CandlestickUtilitiesThis library provides essential functions for candlestick chart analysis and pattern recognition in Pine Script®.
It includes:
• Candle structure analysis (bodies, shadows, lengths)
• Trend detection using EMAs
• Common candlestick pattern recognition
This library is under construction.
Designed to support strategy development and improve signal accuracy for traders.
Created by @xprophetx — under MPL-2.0 license.
Techindicator
centered_rsiLibrary "centered_rsi"
This script defines a custom "Centered RSI" indicator. It calculates the RSI based on the deviation of price from a selected moving average (SMA, EMA, HMA, or ALMA). The MA smooths the price, and the RSI is applied to the centered values (price minus MA), aiming to highlight momentum relative to trend. The indicator includes user inputs for MA type and lengths, and plots the RSI along with reference lines at 30, 50, and 70.
ma(source, length, ma_type, offset, sigma)
Parameters:
source (float)
length (simple int)
ma_type (string)
offset (simple float)
sigma (simple float)
centered_rsi(source, type_ma, length_ma, length_rsi)
Parameters:
source (float)
type_ma (string)
length_ma (simple int)
length_rsi (simple int)
MAEngineLibLibrary "MAEngineLib"
ma_sma(source, length)
Parameters:
source (float)
length (int)
ma_ema(source, length)
Parameters:
source (float)
length (simple int)
ma_dema(source, length)
Parameters:
source (float)
length (simple int)
ma_tema(source, length)
Parameters:
source (float)
length (simple int)
ma_wma(source, length)
Parameters:
source (float)
length (int)
ma_hma(source, length)
Parameters:
source (float)
length (simple int)
ma_vwma(source, length)
Parameters:
source (float)
length (int)
ma_kijun(length)
Parameters:
length (int)
ma_alma(source, length)
Parameters:
source (float)
length (int)
ma_kama(source, length)
Parameters:
source (float)
length (int)
ma_hullmod(source, length)
Parameters:
source (float)
length (int)
selectMA(type, source, length)
Parameters:
type (string)
source (float)
length (simple int)
tafirstlibGeneral Purpose: Starts by stating it's a collection of utility functions for technical analysis.
Core Functionality Areas: Mentions key categories like:
Extrema detection (isMin, isMax, etc.)
Condition checking over time (isMachedInRange, isContinuous, etc.)
Rate of change analysis (isSlowDown)
Moving average calculation (getMA)
Advanced Features: Highlights the more complex functions:
Visualization helpers (getColorNew)
Moving Regression (mr) for smoothing/trend
Cycle analysis (bpDom)
Overall Goal: Concludes by stating the library's aim – simplifying development and enabling complex analysis.
Library "tafirstlib"
TODO: add library description here
isSlowDown(data)
isSlowDown
Parameters:
data (float) : array of numbers
Returns: boolean
isMin(data, maeLength)
isMin
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMax(data, maeLength)
isMax
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMinStopped(data, maeLength)
isMinStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isMaxStopped(data, maeLength)
isMaxStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
Returns: boolean
isLongMinStopped(data, maeLength, distance)
isLongMinStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
distance (int) : number
Returns: boolean
isLongMaxStopped(data, maeLength, distance)
isLongMaxStopped
Parameters:
data (float) : array of numbers
maeLength (int) : number
distance (int) : number
Returns: boolean
isMachedInRangeSkipCurrent(data, findRange, findValue)
isMachedInRangeSkipCurrent
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: boolean
isMachedInRange(data, findRange, findValue)
isMachedInRange
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: boolean
isMachedColorInRange(data, findRange, findValue)
isMachedColorInRange isMachedColorInRange(series color data, int findRange, color findValue)
Parameters:
data (color) : series of color
findRange (int) : int
findValue (color) : color
Returns: boolean
countMachedInRange(data, findRange, findValue)
countMachedInRange
Parameters:
data (bool) : array of numbers
findRange (int) : number
findValue (bool) : number
Returns: number
getColor(data)
getColor
Parameters:
data (float) : array of numbers
Returns: color
getColorNew(data)
getColorNew
Parameters:
data (float) : array of numbers
Returns: color
isColorBetter(color_data)
isColorBetter
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorWorst(color_data)
isColorWorst
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorBetter2(color_data)
isColorBetter2
Parameters:
color_data (color) : array of colors
Returns: boolean
isColorWorst2(color_data)
isColorWorst2
Parameters:
color_data (color) : array of colors
Returns: boolean
isDecreased2Bar(data)
isDecreased2Bar
Parameters:
data (float) : array of numbers
Returns: boolean
isContinuousAdvance(targetSeries, range2Find, howManyException)
isContinuousAdvance
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
howManyException (int) : number
Returns: boolean
isContinuous(targetSeries, range2Find, truefalse)
isContinuous
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
isContinuousNotNow(targetSeries, range2Find, truefalse)
isContinuousNotNow
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
isContinuousTwoFactors(targetSeries, range2Find, truefalse)
isContinuousTwoFactors
Parameters:
targetSeries (bool) : array of booleans
range2Find (int) : number
truefalse (bool) : boolean
Returns: boolean
findEventInRange(startDataBarIndex, neededDataBarIndex, currentBarIndex)
findEventInRange
Parameters:
startDataBarIndex (int) : number
neededDataBarIndex (int) : number
currentBarIndex (int) : number
Returns: boolean
findMin(firstdata, secondata, thirddata, forthdata)
findMin
Parameters:
firstdata (float) : number
secondata (float) : number
thirddata (float) : number
forthdata (float) : number
Returns: number
findMax(firstdata, secondata, thirddata, forthdata)
findMax
Parameters:
firstdata (float) : number
secondata (float) : number
thirddata (float) : number
forthdata (float) : number
Returns: number
getMA(src, length, mav)
getMA
Parameters:
src (float) : number
length (simple int) : number
mav (string) : string
Returns: number
mr(mrb_src, mrb_window, mrb_degree)
Parameters:
mrb_src (float)
mrb_window (int)
mrb_degree (int)
bpDom(maeLength, bpw, mult)
Parameters:
maeLength (int)
bpw (float)
mult (float)
CSCMultiTimeframeToolsLibrary "CSCMultiTimeframeTools"
Calculates instant higher timeframe values for higher timeframe analysis with zero lag.
getAdjustedLookback(current_tf_minutes, higher_tf_minutes, length)
Calculate adjusted lookback period for higher timeframe conversion.
Parameters:
current_tf_minutes (int) : Current chart timeframe in minutes (e.g., 5 for 5m).
higher_tf_minutes (int) : Target higher timeframe in minutes (e.g., 15 for 15m).
length (int) : Base length value (e.g., 14 for RSI/MFI).
Returns: Adjusted lookback period (length × multiplier).
Purpose and Benefits of the TimeframeTools Library
This library is designed to solve a critical pain point for traders who rely on higher timeframe (HTF) indicator values while analyzing lower timeframe (LTF) charts. Traditional methods require waiting for multiple candles to close—for example, to see a 1-hour RSI on a 5-minute chart, you’d need 12 closed candles (5m × 12 = 60m) before the value updates. This lag means missed opportunities, delayed signals, and inefficient decision-making.
Why Traders Need This
Whether you’re scalping (5M/15M) or swing trading (1H/4H), this library bridges the gap between timeframes, giving you HTF context in real time—so you can act faster, with confidence.
How This Library Eliminates the Waiting Game
By dynamically calculating the adjusted lookback period, the library allows:
Real-time HTF values on LTF charts – No waiting for candle closes.
Accurate conversions – A 14-period RSI on a 1-hour chart translates to 168 periods (14 × 12) on a 5-minute chart, ensuring mathematical precision.
Flexible application – Works with common indicators like RSI, MFI, CCI, and moving averages (though confirmations should be done before publishing under your own secondary use).
Key Advantages Over Manual Methods
Speed: Instantly reflects HTF values without waiting for candle resolutions.
Adaptability: Adjusts automatically if the user changes timeframes or lengths.
Consistency: Removes human error in manual period calculations.
Limitations to Note
Not a magic bullet – While it solves the lag issue, traders should still:
Validate signals with price action or additional confirmations.
Be mindful of extreme lookback lengths (e.g., a 200-period daily SMA on a 1-minute chart requires 28,800 periods, which may strain performance).
PineVersatilitiesBundleLibrary "PineVersatilitiesBundle"
Versatilities (aka, Versatile Utilities) Pack includes:
- Eighteen Price Variants bundled in a Map,
- Nine Smoothing Variants bundled in a Map,
- Visualisations that indicate on both - pane and chart.
price_variants(lb)
Computes Several different averages using current and previous OHLC values
Parameters:
lb (int) : - lookback distance for combining OHLC values from the past with the present
Returns: Map of Eighteen Uncommon Combinations of single and two-bar OHLC averages (rounded-to-mintick)
dynamic_MA(masrc, malen, lsmaoff, almasgm, almaoff, almaflr)
Dynamically computes Eight different MAs and returns a Map containing Nine MAs
Parameters:
masrc (float) : source series to compute MA
malen (simple int) : lookback distance for MA
lsmaoff (simple int) : optional LSMA offset - default is 0
almasgm (simple float) : optional ALMA sigma - default is 5
almaoff (simple float) : optional ALMA offset - default is 0.5
almaflr (simple bool) : optional ALMA floor flag - default is false
Returns: Map of MAs - 'ALMA', 'EMA', 'HMA', 'LSMA', 'RMA', 'SMA', 'SWMA', 'WMA', 'ALL' (rounded-to-mintick)
PivotLabelsLibrary "PivotLabels"
drawPivots(qtyLabels, leftLegs, rightLegs)
Displays a label for each of the last `qtyLabels` pivots.
