// © LuxAlgo
//@version=5
indicator(“SuperTrend AI (Clustering) [LuxAlgo]”, “LuxAlgo – SuperTrend AI”, overlay = true, max_labels_count = 500)
//——————————————————————————
//Settings
//—————————————————————————–{
length = input(10, ‘ATR Length’)
minMult = input.int(1, ‘Factor Range’, minval = 0, inline = ‘factor’)
maxMult = input.int(5, ”, minval = 0, inline = ‘factor’)
step = input.float(.5, ‘Step’, minval = 0, step = 0.1)
//Trigger error
if minMult > maxMult
runtime.error(‘Minimum factor is greater than maximum factor in the range’)
perfAlpha = input.float(10, ‘Performance Memory’, minval = 2)
fromCluster = input.string(‘Best’, ‘From Cluster’, options = [‘Best’, ‘Average’, ‘Worst’])
//Optimization
maxIter = input.int(1000, ‘Maximum Iteration Steps’, minval = 0, group = ‘Optimization’)
maxData = input.int(10000, ‘Historical Bars Calculation’, minval = 0, group = ‘Optimization’)
//Style
bearCss = input(color.red, ‘Trailing Stop’, inline = ‘ts’, group = ‘Style’)
bullCss = input(color.teal, ”, inline = ‘ts’, group = ‘Style’)
amaBearCss = input(color.new(color.red, 50), ‘AMA’, inline = ‘ama’, group = ‘Style’)
amaBullCss = input(color.new(color.teal, 50), ”, inline = ‘ama’, group = ‘Style’)
showGradient = input(true, ‘Candle Coloring’, group = ‘Style’)
showSignals = input(true, ‘Show Signals’, group = ‘Style’)
//Dashboard
showDash = input(true, ‘Show Dashboard’, group = ‘Dashboard’)
dashLoc = input.string(‘Top Right’, ‘Location’, options = [‘Top Right’, ‘Bottom Right’, ‘Bottom Left’], group = ‘Dashboard’)
textSize = input.string(‘Small’, ‘Size’ , options = [‘Tiny’, ‘Small’, ‘Normal’], group = ‘Dashboard’)
//—————————————————————————–}
//UDT’s
//—————————————————————————–{
type supertrend
float upper = hl2
float lower = hl2
float output
float perf = 0
float factor
int trend = 0
type vector
array<float> out
//—————————————————————————–}
//Supertrend
//—————————————————————————–{
var holder = array.new<supertrend>(0)
var factors = array.new<float>(0)
//Populate supertrend type array
if barstate.isfirst
for i = 0 to int((maxMult – minMult) / step)
factors.push(minMult + i * step)
holder.push(supertrend.new())
atr = ta.atr(length)
//Compute Supertrend for multiple factors
k = 0
for factor in factors
get_spt = holder.get(k)
up = hl2 + atr * factor
dn = hl2 – atr * factor
get_spt.trend := close > get_spt.upper ? 1 : close < get_spt.lower ? 0 : get_spt.trend
get_spt.upper := close[1] < get_spt.upper ? math.min(up, get_spt.upper) : up
get_spt.lower := close[1] > get_spt.lower ? math.max(dn, get_spt.lower) : dn
diff = nz(math.sign(close[1] – get_spt.output))
get_spt.perf += 2/(perfAlpha+1) * (nz(close – close[1]) * diff – get_spt.perf)
get_spt.output := get_spt.trend == 1 ? get_spt.lower : get_spt.upper
get_spt.factor := factor
k += 1
//—————————————————————————–}
//K-means clustering
//—————————————————————————–{
factor_array = array.new<float>(0)
data = array.new<float>(0)
//Populate data arrays
if last_bar_index – bar_index <= maxData
for element in holder
data.push(element.perf)
factor_array.push(element.factor)
//Intitalize centroids using quartiles
centroids = array.new<float>(0)
centroids.push(data.percentile_linear_interpolation(25))
centroids.push(data.percentile_linear_interpolation(50))
centroids.push(data.percentile_linear_interpolation(75))
//Intialize clusters
var array<vector> factors_clusters = na
var array<vector> perfclusters = na
if last_bar_index – bar_index <= maxData
for _ = 0 to maxIter
factors_clusters := array.from(vector.new(array.new<float>(0)), vector.new(array.new<float>(0)), vector.new(array.new<float>(0)))
perfclusters := array.from(vector.new(array.new<float>(0)), vector.new(array.new<float>(0)), vector.new(array.new<float>(0)))
//Assign value to cluster
i = 0
for value in data
dist = array.new<float>(0)
for centroid in centroids
dist.push(math.abs(value – centroid))
idx = dist.indexof(dist.min())
perfclusters.get(idx).out.push(value)
factors_clusters.get(idx).out.push(factor_array.get(i))
i += 1
//Update centroids
new_centroids = array.new<float>(0)
for cluster_ in perfclusters
new_centroids.push(cluster_.out.avg())
