 5
// Alternatively: int countFloatMatrix = c.count(floatMatrix, 1.0)
The string method of count() can use strings or regular expressions like "bull*" to count all matching occurrences in a string array.
stringArray = array.from('bullish', 'bull', 'bullish', 'bear', 'bull', 'bearish', 'bearish')
int countString = stringArray.count('bullish') // Returns 2
int countStringRegex = stringArray.count('bull*') // Returns 4
To count multiple values, use an array of values instead of a single value. Returning a count map only of elements in the array.
countArray = array.from(1.0, 2.5)
map countMap = floatMatrix.count(countArray)
array mapKeys = countMap.keys() // Returns keys
array mapValues = countMap.values() // Returns counts
Multiple regex patterns or strings can be counted as well.
stringMatrix = matrix.new(3, 3, '')
stringMatrix.set(0, 0, 'a'), stringMatrix.set(1, 0, 'a'), stringMatrix.set(2, 0, 'a')
stringMatrix.set(0, 1, 'b'), stringMatrix.set(1, 1, 'c'), stringMatrix.set(2, 1, 'd')
stringMatrix.set(0, 2, 'a'), stringMatrix.set(1, 2, 'd'), stringMatrix.set(2, 2, 'b')
// Count the number of times the regex patterns `'^(a|c)$'` and `'^(b|d)$'` occur
array regexes = array.from('^(a|c)$', '^(b|d)$')
map countMap = stringMatrix.count(regexes)
array mapKeys = countMap.keys() // Returns
array mapValues = countMap.values() // Returns
An optional comparison operator can be specified to count the number of times an equality was satisfied for `float`, `int`, and `bool` methods of `count()`.
intArray = array.from(2, 2, 2, 3, 4, 4, 4, 4, 4, 6, 6)
// Count the number of times an element is greater than 4
countInt = intArray.count(4, '>') // Returns 2
When passing an array of values to count and a comparison operator, the operator will apply to each value.
intArray = array.from(2, 2, 2, 3, 4, 4, 4, 4, 4, 6, 6)
values = array.from(3, 4)
// Count the number of times and element is greater than 3 and 4
map countMap = intArray.count(values, '>')
array mapKeys = countMap.keys() // Returns
array mapValues = countMap.values() // Returns
Multiple comparison operators can be applied when counting multiple values.
intMatrix = matrix.new(3, 3, 0)
intMatrix.set(0, 0, 2), intMatrix.set(1, 0, 3), intMatrix.set(2, 0, 5)
intMatrix.set(0, 1, 2), intMatrix.set(1, 1, 4), intMatrix.set(2, 1, 2)
intMatrix.set(0, 2, 5), intMatrix.set(1, 2, 2), intMatrix.set(2, 2, 3)
values = array.from(3, 4)
comparisons = array.from('<', '>')
// Count the number of times an element is less than 3 and greater than 4
map countMap = intMatrix.count(values, comparisons)
array mapKeys = countMap.keys() // Returns
array mapValues = countMap.values() // Returns
LapseBacktestingTableLibrary "LapseBacktestingMetrics"
This library provides a robust set of quantitative backtesting and performance evaluation functions for Pine Script strategies. It’s designed to help traders, quants, and developers assess risk, return, and robustness through detailed statistical metrics — including Sharpe, Sortino, Omega, drawdowns, and trade efficiency.
Built to enhance any trading strategy’s evaluation framework, this library allows you to visualize performance with the quantlapseTable() function, producing an interactive on-chart performance table.
Credit to EliCobra and BikeLife76 for original concept inspiration.
curve(disp_ind)
Retrieves a selected performance curve of your strategy.
Parameters:
disp_ind (simple string): Type of curve to plot. Options include "Equity", "Open Profit", "Net Profit", "Gross Profit".
Returns: (float) Corresponding performance curve value.
cleaner(disp_ind, plot)
Filters and displays selected strategy plots for clean visualization.
Parameters:
disp_ind (simple string): Type of display.
plot (simple float): Strategy plot variable.
Returns: (float) Filtered plot value.
maxEquityDrawDown()
Calculates the maximum equity drawdown during the strategy’s lifecycle.
Returns: (float) Maximum equity drawdown percentage.
maxTradeDrawDown()
Computes the worst intra-trade drawdown among all closed trades.
Returns: (float) Maximum intra-trade drawdown percentage.
consecutive_wins()
Finds the highest number of consecutive winning trades.