Colors high pivots in green, low pivots in red, and breached pivots in gray.
Parameters:
qtyLabels (int) : (simple int) Quantity of last labels to display.
leftLegs (int) : (simple int) Left pivot legs.
rightLegs (int) : (simple int) Right pivot legs.
Returns: Nothing.
bollingerBandsV2Library "bollingerBandsV2"
Bollinger bands related functions
get_multiple_bollinger_bands(stdv1, stdv2, stdv3, stdv4, stdv5, stdv6, stdv7, length, source)
: Calculates 7 sets of bollinger bands, with 7 different standard deviations
Parameters:
stdv1 (float) : (simple int): standard deviation 1
stdv2 (float) : (simple int): standard deviation 2
stdv3 (float) : (simple int): standard deviation 3
stdv4 (float) : (simple int): standard deviation 4
stdv5 (float) : (simple int): standard deviation 5
stdv6 (float) : (simple int): standard deviation 6
stdv7 (float) : (simple int): standard deviation 7
length (simple int) : (simple int): Length for the bands
source (float) : (simple float): source for the calculation
Returns: : Returns 8 levels plus the range of all the levels.
get_bb_volatility(bb_highest, bb_lowest, ma_length, lookback)
: Provides a volatility indicator based on Bollinger Bands, and indicates wheather the volatility is increasing or decreasing.
Parameters:
bb_highest (float) : (simple float): Top Bollinger Band on which to calculate the range.
bb_lowest (float) : (simple float): Bottom Bollinger Band on which to calculate the range.
ma_length (simple int) : (simple int): Length to use in the smoothing of Bollinger Bands range.
lookback (int) : (simple int): Lookback period to identify a change in the Bollinger Bands range.
Returns: : Returns 8 levels plus the range of all the levels.
get_bbVolatility_data(source, length, stdv1, stdv2, stdv3, stdv4, stdv5, stdv6, stdv7, trend_direction)
: Generates Bollinger Bands Volatility
Parameters:
source (float) : (float): Source for Bollinger Bands
length (simple int) : (int): Length for Bollinger Bands
stdv1 (int) : (int): Standard Deviation 1
stdv2 (int) : (int): Standard Deviation 2
stdv3 (int) : (int): Standard Deviation 3
stdv4 (int) : (int): Standard Deviation 4
stdv5 (int) : (int): Standard Deviation 5
stdv6 (int) : (int): Standard Deviation 6
stdv7 (int) : (int): Standard Deviation 7
trend_direction (string) : (string): Current direction of the trend
Returns: : Returns a map with the levels, plus direction flag and the data table
ZigZag█ Overview
This Pine Script™ library provides a comprehensive implementation of the ZigZag indicator using advanced object-oriented programming techniques. It serves as a developer resource rather than a standalone indicator, enabling Pine Script™ programmers to incorporate sophisticated ZigZag calculations into their own scripts.
Pine Script™ libraries contain reusable code that can be imported into indicators, strategies, and other libraries. For more information, consult the Libraries section of the Pine Script™ User Manual.
█ About the Original
This library is based on TradingView's official ZigZag implementation .
The original code provides a solid foundation with user-defined types and methods for calculating ZigZag pivot points.
█ What is ZigZag?
The ZigZag indicator filters out minor price movements to highlight significant market trends.
It works by:
1. Identifying significant pivot points (local highs and lows)
2. Connecting these points with straight lines
3. Ignoring smaller price movements that fall below a specified threshold
Traders typically use ZigZag for:
- Trend confirmation
- Identifying support and resistance levels
- Pattern recognition (such as Elliott Waves)
- Filtering out market noise
The algorithm identifies pivot points by analyzing price action over a specified number of bars, then only changes direction when price movement exceeds a user-defined percentage threshold.
█ My Enhancements
This modified version extends the original library with several key improvements:
1. Support and Resistance Visualization
- Adds horizontal lines at pivot points
- Customizable line length (offset from pivot)
- Adjustable line width and color
- Option to extend lines to the right edge of the chart
2. Support and Resistance Zones
- Creates semi-transparent zone areas around pivot points
- Customizable width for better visibility of important price levels
- Separate colors for support (lows) and resistance (highs)
- Visual representation of price areas rather than just single lines
3. Zig Zag Lines
- Separate colors for upward and downward ZigZag movements
- Visually distinguishes between bullish and bearish price swings
- Customizable colors for text
- Width customization
4. Enhanced Settings Structure
- Added new fields to the Settings type to support the additional features
- Extended Pivot type with supportResistance and supportResistanceZone fields
- Comprehensive configuration options for visual elements
These enhancements make the ZigZag more useful for technical analysis by clearly highlighting support/resistance levels and zones, and providing clearer visual cues about market direction.
█ Technical Implementation
This library leverages Pine Script™'s user-defined types (UDTs) to create a robust object-oriented architecture:
- Settings : Stores configuration parameters for calculation and display
- Pivot : Represents pivot points with their visual elements and properties
- ZigZag : Manages the overall state and behavior of the indicator
The implementation follows best practices from the Pine Script™ User Manual's Style Guide and uses advanced language features like methods and object references. These UDTs represent Pine Script™'s most advanced feature set, enabling sophisticated data structures and improved code organization.
For newcomers to Pine Script™, it's recommended to understand the language fundamentals before working with the UDT implementation in this library.
█ Usage Example
//@version=6
indicator("ZigZag Example", overlay = true, shorttitle = 'ZZA', max_bars_back = 5000, max_lines_count = 500, max_labels_count = 500, max_boxes_count = 500)
import andre_007/ZigZag/1 as ZIG
var group_1 = "ZigZag Settings"
//@variable Draw Zig Zag on the chart.
bool showZigZag = input.bool(true, "Show Zig-Zag Lines", group = group_1, tooltip = "If checked, the Zig Zag will be drawn on the chart.", inline = "1")
// @variable The deviation percentage from the last local high or low required to form a new Zig Zag point.
float deviationInput = input.float(5.0, "Deviation (%)", minval = 0.00001, maxval = 100.0,
tooltip = "The minimum percentage deviation from a previous pivot point required to change the Zig Zag's direction.", group = group_1, inline = "2")
// @variable The number of bars required for pivot detection.
int depthInput = input.int(10, "Depth", minval = 1, tooltip = "The number of bars required for pivot point detection.", group = group_1, inline = "3")
// @variable registerPivot (series bool) Optional. If `true`, the function compares a detected pivot
// point's coordinates to the latest `Pivot` object's `end` chart point, then
// updates the latest `Pivot` instance or adds a new instance to the `ZigZag`
// object's `pivots` array. If `false`, it does not modify the `ZigZag` object's
// data. The default is `true`.
bool allowZigZagOnOneBarInput = input.bool(true, "Allow Zig Zag on One Bar", tooltip = "If checked, the Zig Zag calculation can register a pivot high and pivot low on the same bar.",
group = group_1, inline = "allowZigZagOnOneBar")
var group_2 = "Display Settings"
// @variable The color of the Zig Zag's lines (up).
color lineColorUpInput = input.color(color.green, "Line Colors for Up/Down", group = group_2, inline = "4")
// @variable The color of the Zig Zag's lines (down).
color lineColorDownInput = input.color(color.red, "", group = group_2, inline = "4",
tooltip = "The color of the Zig Zag's lines")
// @variable The width of the Zig Zag's lines.
int lineWidthInput = input.int(1, "Line Width", minval = 1, tooltip = "The width of the Zig Zag's lines.", group = group_2, inline = "w")
// @variable If `true`, the Zig Zag will also display a line connecting the last known pivot to the current `close`.
bool extendInput = input.bool(true, "Extend to Last Bar", tooltip = "If checked, the last pivot will be connected to the current close.",
group = group_1, inline = "5")
// @variable If `true`, the pivot labels will display their price values.
bool showPriceInput = input.bool(true, "Display Reversal Price",
tooltip = "If checked, the pivot labels will display their price values.", group = group_2, inline = "6")
// @variable If `true`, each pivot label will display the volume accumulated since the previous pivot.
bool showVolInput = input.bool(true, "Display Cumulative Volume",
tooltip = "If checked, the pivot labels will display the volume accumulated since the previous pivot.", group = group_2, inline = "7")
// @variable If `true`, each pivot label will display the change in price from the previous pivot.
bool showChgInput = input.bool(true, "Display Reversal Price Change",
tooltip = "If checked, the pivot labels will display the change in price from the previous pivot.", group = group_2, inline = "8")
// @variable Controls whether the labels show price changes as raw values or percentages when `showChgInput` is `true`.
string priceDiffInput = input.string("Absolute", "", options = ,
tooltip = "Controls whether the labels show price changes as raw values or percentages when 'Display Reversal Price Change' is checked.",
group = group_2, inline = "8")
// @variable If `true`, the Zig Zag will display support and resistance lines.
bool showSupportResistanceInput = input.bool(true, "Show Support/Resistance Lines",
tooltip = "If checked, the Zig Zag will display support and resistance lines.", group = group_2, inline = "9")
// @variable The number of bars to extend the support and resistance lines from the last pivot point.
int supportResistanceOffsetInput = input.int(50, "Support/Resistance Offset", minval = 0,
tooltip = "The number of bars to extend the support and resistance lines from the last pivot point.", group = group_2, inline = "10")
// @variable The width of the support and resistance lines.
int supportResistanceWidthInput = input.int(1, "Support/Resistance Width", minval = 1,
tooltip = "The width of the support and resistance lines.", group = group_2, inline = "11")
// @variable The color of the support lines.
color supportColorInput = input.color(color.red, "Support/Resistance Color", group = group_2, inline = "12")
// @variable The color of the resistance lines.
color resistanceColorInput = input.color(color.green, "", group = group_2, inline = "12",
tooltip = "The color of the support/resistance lines.")
