The Adaptive Laguerre Filter calculates a smoothed, dynamically adjusted trendline plotted directly on the main price chart. It dynamically adjusts its damping factor (alpha) based on recent price range variations and median percentile calculations over a spec
Indicator · Pine Script v5 · On the chart
Kernel Regression Toolkit
The Kernel Regression Toolkit calculates and plots non-linear regression estimations directly on the price chart.
GitHub stars2on the repository
Pine versionv5runs as is
Lines of code99
Settings40inputs
LicenceNone statedfile: MPL-2.0
Last update8 months ago25 Feb 2026
Add It to Your TradingView Chart
- 1Copy the scriptPress Copy script above, or the copy button on the code below.
- 2Open the Pine EditorOpen a TradingView chart and choose Pine Editor at the bottom of the screen.
- 3Paste over the templateCreate a new indicator, select all of the template text and paste. Press Save and give it a name.
- 4Add it to the chartPress Add to chart, then adjust the settings with the gear icon next to its name.
Source Code
99 lines of Pine Script v5 from pinescript/editors_picks/kernel-regression-toolkit.pine, published under MPL-2.0 (file header). Keep the author's credit and licence lines when you reuse it.
// This source code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © jdehorty © veryfid
//@version=5
indicator(title='Kernel Regression Toolkit', shorttitle='kreg', overlay=true)
import veryfid/KernelFunctionsFilters/1 as kreg
// For more information on this technique refer to to the original open source indicator by @jdehorty located here:
// https://www.tradingview.com/script/AWNvbPRM-Nadaraya-Watson-Rational-Quadratic-Kernel-Non-Repainting/
// Nadaraya-Watson Kernel Regression Settings
src = input(close)
filt1 = input.string("Smooth", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 1 - Fast", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type1 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 1 - Fast", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h = input.int(3, 'Lookback Window', minval=3, group="Kernel Regression 1 - Fast", inline="k")
r = input.float(1., 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 1 - Fast", inline="k1")
x = input.int(2, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 1 - Fast", inline="k1")
lag = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='k1', group="Kernel Regression 1 - Fast")
filt2 = input.string("Smooth", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 2 - Medium", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type2 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 2 - Medium", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h2 = input.int(8, 'Lookback Window', minval=3, tooltip='The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50', group="Kernel Regression 2 - Medium", inline="k")
r2 = input.float(1., 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 2 - Medium", inline="kernel2")
x2 = input.int(20, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 2 - Medium", inline="kernel2")
lag2 = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='kernel2', group='Kernel Regression 2 - Medium')
filt3 = input.string("Smooth", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 3 - Slow", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type3 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 3 - Slow", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h3 = input.int(200, 'Lookback Window', minval=3, tooltip='The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50', group="Kernel Regression 3 - Slow", inline="k")
r3 = input.float(1., 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 3 - Slow", inline="kernel3")
x3 = input.int(500, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 3 - Slow", inline="kernel3")
lag3 = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='kernel3', group='Kernel Regression 3 - Slow')
filt4 = input.string("Smooth", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 4 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type4 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 4 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h4 = input.int(30, 'Lookback Window', minval=3, group="Kernel Regression 4 - Mono", inline="k")
r4 = input.float(8., 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 4 - Mono", inline="k1")
x4 = input.int(35, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 4 - Mono", inline="k1")
lag4 = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='k1', group="Kernel Regression 4 - Mono")
filt5 = input.string("Smooth", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 5 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type5 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 5 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h5 = input.int(69, 'Lookback Window', minval=3, group="Kernel Regression 5 - Mono", inline="k")