Returns: (int) Maximum consecutive wins.
consecutive_losses()
Finds the highest number of consecutive losing trades.
Returns: (int) Maximum consecutive losses.
no_position()
Counts the maximum consecutive bars where no position was held.
Returns: (int) Maximum flat days count.
long_profit()
Calculates total profit generated by long positions as a percentage of initial capital.
Returns: (float) Total long profit %.
short_profit()
Calculates total profit generated by short positions as a percentage of initial capital.
Returns: (float) Total short profit %.
prev_month()
Measures the previous month’s profit or loss based on equity change.
Returns: (float) Monthly equity delta.
w_months()
Counts the number of profitable months in the backtest.
Returns: (int) Total winning months.
l_months()
Counts the number of losing months in the backtest.
Returns: (int) Total losing months.
checktf()
Returns the time-adjusted scaling factor used in Sharpe and Sortino ratio calculations based on chart timeframe.
Returns: (float) Annualization multiplier.
stat_calc()
Performs complete statistical computation including drawdowns, Sharpe, Sortino, Omega, trade stats, and profit ratios.
Returns: (array)
.
f_colors(x, nv)
Generates a color gradient for performance values, supporting dynamic table visualization.
Parameters:
x (simple string): Metric label name.
nv (simple float): Metric numerical value.
Returns: (color) Gradient color value for table background.
quantlapseTable(option, position)
Displays an interactive Performance Table summarizing all major backtesting metrics.
Includes Sharpe, Sortino, Omega, Profit Factor, drawdowns, profitability %, and trade statistics.
Parameters:
option (simple string): Table type — "Full", "Simple", or "None".
position (simple string): Table position — "Top Left", "Middle Right", "Bottom Left", etc.
Returns: (table) On-chart performance visualization table.
This library empowers advanced quantitative evaluation directly within Pine Script®, ideal for strategy developers seeking deeper performance diagnostics and intuitive on-chart metrics.
LibVeloLibrary "LibVelo"
This library provides a sophisticated framework for **Velocity
Profile (Flow Rate)** analysis. It measures the physical
speed of trading at specific price levels by relating volume
to the time spent at those levels.
## Core Concept: Market Velocity
Unlike Volume Profiles, which only answer "how much" traded,
Velocity Profiles answer "how fast" it traded.
It is calculated as:
`Velocity = Volume / Duration`
This metric (contracts per second) reveals hidden market
dynamics invisible to pure Volume or TPO profiles:
1. **High Velocity (Fast Flow):**
* **Aggression:** Initiative buyers/sellers hitting market
orders rapidly.
* **Liquidity Vacuum:** Price slips through a level because
order book depth is thin (low resistance).
2. **Low Velocity (Slow Flow):**
* **Absorption:** High volume but very slow price movement.
Indicates massive passive limit orders ("Icebergs").
* **Apathy:** Little volume over a long time. Lack of
interest from major participants.
## Architecture: Triple-Engine Composition
To ensure maximum performance while offering full statistical
depth for all metrics, this library utilises **object
composition** with a lazy evaluation strategy:
#### Engine A: The Master (`vpVol`)
* **Role:** Standard Volume Profile.
* **Purpose:** Maintains the "ground truth" of volume distribution,
price buckets, and ranges.
#### Engine B: The Time Container (`vpTime`)
* **Role:** specialized container for time duration (in ms).
* **Hack:** It repurposes standard volume arrays (specifically
`aBuy`) to accumulate time duration for each bucket.
#### Engine C: The Calculator (`vpVelo`)
* **Role:** Temporary scratchpad for derived metrics.
* **Purpose:** When complex statistics (like Value Area or Skewness)
are requested for **Velocity**, this engine is assembled
on-demand to leverage the full statistical power of `LibVPrf`
without rewriting complex algorithms.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
create(buckets, rangeUp, rangeLo, dynamic, valueArea, allot, estimator, cdfSteps, split, trendLen)
Construct a new `Velo` controller, initializing its engines.
Parameters:
buckets (int) : series int Number of price buckets ≥ 1.
rangeUp (float) : series float Upper price bound (absolute).
rangeLo (float) : series float Lower price bound (absolute).
dynamic (bool) : series bool Flag for dynamic adaption of profile ranges.
valueArea (int) : series int Percentage for Value Area (1..100).
allot (series AllotMode) : series AllotMode Allocation mode `Classic` or `PDF` (default `PDF`).
estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : series PriceEst PDF model for distribution attribution (default `Uniform`).
cdfSteps (int) : series int Resolution for PDF integration (default 20).
split (series SplitMode) : series SplitMode Buy/Sell split for the master volume engine (default `Classic`).
trendLen (int) : series int Look‑back for trend factor in dynamic split (default 3).
