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
Wskaźniki i strategie
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.
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
ChainAggLib - library for aggregation of main chain tickersLibrary "ChainAggLib"
ChainAggLib — token -> main protocol coin (chain) and top-5 exchange tickers for volume aggregation.
Library only (no plots). All helpers are pure functions and do not modify globals.
norm_sym(s)
Parameters:
s (string)
get_base_from_symbol(full_symbol)
Parameters:
full_symbol (string)
get_chain_for_token(token_symbol)
Parameters:
token_symbol (string)
get_top5_exchange_tickers_for_chain(chain_code)
Parameters:
chain_code (string)
get_top5_exchange_tickers_for_token(token_symbol)
Parameters:
token_symbol (string)
join_tickers(arr)
Parameters:
arr (array)
contains_symbol(arr, symbol)
Parameters:
arr (array)
symbol (string)
contains_current(arr)
Parameters:
arr (array)
get_arr_for_current_token()
get_chain_for_current()
PriceFormatLibrary for automatically converting price values to formatted strings
matching the same format that TradingView uses to display open/high/low/close prices on the chart.
█ OVERVIEW
This library is intended for Pine Coders who are authors of scripts that display numbers onto a user's charts. Typically, 𝚜𝚝𝚛.𝚝𝚘𝚜𝚝𝚛𝚒𝚗𝚐() would be used to convert a number into a string which can be displayed in a label / box / table, but this only works well for values that are formatted as a simple decimal number. The purpose of this library is to provide an easy way to create a formatted string for values which use other types of formats besides the decimal format.
The main functions exported by this library are:
𝚏𝚘𝚛𝚖𝚊𝚝𝙿𝚛𝚒𝚌𝚎() - creates a formatted string from a price value
𝚖𝚎𝚊𝚜𝚞𝚛𝚎𝙿𝚛𝚒𝚌𝚎𝙲𝚑𝚊𝚗𝚐𝚎() - creates a formatted string from the distance between two prices
𝚝𝚘𝚜𝚝𝚛𝚒𝚗𝚐() - an alternative to the built-in 𝚜𝚝𝚛.𝚝𝚘𝚜𝚝𝚛𝚒𝚗𝚐(𝚟𝚊𝚕𝚞𝚎, 𝚏𝚘𝚛𝚖𝚊𝚝)
This library also exports some auxiliary functions which are used under the hood of the previously mentioned functions, but can also be useful to Pine Coders that need fine-tuned control for customized formatting of numeric values:
Functions that determine information about the current chart:
𝚒𝚜𝙵𝚛𝚊𝚌𝚝𝚒𝚘𝚗𝚊𝚕𝙵𝚘𝚛𝚖𝚊𝚝(), 𝚒𝚜𝚅𝚘𝚕𝚞𝚖𝚎𝙵𝚘𝚛𝚖𝚊𝚝(), 𝚒𝚜𝙿𝚎𝚛𝚌𝚎𝚗𝚝𝚊𝚐𝚎𝙵𝚘𝚛𝚖𝚊𝚝(), 𝚒𝚜𝙳𝚎𝚌𝚒𝚖𝚊𝚕𝙵𝚘𝚛𝚖𝚊𝚝(), 𝚒𝚜𝙿𝚒𝚙𝚜𝙵𝚘𝚛𝚖𝚊𝚝()
Functions that convert a 𝚏𝚕𝚘𝚊𝚝 value to a formatted string:
𝚊𝚜𝙳𝚎𝚌𝚒𝚖𝚊𝚕(), 𝚊𝚜𝙿𝚒𝚙𝚜(), 𝚊𝚜𝙵𝚛𝚊𝚌𝚝𝚒𝚘𝚗𝚊𝚕(), 𝚊𝚜𝚅𝚘𝚕𝚞𝚖𝚎()
█ EXAMPLES
• Simple Example
This example shows the simplest way to utilize this library.
//@version=6
indicator("Simple Example")
import n00btraders/PriceFormat/1
var table t = table.new(position.middle_right, 2, 1, bgcolor = color.new(color.blue, 90), force_overlay = true)
if barstate.isfirst
table.cell(t, 0, 0, "Current Price: ", text_color = color.black, text_size = 40)
table.cell(t, 1, 0, text_color = color.blue, text_size = 40)
if barstate.islast
string lastPrice = close.formatPrice() // Simple, easy way to format price
table.cell_set_text(t, 1, 0, lastPrice)
• Complex Example
This example calls all of the main functions and uses their optional arguments.
