Créer un screener basé sur volume-supertrand-ai

• 03/05/2024 at 9:53 AM #229219
  francis59

  Bonjour,

  Serait-il possible de créer un screener pour détecter les meilleurs moments d’ACHAT et de VENTE basé sur : https://www.prorealcode.com/prorealtime-indicators/volume-supertrend-ai/ ?

  Si à tout hasard ce n’est pas pertinent, dites-le moi et ne perdez pas votre temps 🙂

  Par avance merci et bonne journée

   

   

  

  francis59

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  03/05/2024 at 3:38 PM #229266
  Iván

  Vous pouvez passer par le bus suivant :

  //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]

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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

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  francis59

  

  Iván

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