Returns: Velo Freshly initialised velocity profile.
method clone(self)
Create a deep copy of the composite profile.
Namespace types: Velo
Parameters:
self (Velo) : Velo Profile object to copy.
Returns: Velo A completely independent clone.
method clear(self)
Reset all engines and accumulators.
Namespace types: Velo
Parameters:
self (Velo) : Velo Profile object to clear.
Returns: Velo Cleared profile (chaining).
method merge(self, srcVolBuy, srcVolSell, srcTime, srcRangeUp, srcRangeLo, srcVolCvd, srcVolCvdHi, srcVolCvdLo)
Merges external data (Volume and Time) into the current profile.
Automatically handles resizing and re-bucketing if ranges differ.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
srcVolBuy (array) : array Source Buy Volume bucket array.
srcVolSell (array) : array Source Sell Volume bucket array.
srcTime (array) : array Source Time bucket array (ms).
srcRangeUp (float) : series float Upper price bound of the source data.
srcRangeLo (float) : series float Lower price bound of the source data.
srcVolCvd (float) : series float Source Volume CVD final value.
srcVolCvdHi (float) : series float Source Volume CVD High watermark.
srcVolCvdLo (float) : series float Source Volume CVD Low watermark.
Returns: Velo `self` (chaining).
method addBar(self, offset)
Main data ingestion. Distributes Volume and Time to buckets.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
offset (int) : series int Offset of the bar to add (default 0).
Returns: Velo `self` (chaining).
method setBuckets(self, buckets)
Sets the number of buckets for the profile.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
buckets (int) : series int New number of buckets.
Returns: Velo `self` (chaining).
method setRanges(self, rangeUp, rangeLo)
Sets the price range for the profile.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
rangeUp (float) : series float New upper price bound.
rangeLo (float) : series float New lower price bound.
Returns: Velo `self` (chaining).
method setValueArea(self, va)
Set the percentage of volume/time for the Value Area.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
va (int) : series int New Value Area percentage (0..100).
Returns: Velo `self` (chaining).
method getBuckets(self)
Returns the current number of buckets in the profile.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: series int The number of buckets.
method getRanges(self)
Returns the current price range of the profile.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns:
rangeUp series float The upper price bound of the profile.
rangeLo series float The lower price bound of the profile.
method getArrayBuyVol(self)
Returns the internal raw data array for **Buy Volume** directly.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: array The internal array for buy volume.
method getArraySellVol(self)
Returns the internal raw data array for **Sell Volume** directly.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: array The internal array for sell volume.
method getArrayTime(self)
Returns the internal raw data array for **Time** (in ms) directly.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: array The internal array for time duration.
method getArrayBuyVelo(self)
Returns the internal raw data array for **Buy Velocity** directly.
Automatically executes _assemble() if data is dirty.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: array The internal array for buy velocity.
method getArraySellVelo(self)
Returns the internal raw data array for **Sell Velocity** directly.
Automatically executes _assemble() if data is dirty.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
Returns: array The internal array for sell velocity.
method getBucketBuyVol(self, idx)
Returns the **Buy Volume** of a specific bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns: series float The buy volume.
method getBucketSellVol(self, idx)
Returns the **Sell Volume** of a specific bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns: series float The sell volume.
method getBucketTime(self, idx)
Returns the raw accumulated time (in ms) spent in a specific bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns: series float The time in milliseconds.
method getBucketBuyVelo(self, idx)
Returns the **Buy Velocity** (Aggressive Buy Flow) of a bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns: series float The buy velocity in .
method getBucketSellVelo(self, idx)
Returns the **Sell Velocity** (Aggressive Sell Flow) of a bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns: series float The sell velocity in .
method getBktBnds(self, idx)
Returns the price boundaries of a specific bucket.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
idx (int) : series int The index of the bucket.