//@version=6
indicator("Complex Example")
import n00btraders/PriceFormat/1
// Enum values that can be used as optional arguments
precision = input.enum(PriceFormat.Precision.DEFAULT)
language = input.enum(PriceFormat.Language.ENGLISH)
// Main library functions used to create formatted strings
string formattedOpen = open.formatPrice(precision, language, allowPips = true)
string rawOpenPrice = PriceFormat.tostring(open, format.price)
string formattedClose = close.formatPrice(precision, language, allowPips = true)
string rawClosePrice = PriceFormat.tostring(close, format.price)
= PriceFormat.measurePriceChange(open, close, precision, language, allowPips = true)
// Labels to display formatted values on chart
string prices = str.format("Open: {0} ({1}) Close: {2} ({3})", formattedOpen, rawOpenPrice, formattedClose, rawClosePrice)
string change = str.format("Change (close - open): {0} / {1}", distance, ticks)
label.new(chart.point.now(high), prices, yloc = yloc.abovebar, textalign = text.align_left, force_overlay = true)
label.new(chart.point.now(low), change, yloc = yloc.belowbar, style = label.style_label_up, force_overlay = true)
█ NOTES
• Function Descriptions
The library source code uses Markdown for the exported functions. Hover over a function/method call in the Pine Editor to display formatted, detailed information about the function/method.
• Precision Settings
The Precision option in the chart settings can change the format of how prices are displayed on the chart. Since the user's selected choice cannot be known through any Pine built-in variable, this library provides a 𝙿𝚛𝚎𝚌𝚒𝚜𝚒𝚘𝚗 enum that can be used as an optional script input for the user to specify their selected choice.
• Language Settings
The Language option in the user menu can change the decimal/grouping separators in the prices that are displayed on the chart. Since the user's selected choice cannot be known through any Pine built-in variable, this library provides a 𝙻𝚊𝚗𝚐𝚞𝚊𝚐𝚎 enum that can be used as an optional script input for the user to specify their selected choice.
█ EXPORTED FUNCTIONS
method formatPrice(price, precision, language, allowPips)
Formats a price value to match how it would be displayed on the user's current chart.
Namespace types: series float, simple float, input float, const float
Parameters:
price (float) : The value to format.
precision (series Precision) : A Precision.* enum value.
language (series Language) : A Language.* enum value.
allowPips (simple bool) : Whether to allow decimal numbers to display as pips.
Returns: Automatically formatted price string.
measurePriceChange(startPrice, endPrice, precision, language, allowPips)
Measures a change in price in terms of both distance and ticks.
Parameters:
startPrice (float) : The starting price.
endPrice (float) : The ending price.
precision (series Precision) : A Precision.* enum value.
language (series Language) : A Language.* enum value.
allowPips (simple bool) : Whether to allow decimal numbers to display as pips.
Returns: A tuple of formatted strings: .
method tostring(value, format)
Alternative to the Pine `str.tostring(value, format)` built-in function.
Namespace types: series float, simple float, input float, const float
Parameters:
value (float) : (series float) The value to format.
format (string) : (series string) The format string.
Returns: String in the specified format.
isFractionalFormat()
Determines if the default behavior of the chart's price scale is to use a fractional format.
Returns: True if the chart can display prices in fractional format.
isVolumeFormat()
Determines if the default behavior of the chart's price scale is to display prices as volume.
Returns: True if the chart can display prices as volume.
isPercentageFormat()
Determines if the default behavior of the chart's price scale is to display percentages.
Returns: True if the chart can display prices as percentages.
isDecimalFormat()
Determines if the default behavior of the chart's price scale is to use a decimal format.
Returns: True if the chart can display prices in decimal format.
isPipsFormat()
Determines if the current symbol's prices can be displayed as pips.
Returns: True if the chart can display prices as pips.
method asDecimal(value, precision, minTick, decimalSeparator, groupingSeparator, eNotation)
Converts a number to a string in decimal format.