// @variable If `true`, the support and resistance lines will be drawn as zones.
bool showSupportResistanceZoneInput = input.bool(true, "Show Support/Resistance Zones",
tooltip = "If checked, the support and resistance lines will be drawn as zones.", group = group_2, inline = "12-1")
// @variable The color of the support zones.
color supportZoneColorInput = input.color(color.new(color.red, 70), "Support Zone Color", group = group_2, inline = "12-2")
// @variable The color of the resistance zones.
color resistanceZoneColorInput = input.color(color.new(color.green, 70), "", group = group_2, inline = "12-2",
tooltip = "The color of the support/resistance zones.")
// @variable The width of the support and resistance zones.
int supportResistanceZoneWidthInput = input.int(10, "Support/Resistance Zone Width", minval = 1,
tooltip = "The width of the support and resistance zones.", group = group_2, inline = "12-3")
// @variable If `true`, the support and resistance lines will extend to the right of the chart.
bool supportResistanceExtendInput = input.bool(false, "Extend to Right",
tooltip = "If checked, the lines will extend to the right of the chart.", group = group_2, inline = "13")
// @variable References a `Settings` instance that defines the `ZigZag` object's calculation and display properties.
var ZIG.Settings settings =
ZIG.Settings.new(
devThreshold = deviationInput,
depth = depthInput,
lineColorUp = lineColorUpInput,
lineColorDown = lineColorDownInput,
textUpColor = lineColorUpInput,
textDownColor = lineColorDownInput,
lineWidth = lineWidthInput,
extendLast = extendInput,
displayReversalPrice = showPriceInput,
displayCumulativeVolume = showVolInput,
displayReversalPriceChange = showChgInput,
differencePriceMode = priceDiffInput,
draw = showZigZag,
allowZigZagOnOneBar = allowZigZagOnOneBarInput,
drawSupportResistance = showSupportResistanceInput,
supportResistanceOffset = supportResistanceOffsetInput,
supportResistanceWidth = supportResistanceWidthInput,
supportColor = supportColorInput,
resistanceColor = resistanceColorInput,
supportResistanceExtend = supportResistanceExtendInput,
supportResistanceZoneWidth = supportResistanceZoneWidthInput,
drawSupportResistanceZone = showSupportResistanceZoneInput,
supportZoneColor = supportZoneColorInput,
resistanceZoneColor = resistanceZoneColorInput
)
// @variable References a `ZigZag` object created using the `settings`.
var ZIG.ZigZag zigZag = ZIG.newInstance(settings)
// Update the `zigZag` on every bar.
zigZag.update()
//#endregion
The example code demonstrates how to create a ZigZag indicator with customizable settings. It:
1. Creates a Settings object with user-defined parameters
2. Instantiates a ZigZag object using these settings
3. Updates the ZigZag on each bar to detect new pivot points
4. Automatically draws lines and labels when pivots are detected
This approach provides maximum flexibility while maintaining readability and ease of use.
LinearRegressionLibrary "LinearRegression"
Calculates a variety of linear regression and deviation types, with optional emphasis weighting. Additionally, multiple of slope and Pearson’s R calculations.
calcSlope(_src, _len, _condition)
Calculates the slope of a linear regression over the specified length.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The slope of the linear regression.
calcReg(_src, _len, _condition)
Calculates a basic linear regression, returning y1, y2, slope, and average.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) An array of 4 values: .
calcRegStandard(_src, _len, _emphasis, _condition)
Calculates an Standard linear regression with optional emphasis.
Parameters:
_src (float) : (series float) The source data series.
_len (int) : (int) The length of the lookback period.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegRidge(_src, _len, lambda, _emphasis, _condition)
Calculates a ridge regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The ridge regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLasso(_src, _len, lambda, _emphasis, _condition)
Calculates a Lasso regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The Lasso regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcElasticNetLinReg(_src, _len, lambda1, lambda2, _emphasis, _condition)
Calculates an Elastic Net regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda1 (float) : (float) L1 regularization parameter (Lasso).
lambda2 (float) : (float) L2 regularization parameter (Ridge).
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegHuber(_src, _len, delta, iterations, _emphasis, _condition)
Calculates a Huber regression using Iteratively Reweighted Least Squares (IRLS).
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
delta (float) : (float) Huber threshold parameter.
iterations (int) : (int) Number of IRLS iterations.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLAD(_src, _len, iterations, _emphasis, _condition)
Calculates a Least Absolute Deviations (LAD) regression via IRLS.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
iterations (int) : (int) Number of IRLS iterations for LAD.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegBayesian(_src, _len, priorMean, priorSpan, sigma, _emphasis, _condition)
Calculates a Bayesian linear regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
priorMean (float) : (float) The prior mean for the slope.
priorSpan (float) : (float) The prior variance (or span) for the slope.
sigma (float) : (float) The assumed standard deviation of residuals.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRFromLinReg(_src, _len, _slope, _average, _y1, _condition)
Calculates the Pearson correlation coefficient (R) based on linear regression parameters.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_average (float) : (float) The average value of the source data series.
_y1 (float) : (float) The starting point (y-intercept of the oldest bar) for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The Pearson correlation coefficient (R) adjusted for the direction of the slope.
calcRFromSource(_src, _len, _condition)
Calculates the correlation coefficient (R) using a specified length and source data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The correlation coefficient (R).
calcSlopeLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is flattest (closest to zero).
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length to consider (minimum of 2).
_minLen (int) : (int) The minimum length to start from (cannot exceed the max length).
_step (int) : (int) The increment step for lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is flattest.
calcSlopeLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is highest.
calcSlopeLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is lowest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is lowest.
calcSlopeLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length at which the absolute slope value is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the absolute slope value is highest.
calcRLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest absolute R value.
calcRLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest R value.
calcRLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest R value.
calcRLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest absolute R value.
calcDevReverse(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the regressive linear deviation in reverse order, with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevForward(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the progressive linear deviation in forward order (oldest to most recent bar), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is oldest and _src is most recent.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the most recent bar, adjusted by slope).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevBalanced(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the balanced linear deviation with optional emphasis on recent or older data.
Parameters:
_src (float) : (float) Source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the oldest bar).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMean(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the mean absolute deviation from a forward-applied linear trend (oldest to most recent), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMedian(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the median absolute deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data array (index 0 = oldest, index _len - 1 = most recent).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns:
calcDevPercent(_y1, _inputDev, _condition)
Calculates the percent deviation from a given value and a specified percentage.
Parameters:
_y1 (float) : (float) The base value from which to calculate deviation.
_inputDev (float) : (float) The deviation percentage.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevFitted(_len, _slope, _y1, _emphasis, _condition)
Calculates the weighted fitted deviation based on high and low series data, showing max deviation, with optional emphasis.
Parameters:
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevATR(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates an ATR-style deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data (typically close).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcPricePositionPercent(_top, _bot, _src)
Calculates the percent position of a price within a linear regression channel. Top=100%, Bottom=0%.
Parameters:
_top (float) : (float) The top (positive) deviation, corresponding to 100%.
_bot (float) : (float) The bottom (negative) deviation, corresponding to 0%.
_src (float) : (float) The source price.
Returns: (float) The percent position within the channel.
plotLinReg(_len, _y1, _y2, _slope, _devTop, _devBot, _scaleTypeLog, _lineWidth, _extendLines, _channelStyle, _colorFill, _colUpLine, _colDnLine, _colUpFill, _colDnFill)
Plots the linear regression line and its deviations, with configurable styles and fill.
Parameters:
_len (int) : (int) The lookback period for the linear regression.
_y1 (float) : (float) The starting y-value of the regression line.
_y2 (float) : (float) The ending y-value of the regression line.
_slope (float) : (float) The slope of the regression line (used to determine line color).
_devTop (float) : (float) The top deviation to add to the line.
_devBot (float) : (float) The bottom deviation to subtract from the line.
_scaleTypeLog (bool) : (bool) Use a log scale if true; otherwise, linear scale.
_lineWidth (int) : (int) The width of the plotted lines.
_extendLines (string) : (string) How lines should extend (none, left, right, both).
_channelStyle (string) : (string) The style of the channel lines (solid, dashed, dotted).
_colorFill (bool) : (bool) Whether to fill the space between the top and bottom deviation lines.
_colUpLine (color) : (color) Line color when slope is positive.