Returns:
up series float The upper price bound of the bucket.
lo series float The lower price bound of the bucket.
method getPoc(self, target)
Returns Point of Control (POC) information for the specified target metric.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns:
pocIdx series int The index of the POC bucket.
pocPrice series float The mid-price of the POC bucket.
method getVA(self, target)
Returns Value Area (VA) information for the specified target metric.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns:
vaUpIdx series int The index of the upper VA bucket.
vaUpPrice series float The upper price bound of the VA.
vaLoIdx series int The index of the lower VA bucket.
vaLoPrice series float The lower price bound of the VA.
method getMedian(self, target)
Returns the Median price for the specified target metric distribution.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns:
medianIdx series int The index of the bucket containing the median.
medianPrice series float The median price.
method getAverage(self, target)
Returns the weighted average price (VWAP/TWAP) for the specified target.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns:
avgIdx series int The index of the bucket containing the average.
avgPrice series float The weighted average price.
method getStdDev(self, target)
Returns the standard deviation for the specified target distribution.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns: series float The standard deviation.
method getSkewness(self, target)
Returns the skewness for the specified target distribution.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns: series float The skewness.
method getKurtosis(self, target)
Returns the excess kurtosis for the specified target distribution.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns: series float The excess kurtosis.
method getSegments(self, target)
Returns the fundamental unimodal segments for the specified target metric.
Calculates on-demand if the target is 'Velocity' and data changed.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns: matrix A 2-column matrix where each row is an pair.
method getCvd(self, target)
Returns Cumulative Volume/Velo Delta (CVD) information for the target metric.
Namespace types: Velo
Parameters:
self (Velo) : Velo The profile object.
target (series Metric) : Metric The data aspect to analyse (Volume, Time, Velocity).
Returns:
cvd series float The final delta value.
cvdHi series float The historical high-water mark of the delta.
cvdLo series float The historical low-water mark of the delta.
Velo
Velo Composite Velocity Profile Controller.
Fields:
_vpVol (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine A: Master Volume source.
_vpTime (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine B: Time duration container (ms).
_vpVelo (VPrf type from AustrianTradingMachine/LibVPrf/2) : LibVPrf.VPrf Engine C: Scratchpad for velocity stats.
_aTime (array) : array Pointer alias to `vpTime.aBuy` (Time storage).
_valueArea (series float) : int Percentage of total volume to include in the Value Area (1..100)
_estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : LibBrSt.PriceEst PDF model for distribution attribution.
_allot (series AllotMode) : AllotMode Attribution model (Classic or PDF).
_cdfSteps (series int) : int Integration resolution for PDF.
_isDirty (series bool) : bool Lazy evaluation flag for vpVelo.
TrendDetectorLibLibrary "TrendDetector_Lib"
method formatTF(timeframe)
Namespace types: series string, simple string, input string, const string
Parameters:
timeframe (string) : (string) The timeframe to convert (e.g., "15", "60", "240").
Returns: (string) The formatted timeframe (e.g., "15M", "1H", "4H").
f_ma(type, src, len)
Computes a Moving Average value based on type and length.
Parameters:
type (simple string) : (string) One of: "SMA", "EMA", "RMA", "WMA", "VWMA".
src (float) : (series float) Source series for MA (e.g., close).
len (simple int) : (simple int) Length of the MA.
Returns: (float) The computed MA series.
render(tbl, trendDetectorSwitch, frameColor, frameWidth, borderColor, borderWidth, textColor, ma1ShowTrendData, ma1Timeframe, ma1Value, ma2ShowTrendData, ma2Timeframe, ma2Value, ma3ShowTrendData, ma3Timeframe, ma3Value)
Fills the provided table with Trend Detector contents.
@desc This renderer does NOT plot and does NOT create tables; call from indicator after your table exists.
Parameters:
tbl (table) : (table) Existing table to render into.
trendDetectorSwitch (bool) : (bool) Master toggle to draw the table content.
frameColor (color) : (color) Table frame color.
frameWidth (int) : (int) Table frame width (0–5).
borderColor (color) : (color) Table border color.
borderWidth (int) : (int) Table border width (0–5).
textColor (color) : (color) Table text color.
ma1ShowTrendData (bool) : (bool) Show MA #1 in table.
ma1Timeframe (simple string) : (string) MA #1 timeframe.
ma1Value (float)
ma2ShowTrendData (bool) : (bool) Show MA #2 in table.
ma2Timeframe (simple string) : (string) MA #2 timeframe.
ma2Value (float)
ma3ShowTrendData (bool) : (bool) Show MA #3 in table.
ma3Timeframe (simple string) : (string) MA #3 timeframe.
ma3Value (float)
QuickInputsLevelParserLibrary "QuickInputsLevelParser"
Provides a parsing library that indicator authors can use in order to parse Quick Inputs Levels.
parseLevels(s)
Parses the string content and returns the `QILevels` found within.
