Machine Learning Adaptive SuperT…: Free TradingView Indicator (Pine Script)

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

166 lines of Pine Script v5 from pinescript/editors_picks/machine-learning-adaptive-supertrend-algoalpha.pine, published under MPL-2.0 (file header). Keep the author's credit and licence lines when you reuse it.

machine-learning-adaptive-supertrend-algoalpha.pine

// This Pine Script™ code is subject to the terms of the Mozilla Public License 2.0 at https://mozilla.org/MPL/2.0/
// © AlgoAlpha

//@version=5
indicator("Machine Learning Adaptive SuperTrend [AlgoAlpha]", "AlgoAlpha - 🤖 Adaptive SuperTrend", overlay = true, max_labels_count = 500)
import TradingView/ta/7
atr_len = input.int(10, "ATR Length", group = "SuperTrend Settings")
fact = input.float(3, "SuperTrend Factor", group = "SuperTrend Settings")
training_data_period = input.int(100, "Training Data Length", group = "K-Means Settings")
highvol = input.float(0.75, "Initial High volatility Percentile Guess", maxval = 1, group = "K-Means Settings", tooltip = "The initial guess of where the potential 'high volatility' area is, a value of 0.75 will take the 75th percentile of the range of ATR values over the training data period")
midvol = input.float(0.5, "Initial Medium volatility Percentile Guess", maxval = 1, group = "K-Means Settings", tooltip = "The initial guess of where the potential 'medium volatility' area is, a value of 0.5 will take the 50th percentile of the range of ATR values over the training data period")
lowvol = input.float(0.25, "Initial Low volatility Percentile Guess", maxval = 1, group = "K-Means Settings", tooltip = "The initial guess of where the potential 'low volatility' area is, a value of 0.25 will take the 25th percentile of the range of ATR values over the training data period")
t1 = input.int(70, "Transparency 1", maxval = 100, minval = 0, group = "Appearance")
t2 = input.int(95, "Transparency 2", maxval = 100, minval = 0, group = "Appearance")
green = input.color(#00ffbb, "Bullish Color", group = "Appearance")
red = input.color(#ff1100, "Bearish Color", group = "Appearance")

pine_supertrend(factor, atr) =>
    src = hl2
    upperBand = src + factor * atr
    lowerBand = src - factor * atr
    prevLowerBand = nz(lowerBand[1])
    prevUpperBand = nz(upperBand[1])

    lowerBand := lowerBand > prevLowerBand or close[1] < prevLowerBand ? lowerBand : prevLowerBand
    upperBand := upperBand < prevUpperBand or close[1] > prevUpperBand ? upperBand : prevUpperBand
    int _direction = na
    float superTrend = na
    prevSuperTrend = superTrend[1]
    if na(atr[1])
        _direction := 1
    else if prevSuperTrend == prevUpperBand
        _direction := close > upperBand ? -1 : 1
    else
        _direction := close < lowerBand ? 1 : -1
    superTrend := _direction == -1 ? lowerBand : upperBand
    [superTrend, _direction]

volatility = ta.atr(atr_len)

upper = ta.highest(volatility, training_data_period)
lower = ta.lowest(volatility, training_data_period)

high_volatility = lower + (upper-lower) * highvol
medium_volatility = lower + (upper-lower) * midvol
low_volatility = lower + (upper-lower) * lowvol

iterations = 0

size_a = 0
size_b = 0
size_c = 0

hv = array.new_float()
mv = array.new_float()
lv = array.new_float()
amean = array.new_float(1,high_volatility)
bmean = array.new_float(1,medium_volatility)
cmean = array.new_float(1,low_volatility)