_colDnLine (color) : (color) Line color when slope is negative.
_colUpFill (color) : (color) Fill color when slope is positive.
_colDnFill (color) : (color) Fill color when slope is negative.
ootaLibrary "oota"
Collection of all custom and enhanced TA indicators - Object oriented methods implementation
method tr(c, useTrueRange)
returns true range of the candle
Namespace types: Candle
Parameters:
c (Candle) : Candle object containing ohlc data
useTrueRange (bool) : Use true range for atr calculation instead of just high/low difference
method ma(maType, length, source)
returns custom moving averages
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
source (float) : Moving Average Source
Returns: moving average for the given type and length
method atr(maType, length, useTrueRange, c)
returns ATR with custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
useTrueRange (bool) : Use true range for atr calculation instead of just high/low difference
c (Candle) : Candle object containing ohlc
Returns: ATR for the given moving average type and length
method atrpercent(maType, length, useTrueRange, c)
returns ATR as percentage of close price
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
useTrueRange (bool) : Use true range for atr calculation instead of just high/low difference
c (Candle) : Candle object containing ohlc
Returns: ATR as percentage of close price for the given moving average type and length
method bb(maType, length, multiplier, sticky, c)
returns Bollinger band for custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Bollinger band with custom moving average for given source, length and multiplier
method bbw(maType, length, multiplier, sticky, c)
returns Bollinger bandwidth for custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
sticky (simple bool) : sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Bollinger Bandwidth for custom moving average for given source, length and multiplier
method bpercentb(maType, length, multiplier, sticky, c)
returns Bollinger Percent B for custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Bollinger Percent B for custom moving average for given source, length and multiplier
method kc(maType, length, multiplier, useTrueRange, sticky, c)
returns Keltner Channel for custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
useTrueRange (simple bool) : - if set to false, uses high-low.
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Keltner Channel for custom moving average for given souce, length and multiplier
method kcw(maType, length, multiplier, useTrueRange, sticky, c)
returns Keltner Channel Width with custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
useTrueRange (simple bool) : - if set to false, uses high-low.
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Keltner Channel Width for custom moving average
method kpercentk(maType, length, multiplier, useTrueRange, sticky, c)
returns Keltner Channel Percent K Width with custom moving average
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom series type
length (simple int) : Moving Average Length
multiplier (float) : Standard Deviation multiplier
useTrueRange (simple bool) : - if set to false, uses high-low.
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
c (Candle) : Candle object containing ohlc
Returns: Keltner Percent K for given moving average, source, length and multiplier
method dc(c, length, sticky)
returns Custom Donchian Channel
Namespace types: Candle
Parameters:
c (Candle) : Candle object containing ohlc
length (simple int) : - donchian channel length
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
Returns: Donchian channel
method dcw(c, length, sticky)
returns Donchian Channel Width
Namespace types: Candle
Parameters:
c (Candle) : Candle object containing ohlc
length (simple int) : - donchian channel length
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
Returns: Donchian channel width
method dpercentd(c, length, sticky)
returns Donchian Channel Percent of price
Namespace types: Candle
Parameters:
c (Candle) : Candle object containing ohlc
length (simple int) : - donchian channel length
sticky (simple bool) : - sticky boundaries which will only change when value is outside boundary.
Returns: Donchian channel Percent D
method supertrend(maType, length, multiplier, useTrueRange, waitForClose, delayed, c)
supertrend Simple supertrend based on atr but also takes into consideration of custom MA Type, sources
Namespace types: simple CustomSeries
Parameters:
maType (simple CustomSeries) : Custom Series
length (simple int) : ATR Length
multiplier (simple float) : ATR Multiplier
useTrueRange (simple bool) : - if set to false, uses high-low.
waitForClose (simple bool) : : Considers source for direction change crossover if checked. Else, uses highSource and lowSource.
delayed (simple bool) : : if set to true lags supertrend atr stop based on target levels.
c (Candle) : Candle object containing ohlc
Returns: dir : Supertrend direction
supertrend : BuyStop if direction is 1 else SellStop
method oscillatorRange(seriesType, source, highlowLength, rangeLength, sticky)
oscillatorRange - returns Custom overbought/oversold areas for an oscillator input
Namespace types: simple CustomSeries
Parameters:
seriesType (simple CustomSeries) : - Custom series type
source (float) : - Osillator source such as RSI, COG etc.
highlowLength (simple int) : - length on which highlow of the oscillator is calculated
rangeLength (simple int) : - length used for calculating oversold/overbought range - usually same as oscillator length
sticky (simple bool) : - overbought, oversold levels won't change unless crossed
Returns: Dynamic overbought and oversold range for oscillator input
method oscillator(oscillatorType, length, shortLength, longLength, c)
oscillator - returns Choice of oscillator with custom overbought/oversold range
Namespace types: simple OscillatorType
Parameters:
oscillatorType (simple OscillatorType) : OscillatorType object
length (simple int) : - Oscillator length - not used for TSI
shortLength (simple int) : - shortLength only used for TSI
longLength (simple int) : - longLength only used for TSI
c (Candle) : Candle object containing ohlc
Returns: Oscillator value
method oscillatorWithRange(oscillatorType, length, shortLength, longLength, seriesType, highlowLength, sticky, c)
oscillatorWithRange - returns Choice of oscillator with custom overbought/oversold range
Namespace types: simple OscillatorType
Parameters:
oscillatorType (simple OscillatorType) : OscillatorType object
length (simple int) : - Oscillator length - not used for TSI
shortLength (simple int) : - shortLength only used for TSI
longLength (simple int) : - longLength only used for TSI
seriesType (simple CustomSeries) : - CustomSeries enum type
highlowLength (simple int) : - length on which highlow of the oscillator is calculated
sticky (simple bool) : - overbought, oversold levels won't change unless crossed
c (Candle) : Candle object containing ohlc
Returns: Oscillator value along with dynamic overbought and oversold range for oscillator input
Candle
Custom candle object
Fields:
o (series float) : open
h (series float) : high
l (series float) : low
c (series float) : close
barindex (series int) : bar_index
bartime (series int) : time
bartimeclose (series int) : time_close
v (series float) : volume
regressionUtilitiesLibrary "regressionUtilities"
get_linear_regression(bar_index_array, prices_array, stdDev_mult)
: Generates the linear regression channel for an array of values.
Parameters:
bar_index_array (array) : (array): Array with bar indexes
prices_array (array) : (array): Array with prices
stdDev_mult (float) : (float): Standard deviation multiple for the channels
Returns: : Returns x1, x2, y1_mid, y2_mid, y1_up, y2_up, y1_dn, y2_dn, m, b, R2, stdDev
get_optimal_linearRegression_channel(max_length, min_length, source, stdDev_mult, show_data_table, tableYpos, tableXpos, table_textSize, barsToRight, plot_labels, include_levels)
: Gets the best fitting linear regression using optimum length
Parameters:
max_length (int) : (int): Maximum bar length
min_length (int) : (int): Minimum bar length
source (float) : (float): Source for the regression
stdDev_mult (float) : (float): Array with prices
show_data_table (bool) : (bool): Activates and shows the data table
tableYpos (string)
tableXpos (string)
table_textSize (string)
barsToRight (int)
plot_labels (bool)
include_levels (bool)
Returns: : Returns three line objects that conform the regression channel, plus the optimal length and maximum r2
get_regressionChannel_data(max_length, min_length, source, stdDev_mult, plot_linearRegression, plot_labels, include_levels, barsToRight)
: Gets data for the linear regression channel
Parameters:
max_length (int) : (int): Maximum length for the linear regression.
min_length (int) : (int): Minimum length for the linear regression.
source (float) : (float): Source for the linear regression
stdDev_mult (float) : (float): Multiple for the standar deviations for the linear regression channel.
plot_linearRegression (bool)
plot_labels (bool)
include_levels (bool)
barsToRight (int)
Returns: : Returns a maps with the regression levels, the direction flag and the datatable map.
get_regressionChannel_data_v2(max_length, min_length, source, stdDev_mult, plot_linearRegression, plot_labels, include_levels, barsToRight)
Parameters:
max_length (int)
min_length (int)
source (float)
stdDev_mult (float)
plot_linearRegression (bool)
plot_labels (bool)
include_levels (bool)
barsToRight (int)
get_cuadratic_regression(x_array, y_array, bars_to_project, max_length)
: Gets the best fitting linear regression using optimum length
Parameters:
x_array (array) : (array): Maximum bar length
y_array (array) : (array): Minimum bar length
bars_to_project (int) : (int): Array with prices
max_length (int)
Returns: : Returns three line objects
rate_of_changeLibrary "rate_of_change"
// @description: Applies ROC algorithm to any pair of values.
// This library function is used to scale change of value (price, volume) to a percentage value, just as the ROC indicator would do. It is good practice to scale arbitrary ranges to set boundaries when you try to train statistical model.
rateOfChange(value, base, hardlimit)
This function is a helper to scale a value change to its percentage value.