Namespace types: series float, simple float, input float, const float
Parameters:
value (float) : The value to format.
precision (int) : Number of decimal places.
minTick (float) : Minimum tick size.
decimalSeparator (string) : The decimal separator.
groupingSeparator (string) : The thousands separator, aka digit group separator.
eNotation (bool) : Whether the result should use E notation.
Returns: String in decimal format.
method asPips(value, priceScale, minMove, minMove2, decimalSeparator, groupingSeparator)
Converts a number to a string in decimal format with the last digit replaced by a superscript.
Namespace types: series float, simple float, input float, const float
Parameters:
value (float) : The value to format.
priceScale (int) : Price scale.
minMove (int) : Min move.
minMove2 (int) : Min move 2.
decimalSeparator (string) : The decimal separator.
groupingSeparator (string) : The thousands separator, aka digit group separator.
Returns: String in decimal format with an emphasis on the pip value.
method asFractional(value, priceScale, minMove, minMove2, fractionalSeparator1, fractionalSeparator2)
Converts a number to a string in fractional format.
Namespace types: series float, simple float, input float, const float
Parameters:
value (float) : The value to format.
priceScale (int) : Price scale.
minMove (int) : Min move.
minMove2 (int) : Min move 2.
fractionalSeparator1 (string) : The primary fractional separator.
fractionalSeparator2 (string) : The secondary fractional separator.
Returns: String in fractional format.
method asVolume(value, precision, minTick, decimalSeparator, groupingSeparator, spacing)
Converts a number to a string in volume format.
Namespace types: series float, simple float, input float, const float
Parameters:
value (float) : The value to format.
precision (int) : Maximum number of decimal places.
minTick (float) : Minimum tick size.
decimalSeparator (string) : The decimal separator.
groupingSeparator (string) : The thousands separator, aka digit group separator.
spacing (string) : The whitespace separator.
Returns: String in volume format.
LibVPrfLibrary "LibVPrf"
This library provides an object-oriented framework for volume
profile analysis in Pine Script®. It is built around the `VProf`
User-Defined Type (UDT), which encapsulates all data, settings,
and statistical metrics for a single profile, enabling stateful
analysis with on-demand calculations.
Key Features:
1. **Object-Oriented Design (UDT):** The library is built around
the `VProf` UDT. This object encapsulates all profile data
and provides methods for its full lifecycle management,
including creation, cloning, clearing, and merging of profiles.
2. **Volume Allocation (`AllotMode`):** Offers two methods for
allocating a bar's volume:
- **Classic:** Assigns the entire bar's volume to the close
price bucket.
- **PDF:** Distributes volume across the bar's range using a
statistical price distribution model from the `LibBrSt` library.
3. **Buy/Sell Volume Splitting (`SplitMode`):** Provides methods
for classifying volume into buying and selling pressure:
- **Classic:** Classifies volume based on the bar's color (Close vs. Open).
- **Dynamic:** A specific model that analyzes candle structure
(body vs. wicks) and a short-term trend factor to
estimate the buy/sell share at each price level.
4. **Statistical Analysis (On-Demand):** Offers a suite of
statistical metrics calculated using a "Lazy Evaluation"
pattern (computed only when requested via `get...` methods):
- **Central Tendency:** Point of Control (POC), VWAP, and Median.
- **Dispersion:** Value Area (VA) and Population Standard Deviation.
- **Shape:** Skewness and Excess Kurtosis.
- **Delta:** Cumulative Volume Delta, including its
historical high/low watermarks.
5. **Structural Analysis:** Includes a parameter-free method
(`getSegments`) to decompose a profile into its fundamental
unimodal segments, allowing for modality detection (e.g.,
identifying bimodal profiles).
6. **Dynamic Profile Management:**
- **Auto-Fitting:** Profiles set to `dynamic = true` will
automatically expand their price range to fit new data.
- **Manipulation:** The resolution, price range, and Value Area
of a dynamic profile can be changed at any time. This
triggers a resampling process that uses a **linear
interpolation model** to re-bucket existing volume.
- **Assumption:** Non-dynamic profiles are fixed and will throw
a `runtime.error` if `addBar` is called with data
outside their initial range.