if nz(volatility) > 0 and bar_index >= training_data_period-1

    while ((amean.size() == 1 ? true : (amean.first() != amean.get(1))) or (bmean.size() == 1 ? true : (bmean.first() != bmean.get(1))) or (cmean.size() == 1 ? true : (cmean.first() != cmean.get(1))))
        hv.clear()
        mv.clear()
        lv.clear()
        for i = training_data_period-1 to 0
            _1 = math.abs(volatility[i] - amean.first())
            _2 = math.abs(volatility[i] - bmean.first())
            _3 = math.abs(volatility[i] - cmean.first())
            if _1 < _2 and _1 < _3
                hv.unshift(volatility[i])

            if _2 < _1 and _2 < _3
                mv.unshift(volatility[i])

            if _3 < _1 and _3 < _2
                lv.unshift(volatility[i])
        
        amean.unshift(hv.avg())
        bmean.unshift(mv.avg())
        cmean.unshift(lv.avg())
        size_a := hv.size()
        size_b := mv.size()
        size_c := lv.size()
        iterations := iterations + 1

hv_new = amean.first()
mv_new = bmean.first()
lv_new = cmean.first()
vdist_a = math.abs(volatility - hv_new)
vdist_b = math.abs(volatility - mv_new)
vdist_c = math.abs(volatility - lv_new)

distances = array.new_float()
centroids = array.new_float()

distances.push(vdist_a)
distances.push(vdist_b)
distances.push(vdist_c)

centroids.push(hv_new)
centroids.push(mv_new)
centroids.push(lv_new)

cluster = distances.indexof(distances.min()) // 0 for high, 1 for medium, 2 for low
assigned_centroid = cluster == -1 ? na : centroids.get(cluster)

[ST, dir] = pine_supertrend(fact, assigned_centroid)
upTrend = plot(close > ST ? ST : na, color = color.new(green, t1), style = plot.style_linebr) //, force_overlay = true
downTrend = plot(close < ST ? ST : na, color = color.new(red, t1), style = plot.style_linebr, force_overlay = false) //, force_overlay = true
bodyMiddle = plot(barstate.isfirst ? na : (open + close) / 2, "Body Middle",display = display.none)

fill(bodyMiddle, upTrend, (open + close) / 2, ST, color.new(green, t2), color.new(green, t1))
fill(bodyMiddle, downTrend, ST, (open + close) / 2, color.new(red, t1), color.new(red, t2))

plotshape(ta.crossunder(dir, 0) ? ST : na, "Bullish Trend", shape.labelup, location.absolute, green, text = "▲", textcolor = chart.fg_color, size = size.small)
plotshape(ta.crossover(dir, 0) ? ST : na, "Bearish Trend", shape.labeldown, location.absolute, red, text = "▼", textcolor = chart.fg_color, size = size.small)

label.new(bar_index, dir > 0 ? ST + ta.atr(7) : ST - ta.atr(7), text = str.tostring(4 - (cluster + 1)), style = label.style_none, textcolor = color.from_gradient(cluster + 1, 1, 3, color.new(dir > 0 ? red : green, 30), color.new(dir > 0 ? red : green, 90)))

if barstate.islast
    var data_table = table.new(position=position.top_right, columns=4, rows=4, bgcolor = chart.bg_color, border_width=1, border_color = chart.fg_color, frame_color = chart.fg_color, frame_width = 1)
    table.cell(data_table, text_halign=text.align_center, column=0, row=0, text="Cluster Number (Volatility Level)", text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=1, row=0, text="Cluster Centroid (ATR)", text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=2, row=0, text="Cluster Size (No. of Data Points in Each Cluster)", text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=3, row=0, text="Current Volatility", text_color = chart.fg_color)

    table.cell(data_table, text_halign=text.align_center, column=0, row=1, text="3 (High)", text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=0, row=2, text= "2 (Medium)", text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=0, row=3, text= "1 (Low)", text_color = chart.fg_color)

    table.cell(data_table, text_halign=text.align_center, column=1, row=1, text=str.format("{0,number,#.##}", hv_new), text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=1, row=2, text=str.format("{0,number,#.##}", mv_new), text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=1, row=3, text=str.format("{0,number,#.##}", lv_new), text_color = chart.fg_color)