Parameters:
s (string) : The string to parse.
Returns: The parsed WTD levels.
zoneRange
Fields:
high (series float)
low (series float)
QILevels
Fields:
supplyLines (array)
supplyZones (array)
majorLines (array)
onZones (array)
onLines (array)
highLines (array)
lowLines (array)
htfZones (array)
mansupLines (array)
mansupmajLines (array)
mansupZones (array)
mansupmajZones (array)
manresLines (array)
manresmajLines (array)
manresZones (array)
manresmajZones (array)
fibonacci2Library "fibonacci2"
Useful methods to calculate and display fibonacci retracement
modelParamsNew(point_0, point_1)
Parameters:
point_0 (chart.point)
point_1 (chart.point)
modelParamsNew(this, point_0, point_1)
Parameters:
this (viewParams)
point_0 (chart.point)
point_1 (chart.point)
method toModelParams(this, point_0, point_1)
Namespace types: viewParams
Parameters:
this (viewParams)
point_0 (chart.point)
point_1 (chart.point)
method createModel(params)
Namespace types: modelParams
Parameters:
params (modelParams)
method createView(this, params)
Namespace types: model
Parameters:
this (model)
params (viewParams)
method delete(view)
Namespace types: view
Parameters:
view (view)
levelModelParams
Fields:
level (series float)
levelViewParams
Fields:
level (series float)
color (series color)
line_width (series int)
line_style (series lineStyleEnum enum from Hamster-Coder/drawing/1)
levelModel
Represents a Fibonacci retracement level
Fields:
level (series float) : The Fibonacci level ratio (e.g., 0.382, 0.5, 0.618)
value (series float) : The Y-coordinate on the chart corresponding to this level
modelParams
Represents the full parameter set for the Fibonacci retracement model
Fields:
point_1 (chart.point) : Coordinates of the anchor Point (1) of the model
point_0 (chart.point) : Coordinates of the anchor Point (0) of the model
levels (array) : List of levels to display for this model
model
Fields:
point_1 (chart.point)
point_0 (chart.point)
levels (array)
viewParams
Fields:
levels (array)
x1 (series int)
x2 (series int)
xloc (series string)
show_level_value (series bool)
value_format (series string)
force_overlay (series bool)
view
Fields:
model (model)
lines (array)
labels (array)
drawingLibrary "drawing"
Contains common types and methods to draw objects on the chart.
method toTextAlign(input)
Namespace types: series textHorizontalAlignEnum
Parameters:
input (series textHorizontalAlignEnum)
method toTextAlign(input)
Namespace types: series textVertialAlignEnum
Parameters:
input (series textVertialAlignEnum)
method toSize(input)
Namespace types: series sizeEnum
Parameters:
input (series sizeEnum)
method toStyle(input)
Namespace types: series lineStyleEnum
Parameters:
input (series lineStyleEnum)
TraderMathLibrary "TraderMath"
A collection of essential trading utilities and mathematical functions used for technical analysis,
including DEMA, Fisher Transform, directional movement, and ADX calculations.
dema(source, length)
Calculates the value of the Double Exponential Moving Average (DEMA).
Parameters:
source (float) : (series int/float) Series of values to process.
length (simple int) : (simple int) Length for the smoothing parameter calculation.
Returns: (float) The double exponentially weighted moving average of the `source`.
roundVal(val)
Constrains a value to the range .
Parameters:
val (float) : (float) Value to constrain.
Returns: (float) Value limited to the range .
fisherTransform(length)
Computes the Fisher Transform oscillator, enhancing turning point sensitivity.
Parameters:
length (int) : (int) Lookback length used to normalize price within the high-low range.
Returns: (float) Fisher Transform value.
dirmov(len)
Calculates the Plus and Minus Directional Movement components (DI+ and DI−).
Parameters:
len (simple int) : (int) Lookback length for directional movement.
Returns: (float ) Array containing .
adx(dilen, adxlen)
Computes the Average Directional Index (ADX) based on DI+ and DI−.
Parameters:
dilen (simple int) : (int) Lookback length for directional movement calculation.
adxlen (simple int) : (int) Smoothing length for ADX computation.
Returns: (float) Average Directional Index value (0–100).