7. **Bucket-Level Access:** Provides getter methods for direct
iteration and analysis of the raw buy/sell volume and price
boundaries of each individual price bucket.
---
**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 `VProf` object with fixed bucket count & range.
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 of total volume to include in the Value Area (1..100)
allot (series AllotMode) : series AllotMode Allocation mode `classic` or `pdf` (default `classic`)
estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : series LibBrSt.PriceEst PDF model when `model == PDF`. (deflault = 'uniform')
cdfSteps (int) : series int even #sub-intervals for Simpson rule (default 20)
split (series SplitMode) : series SplitMode Buy/Sell determination (default `classic`)
trendLen (int) : series int Look‑back bars for trend factor (default 3)
Returns: VProf freshly initialised profile
method clone(self)
Create a deep copy of the volume profile.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object to copy
Returns: VProf A new, independent copy of the profile
method clear(self)
Reset all bucket tallies while keeping configuration intact.
Namespace types: VProf
Parameters:
self (VProf) : VProf profile object
Returns: VProf cleared profile (chaining)
method merge(self, srcABuy, srcASell, srcRangeUp, srcRangeLo, srcCvd, srcCvdHi, srcCvdLo)
Merges volume data from a source profile into the current profile.
If resizing is needed, it performs a high-fidelity re-bucketing of existing
volume using a linear interpolation model inferred from neighboring buckets,
preventing aliasing artifacts and ensuring accurate volume preservation.
Namespace types: VProf
Parameters:
self (VProf) : VProf The target profile object to merge into.
srcABuy (array) : array The source profile's buy volume bucket array.
srcASell (array) : array The source profile's sell volume bucket array.
srcRangeUp (float) : series float The upper price bound of the source profile.
srcRangeLo (float) : series float The lower price bound of the source profile.
srcCvd (float) : series float The final Cumulative Volume Delta (CVD) value of the source profile.
srcCvdHi (float) : series float The historical high-water mark of the CVD from the source profile.
srcCvdLo (float) : series float The historical low-water mark of the CVD from the source profile.
Returns: VProf `self` (chaining), now containing the merged data.
method addBar(self, offset)
Add current bar’s volume to the profile (call once per realtime bar).
classic mode: allocates all volume to the close bucket and classifies
by `close >= open`. PDF mode: distributes volume across buckets by the
estimator’s CDF mass. For `split = dynamic`, the buy/sell share per
price is computed via context-driven piecewise s(u).
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
offset (int) : series int To offset the calculated bar
Returns: VProf `self` (method chaining)
method setBuckets(self, buckets)
Sets the number of buckets for the volume profile.
Behavior depends on the `isDynamic` flag.
- If `dynamic = true`: Works on filled profiles by re-bucketing to a new resolution.
- If `dynamic = false`: Only works on empty profiles to prevent accidental changes.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
buckets (int) : series int The new number of buckets
Returns: VProf `self` (chaining)
method setRanges(self, rangeUp, rangeLo)
Sets the price range for the volume profile.
Behavior depends on the `dynamic` flag.
- If `dynamic = true`: Works on filled profiles by re-bucketing existing volume.
- If `dynamic = false`: Only works on empty profiles to prevent accidental changes.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
rangeUp (float) : series float The new upper price bound
rangeLo (float) : series float The new lower price bound
Returns: VProf `self` (chaining)
method setValueArea(self, valueArea)
Set the percentage of volume for the Value Area. If the value
changes, the profile is finalized again.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
valueArea (int) : series int The new Value Area percentage (0..100)
Returns: VProf `self` (chaining)
method getBktBuyVol(self, idx)
Get Buy volume of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns: series float Buy volume ≥ 0
method getBktSellVol(self, idx)
Get Sell volume of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns: series float Sell volume ≥ 0
method getBktBnds(self, idx)
Get Bounds of a bucket.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
idx (int) : series int Bucket index
Returns:
up series float The upper price bound of the bucket.
lo series float The lower price bound of the bucket.
method getPoc(self)
Get POC information.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
Returns:
pocIndex series int The index of the Point of Control (POC) bucket.
pocPrice. series float The mid-price of the Point of Control (POC) bucket.
method getVA(self)
Get Value Area (VA) information.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object
Returns:
vaUpIndex series int The index of the upper bound bucket of the Value Area.
