Machine-Learning-Z-Score-Tradingview

• 09/04/2025 at 10:58 AM #250346
  Ciccarelli Franco

  Richiedo , se è possibile, la traduzione di questo indicatore.

  https://it.tradingview.com/script/oJKrJF4L-Machine-Learning-Z-Score-Buy-and-Sell-SS/

  Grazie

  

  Ciccarelli Franco

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  09/10/2025 at 10:21 AM #250617
  Iván

  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

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  //------------------------------------------------------------- // 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

  

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