MirPapa_Lib_trendLibrary: MirPapa_Lib_trend
getMaColor(level)
Parameters:
level (int): 1 = lowest, 2 = low, 3 = mid, 4 = high, 5 = highest, 6 = base
getMA(mode, src, len)
Parameters:
mode (string): MA type
src (float): source
len (simple int): period
Returns: selected MA
getMA(maName, src, intLow, intMid, intHigh)
Parameters:
maName (string): MA type
src (float): source
intLow (simple int): short-term
intMid (simple int): mid-term
intHigh (simple int): long-term
Returns: array
getMA(maName, src, intLowest, intLow, intMid, intHigh, intHighest, intBase)
Parameters:
maName (string): MA type
src (float): source
intLowest (simple int): ultra-short
intLow (simple int): short
intMid (simple int): mid
intHigh (simple int): long
intHighest (simple int): ultra-long
intBase (simple int): base line
Returns: array
getStochastic(src, intLen)
Parameters:
src (float): source
intLen (int): period
Returns: selected stochastic
getStochastic(src, intLow, intMid, intHigh)
Parameters:
src (float): source
intLow (int): short-term
intMid (int): mid-term
intHigh (int): long-term
Returns:
getStochastic(src, intLowest, intLow, intMid, intHigh, intHighest, intBase)
Parameters:
src (float): source
intLowest (int): ultra-short
intLow (int): short
intMid (int): mid
intHigh (int): long
intHighest (int): ultra-long
intBase (int): base
Returns:
getRSX(src, intLen)
Parameters:
src (float): source
intLen (int): period
Returns: selected RSX
getRSX(src, intLow, intMid, intHigh)
Parameters:
src (float): source
intLow (int): short-term
intMid (int): mid-term
intHigh (int): long-term
Returns:
getRSX(src, intLowest, intLow, intMid, intHigh, intHighest, intBase)
Parameters:
src (float): source
intLowest (int): ultra-short
intLow (int): short
intMid (int): mid
intHigh (int): long
intHighest (int): ultra-long
intBase (int): base
Returns:
getMACD(src, fastLen, slowLen, signalLen)
Parameters:
src (float): source
fastLen (simple int): fast EMA period
slowLen (simple int): slow EMA period
signalLen (simple int): signal line period
Returns:
getBollingerBand(src, len, mult)
Parameters:
src (float): source
len (int): period
mult (float): standard deviation multiplier
Returns:
getATR(intLen)
Parameters:
intLen (simple int): ATR period
Returns: selected ATR
getATR(intLow, intMid, intHigh)
Parameters:
intLow (simple int): short-term
intMid (simple int): mid-term
intHigh (simple int): long-term
Returns: array
getATR(intLowest, intLow, intMid, intHigh, intHighest, intBase)
Parameters:
intLowest (simple int): ultra-short
intLow (simple int): short
intMid (simple int): mid
intHigh (simple int): long
intHighest (simple int): ultra-long
intBase (simple int): base
isCross(fastLine, baseLine)
Parameters:
fastLine (float): fast line
baseLine (float): base line
Returns: state (true/false)
isMAtrend(maLow, maMid, maHigh)
Parameters:
maLow (float): fast MA
maMid (float): mid MA
maHigh (float): slow MA
Returns: trend state
isMAline(val, valPrev, intBaseLine)
Parameters:
val (float): current value
valPrev (float): previous value
intBaseLine (int): base value
Returns: state
getStage(v1, v2, v3)
Parameters:
v1 (float): first value
v2 (float): second value
v3 (float): third value
Returns: stage number (1–6)
getBgColor(stage)
Parameters:
stage (int): stage number
Returns: color
getBgColor(stage, transp)
Parameters:
stage (int): stage number
transp (int): transparency
Returns: color
getBGColor(v1, v2, v3)
Parameters:
v1 (float): first value
v2 (float): second value
v3 (float): third value
Returns: color
getBGColor(v1, v2, v3, transp)
Parameters:
v1 (float): first value
v2 (float): second value
v3 (float): third value
transp (int): transparency
Returns: color
createStackedLabel(labelText, isUp, maLowest, maLow, maMid, maHigh, maHighest, maBase)
Parameters:
labelText (string): label text
isUp (bool): true = up, false = down
maLowest (float)
maLow (float)
maMid (float)
maHigh (float)
maHighest (float)
maBase (float)
Returns: created label
isDoubleBottom(src, left, right)
Parameters:
src (float): reference series (e.g., mid MA or low)
left (int): left bar count for pivot search
right (int): right bar count for pivot search
Returns: true if double bottom detected (previous pivot low < current pivot low)
isDoubleTop(src, left, right)
Parameters:
src (float): reference series (e.g., mid MA or high)
left (int): left bar count for pivot search
right (int): right bar count for pivot search
Returns: true if double top detected (previous pivot high > current pivot high)
isFractalHigh(src, left, right)
Parameters:
src (float): high series (e.g., high or mid MA)
left (int): left confirmation bars
right (int): right confirmation bars
Returns: true if fractal high detected
isFractalLow(src, left, right)
Parameters:
src (float): low series (e.g., low or mid MA)
left (int): left confirmation bars
right (int): right confirmation bars
Returns: true if fractal low detected
lower_tfLibrary "lower_tf"
█ OVERVIEW
This library is an enhanced (opinionated) version of the library originally developed by PineCoders contained in lower_tf .
