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//PRC_Volume SuperTrend AI
//version = 0
//29.02.24
//Iván González @ www.prorealcode.com
//Sharing ProRealTime knowledge
///////////////////////////////////////////////////////////////////////
//inputs
// ~~ Input settings for K and N values
k = 3 // Neighbors 1 to 100
//m = 10 // Data
//n = max(k,m)
//// ~~ Input settings for prediction values
//KNNPriceLen = 20 // Price Trend 2 - 500 step 10
//KNNSTlen = 100 // Prediction Trend 2 - 500 step 10
aisignals = 1 // Boolean AI Trend Signals
//// ~~ Define SuperTrend parameters
len = 10 // Length
factor = 3.0 // step 0.1
maSrc = 0 // MaType
// ~~ Calculate the SuperTrend based on the user's choice
vwma = average[len,maSrc](close*volume)/average[len,maSrc](volume)
atr = averagetruerange[len](close)

upperband = vwma + factor*atr
//lowerband = vwma - factor*atr

if barindex < len then
upperband = close
lowerband = close
direction = 1
else
//Redefine upperband
if upperband < upperband[1] or close[1] > upperband[1] then
upperband = upperband
else
upperband = upperband[1]
endif
//Redefine lowerband
if lowerband > lowerband[1] or close[1] < lowerband[1] then
lowerband = lowerband
else
lowerband = lowerband[1]
endif
//Define upperband
prevSupertrend = mysuperTrend[1]
if prevSupertrend = upperband[1] then
if close > upperband then
direction = -1
else
direction = 1
endif
else
if close < lowerband then
direction = 1
else
direction = -1
endif
endif
endif

if direction = -1 then
mysupertrend = lowerband
else
mysupertrend = upperband
endif
//// ~~ Collect data points and their corresponding labels
//myprice = WeightedAverage[KNNPriceLen](close)
//sT = WeightedAverage[kNNSTlen](mysupertrend)
//
//for i=0 to n-1 do
//$data[i]=mysupertrend[i]
//if myprice[i] > sT[i] then
//$labels[i] = 1
//else
//$labels[i] = 0
//endif
//next
//
//// ~~ Classify the current data point
//currentsuperTrend = mysuperTrend
////label = knn_weighted(data, labels, k, current_superTrend)
//
//// Compute distances from the current point to all other points
//n1 = lastset($data)
//for i = 0 to n1-1 do
//$distances[i] = abs(currentsuperTrend[i]-$data[i])
//$indices[i] = i
//next
//// Sort distances and corresponding indices in ascending order
//// Bubble sort method
//for i=0 to n1-2 do
//for j=0 to n1-i-2 do
//if $distances[j]>$distances[j+1] then
//tempDist = $distances[j]
//$distances[j]=$distances[j+1]
//$distances[j+1]=tempDist
//tempindex = $indices[j]
//$indices[j]=$indices[j+1]
//$indices[j+1]=tempindex
//endif
//next
//next
//// Compute weighted sum of labels of the k nearest neighbors
weightedsum=0
totalweight=0
for i=0 to k-1 do
//myindex = $indices[i]
labeli = $labels[i]
weighti = 1 / ($distances[i]+pow(10,-6) )
weightedsum = weighti*labeli+weightedsum
totalweight = weighti+totalweight
next
//
label = floor(weightedsum / totalweight,2)
//
//// ~~ Ai Super Trend Signals
starttrendup = label = 1 and label[1]<>1 and aisignals
starttrenddn = label = 0 and label[1]<>0 and aisignals
TrendUp = direction = -1 and direction[1] = 1 and label = 1 and aisignals
TrendDn = direction = 1 and direction[1] = -1 and label = 0 and aisignals
//

screener [starttrendup or starttrenddn or TrendUp or TrendDn]

Te aviso que saldrán pocos valeurs