Parameters:
value (float)
base (float)
hardlimit (int)
Returns: per: A float comprised between 0 and 100
TALibrary "TA"
Technical analysis library that provides convenience functions and overrides for tradingview's own ta.* functions in order to work around various limitations.
sma(src, length)
Override for ta.sma that allows you to provide series values
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
Returns: float The moving average
ema(src, length)
Override for ta.ema that allows you to provide series values
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
Returns: float The moving average
rma(src, length)
Override for ta.rma that allows you to provide series values
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
Returns: float The moving average
wma(src, length)
Override for ta.wma that allows you to provide series values
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
Returns: float The moving average
vwma(src, length)
Override for ta.vwma that allows you to provide series values
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
Returns: float The moving average
ma(src, length, maType)
Provide the requested moving average
Parameters:
src (float) : The source values to process
length (int) : The length of the source to evaluate for the moving average
maType (string) : The type of moving average to return: SMA (default), EMA, SMMA (RMA), WMA, VWMA
Returns: float The moving average
Cometreon_Public📚 Cometreon Public Library – Advanced Functions for Pine Script
This library contains advanced functions used in my public indicators on TradingView. The goal is to make the code more modular and efficient, allowing users to call pre-built functions for complex calculations without rewriting them from scratch.
🔹 Currently Available Functions:
1️⃣ Moving Average Function – Provides multiple types of moving averages to choose from, including:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
RMA (Smoothed Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
LSMA (Least Squares Moving Average)
VWMA (Volume-Weighted Moving Average)
SMMA (Smoothed Moving Average)
KAMA (Kaufman’s Adaptive Moving Average)
ALMA (Arnaud Legoux Moving Average)
FRAMA (Fractal Adaptive Moving Average)
VIDYA (Variable Index Dynamic Average)
2️⃣ Custom RSI – Uses the Moving Average function to modify the calculation method, with an additional option for a dynamic version.
3️⃣ Custom MACD – Uses the Moving Average function to modify the calculation method, with an additional option for a dynamic version.
4️⃣ Custom Alligator – Uses the Moving Average function to modify generic calculations, allowing users to change the calculation method.
TCTDailyBiasLibraryLibrary "TCTDailyBiasLibrary"
Provides a simple function to return a daily bias based on the break of the morning range
getDailyBias()
Returns the daily bias based on the break of the morning range
Returns: bias
DynamicMALibrary "DynamicMA"
Dynamic Moving Averages Library
Introduction
The Dynamic Moving Averages Library is a specialized collection of custom built functions designed to calculate moving averages dynamically, beginning from the first available bar. Unlike standard moving averages, which rely on fixed length lookbacks, this library ensures that indicators remain fully functional from the very first data point, making it an essential tool for analysing assets with short time series or limited historical data.
This approach allows traders and developers to build robust indicators that do not require a preset amount of historical data before generating meaningful outputs. It is particularly advantageous for:
Newly listed assets with minimal price history.
High-timeframe trading, where large lookback periods can lead to delayed or missing data.
By eliminating the constraints of fixed lookback periods, this library enables the seamless construction of trend indicators, smoothing functions, and hybrid models that adapt instantly to market conditions.
Comprehensive Set of Custom Moving Averages
The library includes a wide range of custom dynamic moving averages, each designed for specific analytical use cases:
SMA (Simple Moving Average) – The fundamental moving average, dynamically computed.
EMA (Exponential Moving Average) – Adaptive smoothing for better trend tracking.
DEMA (Double Exponential Moving Average) – Faster trend detection with reduced lag.
TEMA (Triple Exponential Moving Average) – Even more responsive than DEMA.
WMA (Weighted Moving Average) – Emphasizes recent price action while reducing noise.
VWMA (Volume Weighted Moving Average) – Accounts for volume to give more weight to high-volume periods.
HMA (Hull Moving Average) – A superior smoothing method with low lag.
SMMA (Smoothed Moving Average) – A hybrid approach between SMA and EMA.
LSMA (Least Squares Moving Average) – Uses linear regression for trend detection.
RMA (Relative Moving Average) – Used in RSI-based calculations for smooth momentum readings.
ALMA (Arnaud Legoux Moving Average) – A Gaussian-weighted MA for superior signal clarity.
Hyperbolic MA (HyperMA) – A mathematically optimized averaging method with dynamic weighting.
Each function dynamically adjusts its calculation length to match the available bar count, ensuring instant functionality on all assets.
Fully Optimized for Pine Script v6
This library is built on Pine Script v6, ensuring compatibility with modern TradingView indicators and scripts. It includes exportable functions for seamless integration into custom indicators, making it easy to develop trend-following models, volatility filters, and adaptive risk-management systems.
Why Use Dynamic Moving Averages?
Traditional moving averages suffer from a common limitation: they require a fixed historical window to generate meaningful values. This poses several problems:
New Assets Have No Historical Data - If an asset has only been trading for a short period, traditional moving averages may not be able to generate valid signals.
High Timeframes Require Massive Lookbacks - On 1W or 1M charts, a 200-period SMA would require 200 weeks or months of data, making it unusable on newer assets.
Delayed Signal Initialization - Standard indicators often take dozens of bars to stabilize, reducing effectiveness when trading new trends.
The Dynamic Moving Averages Library eliminates these issues by ensuring that every function:
Starts calculation from bar one, using available data instead of waiting for a lookback period.
Adapts dynamically across timeframes, making it equally effective on low or high timeframes.
Allows smoother, more responsive trend tracking, particularly useful for volatile or low-liquidity assets.
This flexibility makes it indispensable for custom script developers, quantitative analysts, and discretionary traders looking to build more adaptive and resilient indicators.
Final Summary
The Dynamic Moving Averages Library is a versatile and powerful set of functions designed to overcome the limitations of fixed-lookback indicators. By dynamically adjusting the calculation length from the first bar, this library ensures that moving averages remain fully functional across all timeframes and asset types, making it an essential tool for traders and developers alike.
With built-in adaptability, low-lag smoothing, and support for multiple moving average types, this library unlocks new possibilities for quantitative trading and strategy development - especially for assets with short price histories or those traded on higher timeframes.
For traders looking to enhance signal reliability, minimize lag, and build adaptable trading systems, the Dynamic Moving Averages Library provides an efficient and flexible solution.
SMA(sourceData, maxLength)
Dynamic SMA
Parameters:
sourceData (float)
maxLength (int)
EMA(src, length)
Dynamic EMA
Parameters:
src (float)
length (int)
DEMA(src, length)
Dynamic DEMA
Parameters:
src (float)
length (int)
TEMA(src, length)
Dynamic TEMA
Parameters:
src (float)
length (int)
WMA(src, length)
Dynamic WMA
Parameters:
src (float)
length (int)
HMA(src, length)
Dynamic HMA
Parameters:
src (float)
length (int)
VWMA(src, volsrc, length)
Dynamic VWMA
Parameters:
src (float)
volsrc (float)
length (int)
SMMA(src, length)
Dynamic SMMA
Parameters:
src (float)
length (int)
LSMA(src, length, offset)
Dynamic LSMA
Parameters:
src (float)
length (int)
offset (int)
RMA(src, length)
Dynamic RMA
Parameters:
src (float)
length (int)
ALMA(src, length, offset_sigma, sigma)
Dynamic ALMA
Parameters:
src (float)
length (int)
offset_sigma (float)
sigma (float)
HyperMA(src, length)
Dynamic HyperbolicMA
Parameters:
src (float)
length (int)
[GYTS] FiltersToolkit LibraryFiltersToolkit Library
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- 1. INTRODUCTION --------- 🌸
💮 What Does This Library Contain?
This library is a curated collection of high-performance digital signal processing (DSP) filters and auxiliary functions designed specifically for financial time series analysis. It includes a shortlist of our favourite and best performing filters — each rigorously tested and selected for their responsiveness, minimal lag and robustness in diverse market conditions. These tools form an integral part of the GoemonYae Trading System (GYTS), chosen for their unique characteristics in handling market data.
The library contains two main categories:
1. Smoothing filters (low-pass filters and moving averages) for e.g. denoising, trend following
2. Detrending tools (high-pass and band-pass filters, known as "oscillators") for e.g. mean reversion
This collection is finely tuned for practical trading applications and is therefore not meant to be exhaustive. However, will continue to expand as we discover and validate new filtering techniques. I welcome collaboration and suggestions for novel approaches.
🌸 ——— 2. ADDED VALUE ——— 🌸
💮 Unified syntax and comprehensive documentation
The FiltersToolkit Library brings together a wide array of valuable filters under a unified, intuitive syntax. Each function is thoroughly documented, with clear explanations and academic sources that underline the mathematical rigour behind the methods. This level of documentation not only facilitates integration into trading strategies but also helps underlying the underlying concepts and rationale.
💮 Optimised performance and readability
The code prioritizes computational efficiency while maintaining readability. Key optimizations include:
- Minimizing redundant calculations in recursive filters
- Smart coefficient caching
- Efficient state management
- Vectorized operations where applicable
💮 Enhanced functionality and flexibility
Some filters in this library introduce extended functionality beyond the original publications. For instance, the MESA Adaptive Moving Average (MAMA) and Ehlers’ Combined Bandpass Filter incorporate multiple variations found in the literature, thereby providing traders with flexible tools that can be fine-tuned to different market conditions.