r5 = input.float(1, 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 5 - Mono", inline="k1")
x5 = input.int(500, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 5 - Mono", inline="k1")
lag5 = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='k1', group="Kernel Regression 5 - Mono")
filt6 = input.string("Zero Lag", options = ["No Filter", "Smooth", "Zero Lag"],title = "",group ="Kernel Regression 6 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
type6 = input.string("Rational Quadratic", options = ["Rational Quadratic", "Gaussian", "Periodic", "Locally Periodic"],title = "",group ="Kernel Regression 6 - Mono", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
h6 = input.int(200, 'Lookback Window', minval=3, group="Kernel Regression 6 - Mono", inline="k")
r6 = input.float(1., 'Weighting', step=0.25, tooltip='Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25', group="Kernel Regression 6 - Mono", inline="k1")
x6 = input.int(500, "Level", tooltip='Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25', group="Kernel Regression 6 - Mono", inline="k1")
lag6 = input.int(2, "Lag", tooltip="Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2", inline='k1', group="Kernel Regression 6 - Mono")
ksrc = kreg.rationalQuadratic(close, 3, 1, 1, "No FIlter")
line1 = type1 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h, r, x, filt1) : type1 == "Gaussian" ? kreg.gaussian(src, h-lag, x, filt1) : type1 == "Periodic" ? kreg.periodic(src, h, lag, x, filt1) : type1 == "Locally Periodic" ? kreg.locallyPeriodic(src, h, lag, x, filt1) : na
line2 = type2 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h2, r2, x2, filt2) : type2 == "Gaussian" ? kreg.gaussian(src, h2-lag2, x2, filt2) : type2 == "Periodic" ? kreg.periodic(src, h2, lag2, x2, filt2) : type2 == "Locally Periodic" ? kreg.locallyPeriodic(src, h2, lag2, x2, filt2) : na
line3 = type3 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h3, r3, x3, filt3) : type3 == "Gaussian" ? kreg.gaussian(src, h3-lag3, x3, filt3) : type3 == "Periodic" ? kreg.periodic(src, h3, lag3, x3, filt3) : type3 == "Locally Periodic" ? kreg.locallyPeriodic(src, h3, lag3, x3, filt3) : na
line4 = type4 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h4, r4, x4, filt4) : type4 == "Gaussian" ? kreg.gaussian(src, h4-lag4, x4, filt4) : type4 == "Periodic" ? kreg.periodic(src, h4, lag4, x4, filt4) : type4 == "Locally Periodic" ? kreg.locallyPeriodic(src, h4, lag4, x4, filt4) : na
line5 = type5 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h5, r5, x5, filt5) : type5 == "Gaussian" ? kreg.gaussian(src, h5-lag5, x5, filt5) : type5 == "Periodic" ? kreg.periodic(src, h5, lag5, x5, filt5) : type5 == "Locally Periodic" ? kreg.locallyPeriodic(src, h5, lag5, x5, filt5) : na
line6 = type6 == "Rational Quadratic" ? kreg.rationalQuadratic(src, h6, r6, x6, filt6) : type6 == "Gaussian" ? kreg.gaussian(src, h6-lag6, x6, filt6) : type6 == "Periodic" ? kreg.periodic(src, h6, lag6, x6, filt6) : type6 == "Locally Periodic" ? kreg.locallyPeriodic(src, h6, lag6, x6, filt6) : na
usej1 = input(false,"Show J line 1",group ="J Line 1", inline = "k")
jline1src1 = input.string("Line 2", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close"],title = "",group ="J Line 1", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
jline1src2 = input.string("Line 4", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close"],title = "",group ="J Line 1", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
jline1a = jline1src1 == "Line 1" ? line1 : jline1src1 == "Line 2" ? line2 : jline1src1 == "Line 3" ? line3 : jline1src1 == "Line 4" ? line4 : jline1src1 == "Line 5" ? line5 : jline1src1 == "Line 6" ? line6 : jline1src1 == "Close" ? close : jline1src1 == "Ksrc" ? ksrc : ksrc
jline1b = jline1src2 == "Line 1" ? line1 : jline1src2 == "Line 2" ? line2 : jline1src2 == "Line 3" ? line3 : jline1src2 == "Line 4" ? line4 : jline1src2 == "Line 5" ? line5 : jline1src2 == "Line 6" ? line6 : jline1src2 == "Close" ? close : jline1src2 == "Ksrc" ? ksrc : ksrc
jline1 = kreg.j(jline1a,jline1b)
usej2 = input(false,"Show J line 2",group ="J Line 2", inline = "k")
jline2src1 = input.string("Line 3", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close", "J Line 1"],title = "",group ="J Line 2", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
jline2src2 = input.string("Line 5", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close", "J Line 1"],title = "",group ="J Line 2", inline = "k", tooltip = "Select Kernel Regression type from dropdown box. The Lookback Window is the number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 ")
jline2a = jline2src1 == "Line 1" ? line1 : jline2src1 == "Line 2" ? line2 : jline2src1 == "Line 3" ? line3 : jline2src1 == "Line 4" ? line4 : jline2src1 == "Line 5" ? line5 : jline2src1 == "Line 6" ? line6 : jline2src1 == "Close" ? close : jline2src1 == "J Line 1" ? jline1 : ksrc