It is a Pine Script® programming tool for advanced lower-timeframe selection and intra-bar analysis.
█ CONCEPTS
Lower Timeframe Analysis
Lower timeframe analysis refers to the analysis of price action and market microstructure using data from timeframes shorter than the current chart period. This technique allows traders and analysts to gain deeper insights into market dynamics, volume distribution, and the price movements occurring within each bar on the chart. In Pine Script®, the request.security_lower_tf() function allows this analysis by accessing intrabar data.
The library provides a comprehensive set of functions for accurate mapping of lower timeframes, dynamic precision control, and optimized historical coverage using request.security_lower_tf().
█ IMPROVEMENTS
The original library implemented ten precision levels. This enhanced version extends that to twelve levels, adding two ultra-high-precision options:
Coverage-Based Precision (Original 5 levels):
1. "Covering most chart bars (least precise)"
2. "Covering some chart bars (less precise)"
3. "Covering fewer chart bars (more precise)"
4. "Covering few chart bars (very precise)"
5. "Covering the least chart bars (most precise)"
Intrabar-Count-Based Precision (Expanded from 5 to 7 levels):
6. "~12 intrabars per chart bar"
7. "~24 intrabars per chart bar"
8. "~50 intrabars per chart bar"
9. "~100 intrabars per chart bar"
10. "~250 intrabars per chart bar"
11. "~500 intrabars per chart bar" ← NEW
12. "~1000 intrabars per chart bar" ← NEW
The key enhancements in this version include:
1. Extended Precision Range: Adds two ultra-high-precision levels (~500 and ~1000 intrabars) for advanced microstructure analysis requiring maximum granularity.
2. Market-Agnostic Implementation: Eliminates the distinction between crypto/forex and traditional markets, removing the mktFactor variable in favor of a unified, predictable approach across all asset classes.
3. Explicit Precision Mapping: Completely refactors the timeframe selection logic using native Pine Script® timeframe properties ( timeframe.isseconds , timeframe.isminutes , timeframe.isdaily , timeframe.isweekly , timeframe.ismonthly ) and explicit multiplier-based lookup tables. The original library used minute-based calculations with market-dependent conditionals that produced inconsistent results. This version provides deterministic, predictable mappings for every chart timeframe, ensuring consistent precision behavior regardless of asset type or market hours.
An example of the differences can be seen side-by-side in the chart below, where the original library is on the left and the enhanced version is on the right:
█ USAGE EXAMPLE
// This Pine Script® code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © andre_007
//@version=6
indicator("lower_tf Example")
import andre_007/lower_tf/1 as LTF
import PineCoders/Time/5 as PCtime
//#region ———————————————————— Example code
// ————— Constants
color WHITE = color.white
color GRAY = color.gray
string LTF1 = "Covering most chart bars (least precise)"
string LTF2 = "Covering some chart bars (less precise)"
string LTF3 = "Covering less chart bars (more precise)"
string LTF4 = "Covering few chart bars (very precise)"
string LTF5 = "Covering the least chart bars (most precise)"
string LTF6 = "~12 intrabars per chart bar"
string LTF7 = "~24 intrabars per chart bar"
string LTF8 = "~50 intrabars per chart bar"
string LTF9 = "~100 intrabars per chart bar"
string LTF10 = "~250 intrabars per chart bar"
string LTF11 = "~500 intrabars per chart bar"
string LTF12 = "~1000 intrabars per chart bar"
string TT_LTF = "This selection determines the approximate number of intrabars analyzed per chart bar. Higher numbers of
intrabars produce more granular data at the cost of less historical bar coverage, because the maximum number of
available intrabars is 200K.