🌸 ——— 3. THE FILTERS ——— 🌸
💮 Hilbert Transform Function
This function implements the Hilbert Transform as utilised by John Ehlers. It converts a real-valued time series into its analytic signal, enabling the extraction of instantaneous phase and frequency information—an essential step in adaptive filtering.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 Homodyne Discriminator
By leveraging the Hilbert Transform, this function computes the dominant cycle period through a Homodyne Discriminator. It extracts the in-phase and quadrature components of the signal, facilitating a robust estimation of the underlying cycle characteristics.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 MESA Adaptive Moving Average (MAMA)
An advanced dual-stage adaptive moving average, this function outputs both the MAMA and its companion FAMA. It combines adaptive alpha computation with elements from Kaufman’s Adaptive Moving Average (KAMA) to provide a responsive and reliable trend indicator.
Source: John Ehlers - "Rocket Science for Traders" (2001), "TASC 2001 V. 19:9", "Cybernetic Analysis for Stocks and Futures" (2004)
💮 BiQuad Filters
A family of second-order recursive filters offering exceptional control over frequency response:
- High-pass filter for detrending
- Low-pass filter for smooth trend following
- Band-pass filter for cycle isolation
The quality factor (Q) parameter allows fine-tuning of the resonance characteristics, making these filters highly adaptable to different market conditions.
Source: Robert Bristow-Johnson's Audio EQ Cookbook, implemented by @The_Peaceful_Lizard
💮 Relative Vigor Index (RVI)
This filter evaluates the strength of a trend by comparing the closing price to the trading range. Operating similarly to a band-pass filter, the RVI provides insights into market momentum and potential reversals.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Cyber Cycle
The Cyber Cycle filter emphasises market cycles by smoothing out noise and highlighting the dominant cyclical behaviour. It is particularly useful for detecting trend reversals and cyclical patterns in the price data.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Butterworth High Pass Filter
Inspired by the classical Butterworth design, this filter achieves a maximally flat magnitude response in the passband while effectively removing low-frequency trends. Its design minimises phase distortion, which is vital for accurate signal interpretation.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 2-Pole SuperSmoother
Employing a two-pole design, the SuperSmoother filter reduces high-frequency noise with minimal lag. It is engineered to preserve trend integrity while offering a smooth output even in noisy market conditions.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 3-Pole SuperSmoother
An extension of the 2-pole design, the 3-pole SuperSmoother further attenuates high-frequency noise. Its additional pole delivers enhanced smoothing at the cost of slightly increased lag.
Source: John Ehlers – “Cybernetic Analysis for Stocks and Futures” (2004)
💮 Adaptive Directional Volatility Moving Average (ADXVma)
This adaptive moving average adjusts its smoothing factor based on directional volatility. By combining true range and directional movement measurements, it remains exceptionally flat during ranging markets and responsive during directional moves.
Source: Various implementations across platforms, unified and optimized
💮 Ehlers Combined Bandpass Filter with Automated Gain Control (AGC)
This sophisticated filter merges a highpass pre-processing stage with a bandpass filter. An integrated Automated Gain Control normalises the output to a consistent range, while offering both regular and truncated recursive formulations to manage lag.
Source: John F. Ehlers – “Truncated Indicators” (2020), “Cycle Analytics for Traders” (2013)
💮 Voss Predictive Filter
A forward-looking filter that predicts future values of a band-limited signal in real time. By utilising multiple time-delayed feedback terms, it provides anticipatory coupling and delivers a short-term predictive signal.
Source: John Ehlers - "A Peek Into The Future" (TASC 2019-08)
💮 Adaptive Autonomous Recursive Moving Average (A2RMA)
This filter dynamically adjusts its smoothing through an adaptive mechanism based on an efficiency ratio and a dynamic threshold. A double application of an adaptive moving average ensures both responsiveness and stability in volatile and ranging markets alike. Very flat response when properly tuned.
Source: @alexgrover (2019)
💮 Ultimate Smoother (2-Pole)
The Ultimate Smoother filter is engineered to achieve near-zero lag in its passband by subtracting a high-pass response from an all-pass response. This creates a filter that maintains signal fidelity at low frequencies while effectively filtering higher frequencies at the expense of slight overshooting.
Source: John Ehlers - TASC 2024-04 "The Ultimate Smoother"
Note: This library is actively maintained and enhanced. Suggestions for additional filters or improvements are welcome through the usual channels. The source code contains a list of tested filters that did not make it into the curated collection.
Drawings_publicLibrary "Drawings_public"
: Functions to manage drawings on the chart
extend_line(lineId, labelId)
: Extend specific line with its label
Parameters:
lineId (line)
labelId (label)
update_line_coordinates(lineId, labelId, x1, y1, x2, y2)
: Update specific line coordinates with its label
Parameters:
lineId (line)
labelId (label)
x1 (int)
y1 (float)
x2 (int)
y2 (float)
update_label_coordinates(labelId, value)
: Update coordinates of a label
Parameters:
labelId (label)
value (float)
delete_line(lineId, labelId)
: Delete specific line with its label
Parameters:
lineId (line)
labelId (label)
update_box_coordinates(boxId, labelId, left, top, right, bottom)
: Update specific box coordinates with its label
Parameters:
boxId (box)
labelId (label)
left (int)
top (float)
right (int)
bottom (float)
delete_box(boxId, labelId)
: Delete specific box with its label
Parameters:
boxId (box)
labelId (label)
dmarcLevelParserLibrary "dmarcLevelParser"
Provides a parsing library that indicator authors can use in order to parse dmarcLevels.
parseLevels(s)
Parses the string content and returns the `dmarcLevels` found within.
Parameters:
s (string) : The string to parse.
Returns: The parsed dmarc levels.
zoneRange
Fields:
high (series float)
low (series float)
wtdLevels
Fields:
lvnLines (array)
lvnZones (array)
supplyLines (array)
supplyZones (array)
vses (array)
vahs (array)
vals (array)
pocs (array)
miscZones (array)
miscLines (array)
fbos (array)
fbds (array)
majorLevels (array)
mansupLines (array)
mansupmajLines (array)
mansupZones (array)
mansupmajZones (array)
manresLines (array)
manresmajLines (array)
manresZones (array)
manresmajZones (array)
VolumeProfileLibrary "VolumeProfile"
Analyzes volume and price and calculates a volume profile, in particular the Point Of Control and Value Area values.
new(rowSizeInTicks, valueAreaCoverage, startTime)
Constructor method that creates a new Volume Profile
Parameters:
rowSizeInTicks (float) : Internal row size (aka resolution) of the volume profile. Useful for most futures contracts would be '1 / syminfo.mintick'. Default '4'.
valueAreaCoverage (int) : Percentage of total volume that is considered the Value Area. Default '70'
startTime (int) : Start time (unix timestamp in milliseconds) of the Volume Profile. Default 'time'.
Returns: VolumeProfile object
method calculatePOC(vp)
Calculates current Point Of Control of the VP
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
Returns: void
method calculateVA(vp)
Calculates current Value Area High and Low of the VP
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
Returns: void
method update(vp, h, l, v, t)
Processes new chart data and sorts volume into rows. Then calls calculatePOC() and calculateVA() to update the VP. Parameters are usually the output of request.security_lower_tf.
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
h (array) : Array of highs
l (array) : Array of lows
v (array) : Array of volumes
t (array) : Array of candle times
Returns: void
method setSessionHigh(vp, h)
Sets the high of the session the VP is tracking
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
h (float)
Returns: void
method setSessionLow(vp, l)
Sets the low of the session the VP is tracking
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
l (float)
Returns: void
method getPOC(vp)
Gets the current Point Of Control
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
Returns: Point Of Control (float)
method getVAH(vp)
Gets the current Value Area High
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
Returns: Value Area High (float)
method getVAL(vp)
Gets the current Value Area Low
Namespace types: VolumeProfile
Parameters:
vp (VolumeProfile)
Returns: Value Area Low (float)
VolumeProfile
Fields:
rowSizeInTicks (series float)
valueAreaCoverage (series int)
startTime (series int)
valueAreaHigh (series float)
pointOfControl (series float)
valueAreaLow (series float)
sessionHigh (series float)
sessionLow (series float)
volumeByRow (map)
totalVolume (series float)
pocRow (series float)
pocVol (series float)
Request█ OVERVIEW
This library is a tool for Pine Script™ programmers that consolidates access to a wide range of lesser-known data feeds available on TradingView, including metrics from the FRED database, FINRA short sale volume, open interest, and COT data. The functions in this library simplify requests for these data feeds, making them easier to retrieve and use in custom scripts.
█ CONCEPTS
Federal Reserve Economic Data (FRED)
FRED (Federal Reserve Economic Data) is a comprehensive online database curated by the Federal Reserve Bank of St. Louis. It provides free access to extensive economic and financial data from U.S. and international sources. FRED includes numerous economic indicators such as GDP, inflation, employment, and interest rates. Additionally, it provides financial market data, regional statistics, and international metrics such as exchange rates and trade balances.
Sourced from reputable organizations, including U.S. government agencies, international institutions, and other public and private entities, FRED enables users to analyze over 825,000 time series, download their data in various formats, and integrate their information into analytical tools and programming workflows.
On TradingView, FRED data is available from ticker identifiers with the "FRED:" prefix. Users can search for FRED symbols in the "Symbol Search" window, and Pine scripts can retrieve data for these symbols via `request.*()` function calls.
FINRA Short Sale Volume
FINRA (the Financial Industry Regulatory Authority) is a non-governmental organization that supervises and regulates U.S. broker-dealers and securities professionals. Its primary aim is to protect investors and ensure integrity and transparency in financial markets.
