Freelance Service auf MQL5.com: Tradingview indicator Machine Learning Adaptive SuperTrend [AlgoAlpha] convert into mq4

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Spezifikation

I want tradingview indicator Machine Learning Adaptive SuperTrend [AlgoAlpha] convert into mq4

Tradingview indicator code as under

// 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")

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