vaUpPrice series float The upper price bound of the Value Area.
vaLoIndex series int The index of the lower bound bucket of the Value Area.
vaLoPrice series float The lower price bound of the Value Area.
method getMedian(self)
Get the profile's median price and its bucket index. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
medianIndex series int The index of the bucket containing the Median.
medianPrice series float The Median price of the profile.
method getVwap(self)
Get the profile's VWAP and its bucket index. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
vwapIndex series int The index of the bucket containing the VWAP.
vwapPrice series float The Volume Weighted Average Price of the profile.
method getStdDev(self)
Get the profile's volume-weighted standard deviation. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Standard deviation of the profile.
method getSkewness(self)
Get the profile's skewness. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Skewness of the profile.
method getKurtosis(self)
Get the profile's excess kurtosis. Calculates the value on-demand if stale.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns: series float The Kurtosis of the profile.
method getSegments(self)
Get the profile's fundamental unimodal segments. Calculates on-demand if stale.
Uses a parameter-free, pivot-based recursive algorithm.
Namespace types: VProf
Parameters:
self (VProf) : VProf The profile object.
Returns: matrix A 2-column matrix where each row is an pair.
method getCvd(self)
Cumulative Volume Delta (CVD) like metric over all buckets.
Namespace types: VProf
Parameters:
self (VProf) : VProf Profile object.
Returns:
cvd series float The final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol).
cvdHi series float The running high-water mark of the CVD as volume was added.
cvdLo series float The running low-water mark of the CVD as volume was added.
VProf
VProf Bucketed Buy/Sell volume profile plus meta information.
Fields:
buckets (series int) : int Number of price buckets (granularity ≥1)
rangeUp (series float) : float Upper price range (absolute)
rangeLo (series float) : float Lower price range (absolute)
dynamic (series bool) : bool Flag for dynamic adaption of profile ranges
valueArea (series int) : int Percentage of total volume to include in the Value Area (1..100)
allot (series AllotMode) : AllotMode Allocation mode `classic` or `pdf`
estimator (series PriceEst enum from AustrianTradingMachine/LibBrSt/1) : LibBrSt.PriceEst Price density model when `model == PDF`
cdfSteps (series int) : int Simpson integration resolution (even ≥2)
split (series SplitMode) : SplitMode Buy/Sell split strategy per bar
trendLen (series int) : int Look‑back length for trend factor (≥1)
maxBkt (series int) : int User-defined number of buckets (unclamped)
aBuy (array) : array Buy volume per bucket
aSell (array) : array Sell volume per bucket
cvd (series float) : float Final Cumulative Volume Delta (Total Buy Vol - Total Sell Vol).
cvdHi (series float) : float Running high-water mark of the CVD as volume was added.
cvdLo (series float) : float Running low-water mark of the CVD as volume was added.
poc (series int) : int Index of max‑volume bucket (POC). Is `na` until calculated.
vaUp (series int) : int Index of upper Value‑Area bound. Is `na` until calculated.
vaLo (series int) : int Index of lower value‑Area bound. Is `na` until calculated.
median (series float) : float Median price of the volume distribution. Is `na` until calculated.
vwap (series float) : float Profile VWAP (Volume Weighted Average Price). Is `na` until calculated.
stdDev (series float) : float Standard Deviation of volume around the VWAP. Is `na` until calculated.
skewness (series float) : float Skewness of the volume distribution. Is `na` until calculated.
kurtosis (series float) : float Excess Kurtosis of the volume distribution. Is `na` until calculated.
segments (matrix) : matrix A 2-column matrix where each row is an pair. Is `na` until calculated.
LibBrStLibrary "LibBrSt"
This is a library for quantitative analysis, designed to estimate
the statistical properties of price movements *within* a single
OHLC bar, without requiring access to tick data. It provides a
suite of estimators based on various statistical and econometric
models, allowing for analysis of intra-bar volatility and
price distribution.
Key Capabilities:
1. **Price Distribution Models (`PriceEst`):** Provides a selection
of estimators that model intra-bar price action as a probability
distribution over the range. This allows for the
calculation of the intra-bar mean (`priceMean`) and standard
deviation (`priceStdDev`) in absolute price units. Models include:
- **Symmetric Models:** `uniform`, `triangular`, `arcsine`,
`betaSym`, and `t4Sym` (Student-t with fat tails).