//Test if centroid changed
if new_centroids.get(0) == centroids.get(0) and new_centroids.get(1) == centroids.get(1) and new_centroids.get(2) == centroids.get(2)
break
centroids := new_centroids
//—————————————————————————–}
//Signals and trailing stop
//—————————————————————————–{
//Get associated supertrend
var float target_factor = na
var float perf_idx = na
var float perf_ama = na
var from = switch fromCluster
‘Best’ => 2
‘Average’ => 1
‘Worst’ => 0
//Performance index denominator
den = ta.ema(math.abs(close – close[1]), int(perfAlpha))
if not na(perfclusters)
//Get average factors within target cluster
target_factor := nz(factors_clusters.get(from).out.avg(), target_factor)
//Get performance index of target cluster
perf_idx := math.max(nz(perfclusters.get(from).out.avg()), 0) / den
//Get new supertrend
var upper = hl2
var lower = hl2
var os = 0
up = hl2 + atr * target_factor
dn = hl2 – atr * target_factor
upper := close[1] < upper ? math.min(up, upper) : up
lower := close[1] > lower ? math.max(dn, lower) : dn
os := close > upper ? 1 : close < lower ? 0 : os
ts = os ? lower : upper
//Get trailing stop adaptive MA
if na(ts[1]) and not na(ts)
perf_ama := ts
else
perf_ama += perf_idx * (ts – perf_ama)
//—————————————————————————–}
//Dashboard
//—————————————————————————–{
vartable_position=dashLoc==’Bottom Left’?position.bottom_left
:dashLoc==’Top Right’?position.top_right
: position.bottom_right
vartable_size=textSize==’Tiny’?size.tiny
:textSize==’Small’?size.small
: size.normal
var tb = table.new(table_position, 4, 4
, bgcolor = #1e222d
, border_color = #373a46
, border_width = 1
, frame_color = #373a46
, frame_width = 1)
if showDash
if barstate.isfirst
tb.cell(0, 0, ‘Cluster’, text_color = color.white, text_size = table_size)
tb.cell(0, 1, ‘Best’, text_color = color.white, text_size = table_size)
tb.cell(0, 2, ‘Average’, text_color = color.white, text_size = table_size)
tb.cell(0, 3, ‘Worst’, text_color = color.white, text_size = table_size)
tb.cell(1, 0, ‘Size’, text_color = color.white, text_size = table_size)
tb.cell(2, 0, ‘Centroid Dispersion’, text_color = color.white, text_size = table_size)
tb.cell(3, 0, ‘Factors’, text_color = color.white, text_size = table_size)
if barstate.islast
topN = perfclusters.get(2).out.size()
midN = perfclusters.get(1).out.size()
btmN = perfclusters.get(0).out.size()
//Size
tb.cell(1, 1, str.tostring(topN), text_color = color.white, text_size = table_size)
tb.cell(1, 2, str.tostring(midN), text_color = color.white, text_size = table_size)
tb.cell(1, 3, str.tostring(btmN), text_color = color.white, text_size = table_size)
//Content
tb.cell(3, 1, str.tostring(factors_clusters.get(2).out), text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(3, 2, str.tostring(factors_clusters.get(1).out), text_color = color.white, text_size = table_size, text_halign = text.align_left)
tb.cell(3, 3, str.tostring(factors_clusters.get(0).out), text_color = color.white, text_size = table_size, text_halign = text.align_left)
//Calculate dispersion around centroid
i = 0
for cluster_ in perfclusters
disp = 0.
if cluster_.out.size() > 1
for value in cluster_.out
disp += math.abs(value – centroids.get(i))
disp /= switch i
0 => btmN
1 => midN
2 => topN
i += 1
tb.cell(2, 4 – i, str.tostring(disp, ‘#.####’), text_color = color.white, text_size = table_size)
//—————————————————————————–}
//Plots
//—————————————————————————–{
css = os ? bullCss : bearCss
plot(ts, ‘Trailing Stop’, os != os[1] ? na : css)
plot(perf_ama, ‘Trailing Stop AMA’,
ta.cross(close, perf_ama) ? na
: close > perf_ama ? amaBullCss : amaBearCss)
//Candle coloring
barcolor(showGradient ? color.from_gradient(perf_idx, 0, 1, color.new(css, 80), css) : na)
//Signals
n = bar_index
if showSignals
if os > os[1]
label.new(n, ts, str.tostring(int(perf_idx * 10))
, color = bullCss
, style = label.style_label_up
, textcolor = color.white
, size = size.tiny)
if os < os[1]
label.new(n, ts, str.tostring(int(perf_idx * 10))
, color = bearCss
, style = label.style_label_down
, textcolor = color.white
, size = size.tiny)
//—————————————————————————–}
.