The first five options set the lower timeframe based on a specified relative level of chart bar coverage.
The last five options set the lower timeframe based on an approximate number of intrabars per chart bar."
string TAB_TXT = "Uses intrabars at the {0} timeframe. Avg intrabars per chart bar:
{1,number,#.#} Chart bars covered: {2} of {3} ({4,number,#.##}%)"
string ERR_TXT = "No intrabar information exists at the {1}{0}{1} timeframe."
// ————— Inputs
string ltfModeInput = input.string(LTF3, "Intrabar precision", options = , tooltip = TT_LTF)
bool showInfoBoxInput = input.bool(true, "Show information box ")
string infoBoxSizeInput = input.string("normal", "Size ", inline = "01", options = )
string infoBoxYPosInput = input.string("bottom", "↕", inline = "01", options = )
string infoBoxXPosInput = input.string("right", "↔", inline = "01", options = )
color infoBoxColorInput = input.color(GRAY, "", inline = "01")
color infoBoxTxtColorInput = input.color(WHITE, "T", inline = "01")
// ————— Calculations
// @variable A "string" representing the lower timeframe for the data request.
// NOTE:
// This line is a good example where using `var` in the declaration can improve a script's performance.
// By using `var` here, the script calls `ltf()` only once, on the dataset's first bar, instead of redundantly
// evaluating unchanging strings on every bar. We only need one evaluation of this function because the selected
// timeframe does not change across bars in this script.
var string ltfString = LTF.ltf(ltfModeInput, LTF1, LTF2, LTF3, LTF4, LTF5, LTF6, LTF7, LTF8, LTF9, LTF10, LTF11, LTF12)
// @variable An array containing all intrabar `close` prices from the `ltfString` timeframe for the current chart bar.
array intrabarCloses = request.security_lower_tf(syminfo.tickerid, ltfString, close)
// Calculate the intrabar stats.
= LTF.ltfStats(intrabarCloses)
int chartBars = bar_index + 1
// ————— Visuals
// Plot the `avgIntrabars` and `intrabars` series in all display locations.
plot(avgIntrabars, "Average intrabars", color.silver, 6)
plot(intrabars, "Intrabars", color.blue, 2)
// Plot the `chartBarsCovered` and `chartBars` values in the Data Window and the script's status line.
plot(chartBarsCovered, "Chart bars covered", display = display.data_window + display.status_line)
plot(chartBars, "Chart bars total", display = display.data_window + display.status_line)
// Information box logic.
if showInfoBoxInput
// @variable A single-cell table that displays intrabar information.
var table infoBox = table.new(infoBoxYPosInput + "_" + infoBoxXPosInput, 1, 1)
// @variable The span of the `ltfString` timeframe formatted as a number of automatically selected time units.
string formattedLtf = PCtime.formattedNoOfPeriods(timeframe.in_seconds(ltfString) * 1000)
// @variable A "string" containing the formatted text to display in the `infoBox`.
string txt = str.format(
TAB_TXT, formattedLtf, avgIntrabars, chartBarsCovered, chartBars, chartBarsCovered / chartBars * 100, "'"
)
// Initialize the `infoBox` cell on the first bar.
if barstate.isfirst
table.cell(
infoBox, 0, 0, txt, text_color = infoBoxTxtColorInput, text_size = infoBoxSizeInput,
bgcolor = infoBoxColorInput
)
// Update the cell's text on the latest bar.
else if barstate.islast
table.cell_set_text(infoBox, 0, 0, txt)
// Raise a runtime error if no intrabar data is available.
if ta.cum(intrabars) == 0 and barstate.islast
runtime.error(str.format(ERR_TXT, ltfString, "'"))
//#endregion
█ EXPORTED FUNCTIONS
ltf(userSelection, choice1, choice2, ...)
Returns the optimal lower timeframe string based on user selection and current chart timeframe. Dynamically calculates precision to balance granularity with historical coverage within the 200K intrabar limit.
ltfStats(intrabarValues)
Analyzes an intrabar array returned by request.security_lower_tf() and returns statistics: number of intrabars in current bar, total chart bars covered, and average intrabars per bar.
█ CREDITS AND LICENSING
Original Concept : PineCoders Team
Original Lower TF Library :
License : Mozilla Public License 2.0
GBTimes2Library "GBTimes2"
Library containing all GB (Gartley Butterfly) time values for trading indicators
getTimes()
Returns array of all GB time values in packed format (HHMM)
Returns: Array of integers representing GB times throughout the day