FINRA's Short Sale Volume data provides detailed information about daily short-selling activity across U.S. equity markets. This data tracks the volume of short sales reported to FINRA's trade reporting facilities (TRFs), including shares sold on FINRA-regulated Alternative Trading Systems (ATSs) and over-the-counter (OTC) markets, offering transparent access to short-selling information not typically available from exchanges. This data helps market participants, researchers, and regulators monitor trends in short-selling and gain insights into bearish sentiment, hedging strategies, and potential market manipulation. Investors often use this data alongside other metrics to assess stock performance, liquidity, and overall trading activity.
It is important to note that FINRA's Short Sale Volume data does not consolidate short sale information from public exchanges and excludes trading activity that is not publicly disseminated.
TradingView provides ticker identifiers for requesting Short Sale Volume data with the format "FINRA:_SHORT_VOLUME", where "" is a supported U.S. equities symbol (e.g., "AAPL").
Open Interest (OI)
Open interest is a cornerstone indicator of market activity and sentiment in derivatives markets such as options or futures. In contrast to volume, which measures the number of contracts opened or closed within a period, OI measures the number of outstanding contracts that are not yet settled. This distinction makes OI a more robust indicator of how money flows through derivatives, offering meaningful insights into liquidity, market interest, and trends. Many traders and investors analyze OI alongside volume and price action to gain an enhanced perspective on market dynamics and reinforce trading decisions.
TradingView offers many ticker identifiers for requesting OI data with the format "_OI", where "" represents a derivative instrument's ticker ID (e.g., "COMEX:GC1!").
Commitment of Traders (COT)
Commitment of Traders data provides an informative weekly breakdown of the aggregate positions held by various market participants, including commercial hedgers, non-commercial speculators, and small traders, in the U.S. derivative markets. Tallied and managed by the Commodity Futures Trading Commission (CFTC) , these reports provide traders and analysts with detailed insight into an asset's open interest and help them assess the actions of various market players. COT data is valuable for gaining a deeper understanding of market dynamics, sentiment, trends, and liquidity, which helps traders develop informed trading strategies.
TradingView has numerous ticker identifiers that provide access to time series containing data for various COT metrics. To learn about COT ticker IDs and how they work, see our LibraryCOT publication.
█ USING THE LIBRARY
Common function characteristics
• This library's functions construct ticker IDs with valid formats based on their specified parameters, then use them as the `symbol` argument in request.security() to retrieve data from the specified context.
• Most of these functions automatically select the timeframe of a data request because the data feeds are not available for all timeframes.
• All the functions have two overloads. The first overload of each function uses values with the "simple" qualifier to define the requested context, meaning the context does not change after the first script execution. The second accepts "series" values, meaning it can request data from different contexts across executions.
• The `gaps` parameter in most of these functions specifies whether the returned data is `na` when a new value is unavailable for request. By default, its value is `false`, meaning the call returns the last retrieved data when no new data is available.
• The `repaint` parameter in applicable functions determines whether the request can fetch the latest unconfirmed values from a higher timeframe on realtime bars, which might repaint after the script restarts. If `false`, the function only returns confirmed higher-timeframe values to avoid repainting. The default value is `true`.
`fred()`
The `fred()` function retrieves the most recent value of a specified series from the Federal Reserve Economic Data (FRED) database. With this function, programmers can easily fetch macroeconomic indicators, such as GDP and unemployment rates, and use them directly in their scripts.
How it works
The function's `fredCode` parameter accepts a "string" representing the unique identifier of a specific FRED series. Examples include "GDP" for the "Gross Domestic Product" series and "UNRATE" for the "Unemployment Rate" series. Over 825,000 codes are available. To access codes for available series, search the FRED website .
The function adds the "FRED:" prefix to the specified `fredCode` to construct a valid FRED ticker ID (e.g., "FRED:GDP"), which it uses in request.security() to retrieve the series data.
Example Usage
This line of code requests the latest value from the Gross Domestic Product series and assigns the returned value to a `gdpValue` variable:
float gdpValue = fred("GDP")
`finraShortSaleVolume()`
The `finraShortSaleVolume()` function retrieves EOD data from a FINRA Short Sale Volume series. Programmers can call this function to retrieve short-selling information for equities listed on supported exchanges, namely NASDAQ, NYSE, and NYSE ARCA.
How it works
The `symbol` parameter determines which symbol's short sale volume information is retrieved by the function. If the value is na , the function requests short sale volume data for the chart's symbol. The argument can be the name of the symbol from a supported exchange (e.g., "AAPL") or a ticker ID with an exchange prefix ("NASDAQ:AAPL"). If the `symbol` contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", or "BATS".
The function constructs a ticker ID in the format "FINRA:ticker_SHORT_VOLUME", where "ticker" is the symbol name without the exchange prefix (e.g., "AAPL"). It then uses the ticker ID in request.security() to retrieve the available data.
Example Usage
This line of code retrieves short sale volume for the chart's symbol and assigns the result to a `shortVolume` variable:
float shortVolume = finraShortSaleVolume(syminfo.tickerid)
This example requests short sale volume for the "NASDAQ:AAPL" symbol, irrespective of the current chart:
float shortVolume = finraShortSaleVolume("NASDAQ:AAPL")
`openInterestFutures()` and `openInterestCrypto()`
The `openInterestFutures()` function retrieves EOD open interest (OI) data for futures contracts. The `openInterestCrypto()` function provides more granular OI data for cryptocurrency contracts.
How they work
The `openInterestFutures()` function retrieves EOD closing OI information. Its design is focused primarily on retrieving OI data for futures, as only EOD OI data is available for these instruments. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe.
The `openInterestCrypto()` function retrieves opening, high, low, and closing OI data for a cryptocurrency contract on a specified timeframe. Unlike `openInterest()`, this function can also retrieve granular data from intraday timeframes.
Both functions contain a `symbol` parameter that determines the symbol for which the calls request OI data. The functions construct a valid OI ticker ID from the chosen symbol by appending "_OI" to the end (e.g., "CME:ES1!_OI").
The `openInterestFutures()` function requests and returns a two-element tuple containing the futures instrument's EOD closing OI and a "bool" condition indicating whether OI is rising.
The `openInterestCrypto()` function requests and returns a five-element tuple containing the cryptocurrency contract's opening, high, low, and closing OI, and a "bool" condition indicating whether OI is rising.
Example usage
This code line calls `openInterest()` to retrieve EOD OI and the OI rising condition for a futures symbol on the chart, assigning the values to two variables in a tuple:
= openInterestFutures(syminfo.tickerid)
This line retrieves the EOD OI data for "CME:ES1!", irrespective of the current chart's symbol:
= openInterestFutures("CME:ES1!")
This example uses `openInterestCrypto()` to retrieve OHLC OI data and the OI rising condition for a cryptocurrency contract on the chart, sampled at the chart's timeframe. It assigns the returned values to five variables in a tuple:
= openInterestCrypto(syminfo.tickerid, timeframe.period)
This call retrieves OI OHLC and rising information for "BINANCE:BTCUSDT.P" on the "1D" timeframe:
= openInterestCrypto("BINANCE:BTCUSDT.P", "1D")
`commitmentOfTraders()`
The `commitmentOfTraders()` function retrieves data from the Commitment of Traders (COT) reports published by the Commodity Futures Trading Commission (CFTC). This function significantly simplifies the COT request process, making it easier for programmers to access and utilize the available data.
How It Works
This function's parameters determine different parts of a valid ticker ID for retrieving COT data, offering a streamlined alternative to constructing complex COT ticker IDs manually. The `metricName`, `metricDirection`, and `includeOptions` parameters are required. They specify the name of the reported metric, the direction, and whether it includes information from options contracts.
The function also includes several optional parameters. The `CFTCCode` parameter allows programmers to request data for a specific report code. If unspecified, the function requests data based on the chart symbol's root prefix, base currency, or quoted currency, depending on the `mode` argument. The call can specify the report type ("Legacy", "Disaggregated", or "Financial") and metric type ("All", "Old", or "Other") with the `typeCOT` and `metricType` parameters.
Explore the CFTC website to find valid report codes for specific assets. To find detailed information about the metrics included in the reports and their meanings, see the CFTC's Explanatory Notes .
View the function's documentation below for detailed explanations of its parameters. For in-depth information about COT ticker IDs and more advanced functionality, refer to our previously published COT library .
