Machine-Learning-Z-Score-Tradingview

Ecco l’indicatore. Non avendo ancora la possibilità di creare funzioni proprie, mi è risultato complesso programmare la correlazione e consuma molte risorse.
Nell’indicatore vedrai che per impostazione predefinita utilizzo un’approssimazione e, nel caso si desideri il coefficiente di Pearson, suggerisco di attivare l’opzione per calcolare solo sulle ultime X barre (200 per esempio).

//-------------------------------------------------------------
// PRC_Machine Learning Z-Score by Steversteves
// version = 0
// 10.09.25
// Iván González @ www.prorealcode.com
// Sharing ProRealTime knowledge
//-------------------------------------------------------------
//defparam calculateonlastbars=200
//-------------------------------------------------------------
// ===== Input parameters =====
//-------------------------------------------------------------
autoadjust = 1   // 1 = true, 0 = false
lengthinput = 75 // default length if autoadjust = 0
plottgt = 1      // 1 = true, 0 = false
manualprice = 0  // 1 = true, 0 = false
pullbacksd = 1.0 // pullback standard deviation factor
aproxCorr = 1 // 1 = use R2 , 0 = use pearson correlation (uncomment defparam line)
barcolor = 1 // 1 = true, 0 = false
//-------------------------------------------------------------
// Correlation calculation
//-------------------------------------------------------------
if aproxCorr then
   // ===== Correlation approximation with R2 =====
   cor1 = R2[50](close)
   cor2 = R2[100](close)
   cor3 = R2[150](close)
   cor4 = R2[200](close)
   cor5 = R2[250](close)
   cor6 = R2[300](close)
   cor7 = R2[350](close)
   cor8 = R2[400](close)
   cor9 = R2[450](close)
   cor10 = R2[500](close)
else
   // ===== Pearson Correlation =====
   // --- Pearson correlation for 50 ---
   len = 50
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor1 = ABS(num / den)
   ELSE
      cor1 = 0
   ENDIF
   
   // --- Pearson correlation for 100 ---
   len = 100
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor2 = ABS(num / den)
   ELSE
      cor2 = 0
   ENDIF
   
   // --- Pearson correlation for 150 ---
   len = 150
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor3 = ABS(num / den)
   ELSE
      cor3 = 0
   ENDIF
   
   // --- Pearson correlation for 200 ---
   len = 200
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor4 = ABS(num / den)
   ELSE
      cor4 = 0
   ENDIF
   
   // --- Pearson correlation for 250 ---
   len = 250
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor5 = ABS(num / den)
   ELSE
      cor5 = 0
   ENDIF
   
   // --- Pearson correlation for 300 ---
   len = 300
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor6 = ABS(num / den)
   ELSE
      cor6 = 0
   ENDIF
   
   // --- Pearson correlation for 350 ---
   len = 350
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor7 = ABS(num / den)
   ELSE
      cor7 = 0
   ENDIF
   
   // --- Pearson correlation for 400 ---
   len = 400
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor8 = ABS(num / den)
   ELSE
      cor8 = 0
   ENDIF
   
   // --- Pearson correlation for 450 ---
   len = 450
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor9 = ABS(num / den)
   ELSE
      cor9 = 0
   ENDIF
   
   // --- Pearson correlation for 500 ---
   len = 500
   sumX = 0
   sumY = 0
   sumXY = 0
   sumX2 = 0
   sumY2 = 0
   
   FOR i = 0 TO len-1 DO
      xi = barindex[i]
      yi = close[i]
      sumX = sumX + xi
      sumY = sumY + yi
      sumXY = sumXY + xi * yi
      sumX2 = sumX2 + xi * xi
      sumY2 = sumY2 + yi * yi
   NEXT
   
   n = len
   num = n * sumXY - sumX * sumY
   den = SQRT((n * sumX2 - sumX * sumX) * (n * sumY2 - sumY * sumY))
   
   IF den <> 0 THEN
      cor10 = ABS(num / den)
   ELSE
      cor10 = 0
   ENDIF
endif

maxcor = MAX(MAX(MAX(MAX(MAX(cor1,cor2),cor3),MAX(cor4,cor5)),MAX(cor6,cor7)),MAX(MAX(cor8,cor9),cor10))

IF autoadjust = 1 THEN
   IF maxcor = cor1 THEN
      len = 50
   ELSIF maxcor = cor2 THEN
      len = 100
   ELSIF maxcor = cor3 THEN
      len = 150
   ELSIF maxcor = cor4 THEN
      len = 200
   ELSIF maxcor = cor5 THEN
      len = 250
   ELSIF maxcor = cor6 THEN
      len = 300
   ELSIF maxcor = cor7 THEN
      len = 350
   ELSIF maxcor = cor8 THEN
      len = 400
   ELSIF maxcor = cor9 THEN
      len = 450
   ELSE
      len = 500
   ENDIF
ELSE
   len = lengthinput
ENDIF
//-------------------------------------------------------------
// ===== Z-Score calculations =====
//-------------------------------------------------------------
a = Average[len](close)        // mean
b = Std[len](close)            // standard deviation
c = (close - a) / b            // z-score

sma = Average[len](c)          // smoothed z-score

IF manualprice = 1 THEN
   z0 = a + pullbacksd * b
ELSE
   z0 = a + sma * b
ENDIF

highz = Highest[len](c)
lowz  = Lowest[len](c)

//-------------------------------------------------------------
// ===== Buy & Sell logic =====
//-------------------------------------------------------------
ONCE buywait = 0
ONCE sellwait = 0

lowestprice = Lowest[20](low)
highestprice = Highest[20](high)
atr = AverageTrueRange[14](close)

// ---- Buy signal ----
IF c <= lowz THEN
   buywait = buywait + 1
   IF buywait > 8 THEN
      DRAWARROWUP(barindex,low-0.35*atr)coloured(0,255,0)
      DRAWTEXT("Z-Score= #c#", barindex, lowestprice - 3*atr) COLOURED("darkgreen")
      DRAWTEXT("Cor Length= #len#", barindex, lowestprice - 2*atr) COLOURED("darkgreen")
      DRAWTEXT("TP= #z0#", barindex, lowestprice - atr) COLOURED("darkgreen")
      IF plottgt = 1 THEN
         DRAWSEGMENT(barindex, z0, barindex+100, z0) coloured(0,255,0)
      ENDIF
      buywait = 0
   ENDIF
ENDIF

// ---- Sell signal ----
IF c >= highz THEN
   sellwait = sellwait + 1
   IF sellwait > 8 THEN
      DRAWARROWDOWN(barindex,high+0.35*atr)coloured(255,0,0)
      DRAWTEXT("Z-Score= #c#", barindex, highestprice + 3*atr) COLOURED("darkred")
      DRAWTEXT("Cor Length= #len#", barindex, highestprice + 2*atr) COLOURED("darkred")
      DRAWTEXT("TP= #z0#", barindex, highestprice + 1*atr) COLOURED("darkred")
      IF plottgt = 1 THEN
         DRAWSEGMENT(barindex, z0, barindex+100, z0) COLOURED(255,0,0)
      ENDIF
      sellwait = 0
   ENDIF
ENDIF
//-------------------------------------------------------------
// ===== Bar colors =====
//-------------------------------------------------------------
overbought = c >= 2
oversold   = c <= -2
if barcolor then
   IF overbought THEN
      DRAWCANDLE(open,high,low,close) COLOURED("fuchsia")
   ELSIF oversold THEN
      DRAWCANDLE(open,high,low,close) COLOURED("black")
   ENDIF
endif
//-------------------------------------------------------------
return