- **Skewed Models:** `betaSkew` and `t4Skew`, which adjust
their shape based on the Open/Close position.
- **Model Assumptions:** The skewed models rely on specific
internal constants. `betaSkew` uses a fixed concentration
parameter (`BETA_SKEW_CONCENTRATION = 4.0`), and `t4Sym`/`t4Skew`
use a heuristic scaling factor (`T4_SHAPE_FACTOR`)
to map the distribution.
2. **Econometric Log-Return Estimators (`LogEst`):** Includes a set of
econometric estimators for calculating the volatility (`logStdDev`)
and drift (`logMean`) of logarithmic returns within a single bar.
These are unit-less measures. Models include:
- **Parkinson (1980):** A High-Low range estimator.
- **Garman-Klass (1980):** An OHLC-based estimator.
- **Rogers-Satchell (1991):** An OHLC estimator that accounts
for non-zero drift.
3. **Distribution Analysis (PDF/CDF):** Provides functions to work
with the Probability Density Function (`pricePdf`) and
Cumulative Distribution Function (`priceCdf`) of the
chosen price model.
- **Note on `priceCdf`:** This function uses analytical (exact)
calculations for the `uniform`, `triangular`, and `arcsine`
models. For all other models (e.g., `betaSkew`, `t4Skew`),
it uses **numerical integration (Simpson's rule)** as
an approximation of the cumulative probability.
4. **Mathematical Functions:** The library's Beta distribution
models (`betaSym`, `betaSkew`) are supported by an internal
implementation of the natural log-gamma function, which is
based on the Lanczos approximation.
---
**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.
priceStdDev(estimator, offset)
Estimates **σ̂** (standard deviation) *in price units* for the current
bar, according to the chosen `PriceEst` distribution assumption.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float σ̂ ≥ 0 ; `na` if undefined (e.g. zero range).
priceMean(estimator, offset)
Estimates **μ̂** (mean price) for the chosen `PriceEst` within the
current bar.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float μ̂ in price units.
pricePdf(estimator, price, offset)
Probability-density under the chosen `PriceEst` model.
**Returns 0** when `p` is outside the current bar’s .
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
price (float) : series float Price level to evaluate.
offset (int) : series int To offset the calculated bar
Returns: series float Density value.
priceCdf(estimator, upper, lower, steps, offset)
Cumulative probability **between** `upper` and `lower` under
the chosen `PriceEst` model. Outside-bar regions contribute zero.
Uses a fast, analytical calculation for Uniform, Triangular, and
Arcsine distributions, and defaults to numerical integration
(Simpson's rule) for more complex models.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
upper (float) : series float Upper Integration Boundary.
lower (float) : series float Lower Integration Boundary.
steps (int) : series int # of sub-intervals for numerical integration (if used).
offset (int) : series int To offset the calculated bar.
Returns: series float Probability mass ∈ .
logStdDev(estimator, offset)
Estimates **σ̂** (standard deviation) of *log-returns* for the current bar.
Parameters:
estimator (series LogEst) : series LogEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float σ̂ (unit-less); `na` if undefined.
logMean(estimator, offset)
Estimates μ̂ (mean log-return / drift) for the chosen `LogEst`.
The returned value is consistent with the assumptions of the
selected volatility estimator.
Parameters:
estimator (series LogEst) : series LogEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float μ̂ (unit-less log-return).
LibPvotLibrary "LibPvot"
This is a library for advanced technical analysis, specializing
in two core areas: the detection of price-oscillator
divergences and the analysis of market structure. It provides
a back-end engine for signal detection and a toolkit for
indicator plotting.
Key Features:
1. **Complete Divergence Suite (Class A, B, C):** The engine detects
all three major types of divergences, providing a full spectrum of
analytical signals:
- **Regular (A):** For potential trend reversals.
- **Hidden (B):** For potential trend continuations.
- **Exaggerated (C):** For identifying weakness at double tops/bottoms.
2. **Advanced Signal Filtering:** The detection logic uses a
percentage-based price tolerance (`prcTol`). This feature
enables the practical detection of Exaggerated divergences
(which rarely occur at the exact same price) and creates a
"dead zone" to filter insignificant noise from triggering
Regular divergences.