Available metrics
Different COT report types provide different metrics . The tables below list all available metrics for each type and their applicable directions:
+------------------------------+------------------------+
| Legacy (COT) Metric Names | Directions |
+------------------------------+------------------------+
| Open Interest | No direction |
| Noncommercial Positions | Long, Short, Spreading |
| Commercial Positions | Long, Short |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No direction |
| Traders Noncommercial | Long, Short, Spreading |
| Traders Commercial | Long, Short |
| Traders Total Reportable | Long, Short |
| Concentration Gross LT 4 TDR | Long, Short |
| Concentration Gross LT 8 TDR | Long, Short |
| Concentration Net LT 4 TDR | Long, Short |
| Concentration Net LT 8 TDR | Long, Short |
+------------------------------+------------------------+
+-----------------------------------+------------------------+
| Disaggregated (COT2) Metric Names | Directions |
+-----------------------------------+------------------------+
| Open Interest | No Direction |
| Producer Merchant Positions | Long, Short |
| Swap Positions | Long, Short, Spreading |
| Managed Money Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Producer Merchant | Long, Short |
| Traders Swap | Long, Short, Spreading |
| Traders Managed Money | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-----------------------------------+------------------------+
+-------------------------------+------------------------+
| Financial (COT3) Metric Names | Directions |
+-------------------------------+------------------------+
| Open Interest | No Direction |
| Dealer Positions | Long, Short, Spreading |
| Asset Manager Positions | Long, Short, Spreading |
| Leveraged Funds Positions | Long, Short, Spreading |
| Other Reportable Positions | Long, Short, Spreading |
| Total Reportable Positions | Long, Short |
| Nonreportable Positions | Long, Short |
| Traders Total | No Direction |
| Traders Dealer | Long, Short, Spreading |
| Traders Asset Manager | Long, Short, Spreading |
| Traders Leveraged Funds | Long, Short, Spreading |
| Traders Other Reportable | Long, Short, Spreading |
| Traders Total Reportable | Long, Short |
| Concentration Gross LE 4 TDR | Long, Short |
| Concentration Gross LE 8 TDR | Long, Short |
| Concentration Net LE 4 TDR | Long, Short |
| Concentration Net LE 8 TDR | Long, Short |
+-------------------------------+------------------------+
Example usage
This code line retrieves "Noncommercial Positions (Long)" data, without options information, from the "Legacy" report for the chart symbol's root, base currency, or quote currency:
float nonCommercialLong = commitmentOfTraders("Noncommercial Positions", "Long", false)
This example retrieves "Managed Money Positions (Short)" data, with options included, from the "Disaggregated" report:
float disaggregatedData = commitmentOfTraders("Managed Money Positions", "Short", true, "", "Disaggregated")
█ NOTES
• This library uses dynamic requests , allowing dynamic ("series") arguments for the parameters defining the context (ticker ID, timeframe, etc.) of a `request.*()` function call. With this feature, a single `request.*()` call instance can flexibly retrieve data from different feeds across historical executions. Additionally, scripts can use such calls in the local scopes of loops, conditional structures, and even exported library functions, as demonstrated in this script. All scripts coded in Pine Script™ v6 have dynamic requests enabled by default. To learn more about the behaviors and limitations of this feature, see the Dynamic requests section of the Pine Script™ User Manual.
• The library's example code offers a simple demonstration of the exported functions. The script retrieves available data using the function specified by the "Series type" input. The code requests a FRED series or COT (Legacy), FINRA Short Sale Volume, or Open Interest series for the chart's symbol with specific parameters, then plots the retrieved data as a step-line with diamond markers.
Look first. Then leap.
█ EXPORTED FUNCTIONS
This library exports the following functions:
fred(fredCode, gaps)
Requests a value from a specified Federal Reserve Economic Data (FRED) series. FRED is a comprehensive source that hosts numerous U.S. economic datasets. To explore available FRED datasets and codes, search for specific categories or keywords at fred.stlouisfed.org Calls to this function count toward a script's `request.*()` call limit.
Parameters:
fredCode (series string) : The unique identifier of the FRED series. The function uses the value to create a valid ticker ID for retrieving FRED data in the format `"FRED:fredCode"`. For example, `"GDP"` refers to the "Gross Domestic Product" series ("FRED:GDP"), and `"GFDEBTN"` refers to the "Federal Debt: Total Public Debt" series ("FRED:GFDEBTN").
gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.
Returns: (float) The value from the requested FRED series.
finraShortSaleVolume(symbol, gaps, repaint)
Requests FINRA daily short sale volume data for a specified symbol from one of the following exchanges: NASDAQ, NYSE, NYSE ARCA. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.
Parameters:
symbol (series string) : The symbol for which to request short sale volume data. If the specified value contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", "BATS".
gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.
repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.
Returns: (float) The short sale volume for the specified symbol or the chart's symbol.
openInterestFutures(symbol, gaps, repaint)
Requests EOD open interest (OI) and OI rising information for a valid futures symbol. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.
Parameters:
symbol (series string) : The symbol for which to request open interest data.
gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.
repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.
Returns: ( ) A tuple containing the following values:
- The closing OI value for the symbol.
- `true` if the closing OI is above the previous period's value, `false` otherwise.
openInterestCrypto(symbol, timeframe, gaps, repaint)
Requests opening, high, low, and closing open interest (OI) data and OI rising information for a valid cryptocurrency contract on a specified timeframe. Calls to this function count toward a script's `request.*()` call limit.
Parameters:
symbol (series string) : The symbol for which to request open interest data.
timeframe (series string) : The timeframe of the data request. If the timeframe is lower than the chart's timeframe, it causes a runtime error.
gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.
repaint (simple bool) : Optional. If `true` and the `timeframe` represents a higher timeframe, the function returns unconfirmed values from the timeframe on realtime bars, which repaint when the script restarts its executions. If `false`, it returns only confirmed higher-timeframe values to avoid repainting. The default is `true`.
Returns: ( ) A tuple containing the following values:
- The opening, high, low, and closing OI values for the symbol, respectively.
- `true` if the closing OI is above the previous period's value, `false` otherwise.
commitmentOfTraders(metricName, metricDirection, includeOptions, CFTCCode, typeCOT, mode, metricType)
Requests Commitment of Traders (COT) data with specified parameters. This function provides a simplified way to access CFTC COT data available on TradingView. Calls to this function count toward a script's `request.*()` call limit. For more advanced tools and detailed information about COT data, see TradingView's LibraryCOT library.
Parameters:
metricName (series string) : One of the valid metric names listed in the library's documentation and source code.
metricDirection (series string) : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction". Consult the library's documentation or code to see which direction values apply to the specified metric.
includeOptions (series bool) : If `true`, the COT symbol includes options information. Otherwise, it does not.
CFTCCode (series string) : Optional. The CFTC code for the asset. For example, wheat futures (root "ZW") have the code "001602". If one is not specified, the function will attempt to get a valid code for the chart symbol's root, base currency, or main currency.
typeCOT (series string) : Optional. The type of report to request. Possible values are: "Legacy", "Disaggregated", "Financial". The default is "Legacy".
mode (series string) : Optional. Specifies the information the function extracts from a symbol. Possible modes are:
- "Root": The function extracts the futures symbol's root prefix information (e.g., "ES" for "ESH2020").
- "Base currency": The function extracts the first currency from a currency pair (e.g., "EUR" for "EURUSD").
- "Currency": The function extracts the currency of the symbol's quoted values (e.g., "JPY" for "TSE:9984" or "USDJPY").
- "Auto": The function tries the first three modes (Root -> Base currency -> Currency) until it finds a match.
The default is "Auto". If the specified mode is not available for the symbol, it causes a runtime error.
metricType (series string) : Optional. The metric type. Possible values are: "All", "Old", "Other". The default is "All".
Returns: (float) The specified Commitment of Traders data series. If no data is available, it causes a runtime error.
LibraryDivergenceV6LibraryDivergenceV6
Enhance your trading strategies with LibraryDivergenceV6, a comprehensive Pine Script library designed to simplify and optimize the detection of bullish and bearish divergences across multiple technical indicators. Whether you're developing your own indicators or seeking to incorporate robust divergence analysis into your trading systems, this library provides the essential tools and functions to accurately identify potential market reversals and continuations.
Overview
LibraryDivergenceV6 offers a suite of functions that detect divergences between price movements and key technical indicators such as the Relative Strength Index (RSI) and On-Balance Volume (OBV). By automating the complex calculations involved in divergence detection, this library enables traders and developers to implement reliable and customizable divergence strategies with ease.
Key Features
Comprehensive Divergence Detection
Bullish Divergence: Identifies instances where the indicator forms higher lows while the price forms lower lows, signaling potential upward reversals.
Bearish Divergence: Detects situations where the indicator creates lower highs while the price forms higher highs, indicating possible downward reversals.
Overbought and Oversold Conditions: Differentiates between standard and strong divergences by considering overbought and oversold levels, enhancing signal reliability.
Multi-Indicator Support
RSI (Relative Strength Index): Analyze momentum-based divergences to spot potential trend reversals.
OBV (On-Balance Volume): Incorporate volume flow into divergence analysis for a more comprehensive market perspective.
Customizable Parameters
Pivot Points Configuration: Adjust the number of bars to the left and right for pivot detection, allowing fine-tuning based on different timeframes and trading styles.
Range Settings: Define minimum and maximum bar ranges to control the sensitivity of divergence detection, reducing false signals.
Noise Cancellation: Enable or disable noise filtering to focus on significant divergences and minimize minor fluctuations.
Flexible Usage
Exported Functions: Easily integrate divergence detection into your custom indicators or trading strategies with exported functions such as DivergenceBull, DivergenceBear, DivergenceBullOversold, and DivergenceBearOverbought.
Occurrence Handling: Specify which occurrence of a divergence to consider (e.g., most recent, previous) for precise analysis.
Optimized Performance
Efficient Calculations: Designed to handle multiple occurrences and pivot points without compromising script performance.
Line Management: Automatically creates and deletes trend lines based on divergence conditions, ensuring a clean and uncluttered chart display.