    table.cell(data_table, text_halign=text.align_center, column=2, row=1, text=str.format("{0,number,#.##}", size_c), text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=2, row=2, text=str.format("{0,number,#.##}", size_b), text_color = chart.fg_color)
    table.cell(data_table, text_halign=text.align_center, column=2, row=3, text=str.format("{0,number,#.##}", size_a), text_color = chart.fg_color)

    table.cell(data_table, text_halign=text.align_center, column=3, row=1, text="HIGH " + "(ATR: " + str.format("{0,number,#.##}", volatility) + ")", text_color = chart.bg_color)
    table.cell(data_table, text_halign=text.align_center, column=3, row=2, text="MEDIUM " + "(ATR: " + str.format("{0,number,#.##}", volatility) + ")", text_color = chart.bg_color)
    table.cell(data_table, text_halign=text.align_center, column=3, row=3, text="LOW " + "(ATR: " + str.format("{0,number,#.##}", volatility) + ")", text_color = chart.bg_color)

    if cluster == 0
        data_table.cell_set_bgcolor(3, 1, chart.fg_color)
    else
        data_table.cell_set_bgcolor(3, 1, chart.bg_color)

    if cluster == 1
        data_table.cell_set_bgcolor(3, 2, chart.fg_color)
    else
        data_table.cell_set_bgcolor(3, 2, chart.bg_color)

    if cluster == 2
        data_table.cell_set_bgcolor(3, 3, chart.fg_color)
    else
        data_table.cell_set_bgcolor(3, 3, chart.bg_color)

////////////////////////////Alerts
alertcondition(ta.crossunder(dir, 0) and barstate.isconfirmed, "Bullish Trend Shift")
alertcondition(ta.crossover(dir, 0) and barstate.isconfirmed, "Bearish Trend Shift")
alertcondition(cluster == 0 and cluster[1] != 0 and barstate.isconfirmed, "High Volatility")
alertcondition(cluster == 1 and cluster[1] != 1 and barstate.isconfirmed, "Medium Volatility")
alertcondition(cluster == 2 and cluster[1] != 2 and barstate.isconfirmed, "Low Volatility")
alertcondition(cluster == 2 and cluster[1] != 2 and barstate.isconfirmed, "Low Volatility")

Overview

Machine Learning Adaptive SuperTrend [AlgoAlpha] segments historical volatility into high, medium, and low clusters to dynamically adjust the SuperTrend bands to shifting market conditions. Plotted directly on the price chart, it provides directional trend indications with color-coded bands based on bullish and bearish state changes. Configurable inputs include the ATR length, baseline factor, training sample length, and initial percentile guesses.

K-means clustering algorithm applied to ATR volatility
Adaptive SuperTrend trend-following bands
Dynamic volatility regime classification
Configurable training sample length and percentile thresholds
Overlay display with customizable band colors and transparencies

Details

Published by

regalouisei on GitHub

Code credit

AlgoAlpha, as written in the script's header

Type

Indicator, Pine Script v5

Draws

On the price chart

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.

File

pinescript/editors_picks/machine-learning-adaptive-supertrend-algoalpha.pine

Repository

github.com/regalouisei/collect-tradingview. See its other scripts in the library

Settings

The inputs you can change from the gear icon on the chart. Defaults are the author's.

Setting	Type	Default	Variable
ATR Length	`int`	`10`	`atr_len`
SuperTrend Factor	`float`	`3`	`fact`
Training Data Length	`int`	`100`	`training_data_period`
Initial High volatility Percentile Guess	`float`	`0.75`	`highvol`
Initial Medium volatility Percentile Guess	`float`	`0.5`	`midvol`
Initial Low volatility Percentile Guess	`float`	`0.25`	`lowvol`
Transparency 1	`int`	`70`	`t1`
Transparency 2	`int`	`95`	`t2`
Bullish Color	`color`	`#00ffbb`	`green`
Bearish Color	`color`	`#ff1100`	`red`

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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