3. **Pivot Synchronization:** A bar tolerance (`barTol`) is used
to reliably match price and oscillator pivots that do not
align perfectly on the same bar, preventing missed signals.
4. **Signal Invalidation Logic:** Features two built-in invalidation
rules:
- An optional `invalidate` parameter automatically terminates
active divergences if the price or the oscillator breaks
the level of the confirming pivot.
- The engine also discards 'half-pivots' (e.g., a price pivot)
if a corresponding oscillator pivot does not appear within
the `barTol` window.
5. **Stateful Plotting Helpers:** Provides helper functions
(`bullDivPos` and `bearDivPos`) that abstract away the
state management issues of visualizing persistent signals.
They generate gap-free, accurately anchored data series
ready to be used in `plotshape` functions, simplifying
indicator-side code.
6. **Rich Data Output:** The core detection functions (`bullDiv`, `bearDiv`)
return a comprehensive 9-field data tuple. This includes the
boolean flags for each divergence type and the precise
coordinates (price, oscillator value, bar index) of both the
starting and the confirming pivots.
7. **Market Structure & Trend Analysis:** Includes a
`marketStructure` function to automatically identify pivot
highs/lows, classify their relationship (HH, LH, LL, HL),
detect structure breaks, and determine the current trend
state (Up, Down, Neutral) based on pivot sequences.
---
**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.
bullDiv(priceSrc, oscSrc, leftLen, rightLen, depth, barTol, prcTol, persist, invalidate)
Detects bullish divergences (Regular, Hidden, Exaggerated) based on pivot lows.
Parameters:
priceSrc (float) : series float Price series to check for pivots (e.g., `low`).
oscSrc (float) : series float Oscillator series to check for pivots.
leftLen (int) : series int Number of bars to the left of a pivot (default 5).
rightLen (int) : series int Number of bars to the right of a pivot (default 5).
depth (int) : series int Maximum number of stored pivot pairs to check against (default 2).
barTol (int) : series int Maximum bar distance allowed between the price pivot and the oscillator pivot (default 3).
prcTol (float) : series float The percentage tolerance for comparing pivot prices. Used to detect Exaggerated
divergences and filter out market noise (default 0.05%).
persist (bool) : series bool If `true` (default), the divergence flag stays active for the entire duration of the signal.
If `false`, it returns a single-bar pulse on detection.
invalidate (bool) : series bool If `true` (default), terminates an active divergence if price or oscillator break
below the confirming pivot low.
Returns: A tuple containing comprehensive data for a detected bullish divergence.
regBull series bool `true` if a Regular bullish divergence (Class A) is active.
hidBull series bool `true` if a Hidden bullish divergence (Class B) is active.
exgBull series bool `true` if an Exaggerated bullish divergence (Class C) is active.
initPivotPrc series float Price value of the initial (older) pivot low.
initPivotOsz series float Oscillator value of the initial pivot low.
initPivotBar series int Bar index of the initial pivot low.
lastPivotPrc series float Price value of the last (confirming) pivot low.
lastPivotOsz series float Oscillator value of the last pivot low.
lastPivotBar series int Bar index of the last pivot low.
bearDiv(priceSrc, oscSrc, leftLen, rightLen, depth, barTol, prcTol, persist, invalidate)
Detects bearish divergences (Regular, Hidden, Exaggerated) based on pivot highs.
Parameters:
priceSrc (float) : series float Price series to check for pivots (e.g., `high`).
oscSrc (float) : series float Oscillator series to check for pivots.
leftLen (int) : series int Number of bars to the left of a pivot (default 5).
rightLen (int) : series int Number of bars to the right of a pivot (default 5).
depth (int) : series int Maximum number of stored pivot pairs to check against (default 2).
barTol (int) : series int Maximum bar distance allowed between the price pivot and the oscillator pivot (default 3).
prcTol (float) : series float The percentage tolerance for comparing pivot prices. Used to detect Exaggerated
divergences and filter out market noise (default 0.05%).
persist (bool) : series bool If `true` (default), the divergence flag stays active for the entire duration of the signal.