jline2b = jline2src2 == "Line 1" ? line1 : jline2src2 == "Line 2" ? line2 : jline2src2 == "Line 3" ? line3 : jline2src2 == "Line 4" ? line4 : jline2src2 == "Line 5" ? line5 : jline2src2 == "Line 6" ? line6 : jline2src2 == "Close" ? close : jline2src2 == "J Line 1" ? jline1 : ksrc
jline2 = kreg.j(jline2a,jline2b)
useblend = input(false,"Show",group ="Blend Two Lines", inline = "k")
blendsrc1 = input.string("Line 2", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close"],title = "",group ="Blend Two Lines", inline = "k", tooltip = "Select two Kernel Regression lines you would like to blend from the dropdown box. Adjust the percentage of the blend from 0-100%. It is also possible to overdrive the blend by pushing it over 100")
blendsrc2 = input.string("Line 4", options = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5", "Line 6","Close"],title = "",group ="Blend Two Lines", inline = "k")
blenda = blendsrc1 == "Line 1" ? line1 : blendsrc1 == "Line 2" ? line2 : blendsrc1 == "Line 3" ? line3 : blendsrc1 == "Line 4" ? line4 : blendsrc1 == "Line 5" ? line5 : blendsrc1 == "Line 6" ? line6 : blendsrc1 == "Close" ? close : blendsrc1 == "Ksrc" ? ksrc : ksrc
blendb = blendsrc2 == "Line 1" ? line1 : blendsrc2 == "Line 2" ? line2 : blendsrc2 == "Line 3" ? line3 : blendsrc2 == "Line 4" ? line4 : blendsrc2 == "Line 5" ? line5 : blendsrc2 == "Line 6" ? line6 : blendsrc2 == "Close" ? close : blendsrc2 == "Ksrc" ? ksrc : ksrc
weight = input(50,"",group ="Blend Two Lines", inline = "k")
weightcalc = weight/100
blend = blenda*weightcalc + blendb * (1 - weightcalc)
plot(line1,"Line1", color= line1 > line1[1] ? color.teal : color.red, linewidth=2)
plot(line2,"Line2", color= line2 > line2[1] ? color.teal : color.red , linewidth=2)
plot(line3,"Line3", color= line3 > line3[1] ? color.teal : color.red , linewidth=2)
plot(line4,"Line4", color= color.gray, linewidth=1)
plot(line5,"Line5", color= color.gray, linewidth=1)
plot(line6,"Line6", color= color.gray, linewidth=1)
plot(usej1 ? jline1 : na,"j1",color = color.white)
plot(usej2 ? jline2 : na,"j2",color = color.white)
plot(useblend ? blend : na,"Blend")
Overview
Kernel Regression Toolkit provides multiple configurable kernel regression lines using functions such as Rational Quadratic, Gaussian, Periodic, and Locally Periodic kernels. Users can apply filtering methods like Smooth or Zero Lag, adjust lookback windows, weighting parameters, level fitting, and lag to tailor the responsiveness and trend-filtering capabilities.
- Multiple kernel regression types
- Filtering options including Smooth and Zero Lag
- Customizable lookback windows and weighting
- Configurable lag and level parameters
Details
Published by
regalouisei on GitHub
Code credit
jdehorty © veryfid, as written in the script's header
Type
Draws
On the price chart
Category
Markets
Any symbol
Timeframes
Any timeframe
Risk Notes
No repainting, lookahead or stacked entries were found in its code or README. That says nothing about how well it works: test it first.
Settings
The inputs you can change from the gear icon on the chart. Defaults are the author's.
| Setting | Type | Default | Variable |
|---|---|---|---|
| src | source | close | src |
| filt1 | string | Smooth | filt1 |
| type1 | string | Rational Quadratic | type1 |
| Lookback Window | int | 3 | h |
| Weighting | float | 1. | r |
| Level | int | 2 | x |
| Lag | int | 2 | lag |
| filt2 | string | Smooth | filt2 |
| type2 | string | Rational Quadratic | type2 |
| Lookback Window | int | 8 | h2 |
| Weighting | float | 1. | r2 |
| Level | int | 20 | x2 |
Show all 40 settings
| Setting | Type | Default | Variable |
|---|---|---|---|
| Lag | int | 2 | lag2 |
| filt3 | string | Smooth | filt3 |
| type3 | string | Rational Quadratic | type3 |
| Lookback Window | int | 200 | h3 |
| Weighting | float | 1. | r3 |
| Level | int | 500 | x3 |
| Lag | int | 2 | lag3 |
| filt4 | string | Smooth | filt4 |
| type4 | string | Rational Quadratic | type4 |
| Lookback Window | int | 30 | h4 |
| Weighting | float | 8. | r4 |
| Level | int | 35 | x4 |
| Lag | int | 2 | lag4 |
| filt5 | string | Smooth | filt5 |
| type5 | string | Rational Quadratic | type5 |
| Lookback Window | int | 69 | h5 |
| Weighting | float | 1 | r5 |
| Level | int | 500 | x5 |
| Lag | int | 2 | lag5 |
| filt6 | string | Zero Lag | filt6 |
| type6 | string | Rational Quadratic | type6 |
| Lookback Window | int | 200 | h6 |
| Weighting | float | 1. | r6 |
| Level | int | 500 | x6 |
| Lag | int | 2 | lag6 |
| Show J line 1 | bool | false | usej1 |
| jline1src1 | string | Line 2 | jline1src1 |
| jline1src2 | string | Line 4 | jline1src2 |
Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.
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