If `false`, it returns a single-bar pulse on detection.
invalidate (bool) : series bool If `true` (default), terminates an active divergence if price or oscillator break
above the confirming pivot high.
Returns: A tuple containing comprehensive data for a detected bearish divergence.
regBear series bool `true` if a Regular bearish divergence (Class A) is active.
hidBear series bool `true` if a Hidden bearish divergence (Class B) is active.
exgBear series bool `true` if an Exaggerated bearish divergence (Class C) is active.
initPivotPrc series float Price value of the initial (older) pivot high.
initPivotOsz series float Oscillator value of the initial pivot high.
initPivotBar series int Bar index of the initial pivot high.
lastPivotPrc series float Price value of the last (confirming) pivot high.
lastPivotOsz series float Oscillator value of the last pivot high.
lastPivotBar series int Bar index of the last pivot high.
bullDivPos(regBull, hidBull, exgBull, rightLen, yPos)
Calculates the plottable data series for bullish divergences. It manages
the complex state of a persistent signal's plotting window to ensure
gap-free and accurately anchored visualization.
Parameters:
regBull (bool) : series bool The regular bullish divergence flag from `bullDiv`.
hidBull (bool) : series bool The hidden bullish divergence flag from `bullDiv`.
exgBull (bool) : series bool The exaggerated bullish divergence flag from `bullDiv`.
rightLen (int) : series int The same `rightLen` value used in `bullDiv` for correct timing.
yPos (float) : series float The series providing the base Y-coordinate for the shapes (e.g., `low`).
Returns: A tuple of three `series float` for plotting bullish divergences.
regBullPosY series float Contains the static anchor Y-value for Regular divergences where a shape should be plotted; `na` otherwise.
hidBullPosY series float Contains the static anchor Y-value for Hidden divergences where a shape should be plotted; `na` otherwise.
exgBullPosY series float Contains the static anchor Y-value for Exaggerated divergences where a shape should be plotted; `na` otherwise.
bearDivPos(regBear, hidBear, exgBear, rightLen, yPos)
Calculates the plottable data series for bearish divergences. It manages
the complex state of a persistent signal's plotting window to ensure
gap-free and accurately anchored visualization.
Parameters:
regBear (bool) : series bool The regular bearish divergence flag from `bearDiv`.
hidBear (bool) : series bool The hidden bearish divergence flag from `bearDiv`.
exgBear (bool) : series bool The exaggerated bearish divergence flag from `bearDiv`.
rightLen (int) : series int The same `rightLen` value used in `bearDiv` for correct timing.
yPos (float) : series float The series providing the base Y-coordinate for the shapes (e.g., `high`).
Returns: A tuple of three `series float` for plotting bearish divergences.
regBearPosY series float Contains the static anchor Y-value for Regular divergences where a shape should be plotted; `na` otherwise.
hidBearPosY series float Contains the static anchor Y-value for Hidden divergences where a shape should be plotted; `na` otherwise.
exgBearPosY series float Contains the static anchor Y-value for Exaggerated divergences where a shape should be plotted; `na` otherwise.
marketStructure(highSrc, lowSrc, leftLen, rightLen, srcTol)
Analyzes the market structure by identifying pivot points, classifying
their sequence (e.g., Higher Highs, Lower Lows), and determining the
prevailing trend state.
Parameters:
highSrc (float) : series float Price series for pivot high detection (e.g., `high`).
lowSrc (float) : series float Price series for pivot low detection (e.g., `low`).
leftLen (int) : series int Number of bars to the left of a pivot (default 5).
rightLen (int) : series int Number of bars to the right of a pivot (default 5).
srcTol (float) : series float Percentage tolerance to consider two pivots as 'equal' (default 0.05%).
Returns: A tuple containing detailed market structure information.
pivType series PivType The type of the most recently formed pivot (e.g., `hh`, `ll`).
lastPivHi series float The price level of the last confirmed pivot high.
lastPivLo series float The price level of the last confirmed pivot low.
lastPiv series float The price level of the last confirmed pivot (either high or low).
pivHiBroken series bool `true` if the price has broken above the last pivot high.
pivLoBroken series bool `true` if the price has broken below the last pivot low.
trendState series TrendState The current trend state (`up`, `down`, or `neutral`).
