vwap_bands

ÿþ//------------------------------------------------------------------

#property copyright "© mladen, 2016, MetaQuotes Software Corp."

#property link      "www.forex-tsd.com, www.mql5.com"

#property version   "1.00"

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#property indicator_chart_window

#property indicator_buffers 8

#property indicator_plots   7

#property indicator_type1   DRAW_LINE

#property indicator_color1  clrLimeGreen

#property indicator_width1  2

#property indicator_type2   DRAW_LINE

#property indicator_color2  clrLimeGreen

#property indicator_type3   DRAW_LINE

#property indicator_color3  clrLimeGreen

#property indicator_style3  STYLE_DOT

#property indicator_type4   DRAW_LINE

#property indicator_color4  clrSilver

#property indicator_style4  STYLE_DOT

#property indicator_type5   DRAW_LINE

#property indicator_color5  clrSandyBrown

#property indicator_style5  STYLE_DOT

#property indicator_type6   DRAW_LINE

#property indicator_color6  clrSandyBrown

#property indicator_type7   DRAW_LINE

#property indicator_color7  clrSandyBrown

#property indicator_width7  2



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enum enPrices

{

   pr_close,      // Close

   pr_open,       // Open

   pr_high,       // High

   pr_low,        // Low

   pr_median,     // Median

   pr_typical,    // Typical

   pr_weighted,   // Weighted

   pr_average,    // Average (high+low+open+close)/4

   pr_medianb,    // Average median body (open+close)/2

   pr_tbiased,    // Trend biased price

   pr_tbiased2,   // Trend biased (extreme) price

   pr_haclose,    // Heiken ashi close

   pr_haopen ,    // Heiken ashi open

   pr_hahigh,     // Heiken ashi high

   pr_halow,      // Heiken ashi low

   pr_hamedian,   // Heiken ashi median

   pr_hatypical,  // Heiken ashi typical

   pr_haweighted, // Heiken ashi weighted

   pr_haaverage,  // Heiken ashi average

   pr_hamedianb,  // Heiken ashi median body

   pr_hatbiased,  // Heiken ashi trend biased price

   pr_hatbiased2  // Heiken ashi trend biased (extreme) price

};



input int        AvgPeriod           = 20;          // Volume weighted average period

input enPrices   Price               = pr_close;    // Price

input bool       UseRealVolume       = false;       // Use real volume?

input bool       DeviationSample     = false;       // Deviation with sample correction?

input double     DeviationMuliplier1 = 1;           // First band(s) deviation

input double     DeviationMuliplier2 = 2;           // Second band(s) deviation

input double     DeviationMuliplier3 = 2.5;         // Third band(s) deviation



double  bandm[],bandu1[],bandu2[],bandu3[],bandd1[],bandd2[],bandd3[],prices[];



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void OnInit()

{

   SetIndexBuffer(0,bandu3 ,INDICATOR_DATA);

   SetIndexBuffer(1,bandu2 ,INDICATOR_DATA);

   SetIndexBuffer(2,bandu1 ,INDICATOR_DATA);

   SetIndexBuffer(3,bandm  ,INDICATOR_DATA);

   SetIndexBuffer(4,bandd1 ,INDICATOR_DATA);

   SetIndexBuffer(5,bandd2 ,INDICATOR_DATA);

   SetIndexBuffer(6,bandd3 ,INDICATOR_DATA);

   SetIndexBuffer(7,prices ,INDICATOR_CALCULATIONS);

      IndicatorSetString(INDICATOR_SHORTNAME,"VWAP bands ("+(string)AvgPeriod+")");

}



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int OnCalculate(const int rates_total,

                const int prev_calculated,

                const datetime& time[],

                const double& open[],

                const double& high[],

                const double& low[],

                const double& close[],

                const long& tick_volume[],

                const long& real_volume[],

                const int& spread[])

{

   if (Bars(_Symbol,_Period)<rates_total) return(-1);

   

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   int i=(int)MathMax(prev_calculated-1,0); for (; i<rates_total && !_StopFlag; i++)

   {

      prices[i] = getPrice(Price,open,close,high,low,i,rates_total);

         double sum1=0,sum2=0, deviation = iDeviation(prices[i],AvgPeriod,DeviationSample,i,rates_total);

		      for (int k=0; k<AvgPeriod && (i-k)>=0; k++)

		      {

		         double volume = (UseRealVolume) ? (double)real_volume[i-k] : (double)tick_volume[i-k];

		            sum1 += volume*prices[i-k];

			         sum2 += volume;

		      }

		      bandm[i]  = (sum2!=0) ? sum1/sum2 : prices[i];

		      bandu1[i] = (DeviationMuliplier1>0) ? bandm[i]+DeviationMuliplier1*deviation : EMPTY_VALUE;

		      bandd1[i] = (DeviationMuliplier1>0) ? bandm[i]-DeviationMuliplier1*deviation : EMPTY_VALUE;

		      bandu2[i] = (DeviationMuliplier2>0) ? bandm[i]+DeviationMuliplier2*deviation : EMPTY_VALUE;

		      bandd2[i] = (DeviationMuliplier2>0) ? bandm[i]-DeviationMuliplier2*deviation : EMPTY_VALUE;

		      bandu3[i] = (DeviationMuliplier3>0) ? bandm[i]+DeviationMuliplier3*deviation : EMPTY_VALUE;

		      bandd3[i] = (DeviationMuliplier3>0) ? bandm[i]-DeviationMuliplier3*deviation : EMPTY_VALUE;

   }         

   return(i);

}



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double workDev[];

double iDeviation(double value, int length, bool isSample, int i, int bars)

{

   if (ArraySize(workDev)!=bars) ArrayResize(workDev,bars); workDev[i] = value;

                 

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      double oldMean   = value;

      double newMean   = value;

      double squares   = 0; int k;

      for (k=1; k<length && (i-k)>=0; k++)

      {

         newMean  = (workDev[i-k]-oldMean)/(k+1)+oldMean;

         squares += (workDev[i-k]-oldMean)*(workDev[i-k]-newMean);

         oldMean  = newMean;

      }

      return(MathSqrt(squares/MathMax(k-isSample,1)));

}



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#define _pricesInstances 1

#define _pricesSize      4

double workHa[][_pricesInstances*_pricesSize];

double getPrice(int tprice, const double& open[], const double& close[], const double& high[], const double& low[], int i,int _bars, int instanceNo=0)

{

  if (tprice>=pr_haclose)

   {

      if (ArrayRange(workHa,0)!= _bars) ArrayResize(workHa,_bars); instanceNo*=_pricesSize;

         

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         double haOpen;

         if (i>0)

                haOpen  = (workHa[i-1][instanceNo+2] + workHa[i-1][instanceNo+3])/2.0;

         else   haOpen  = (open[i]+close[i])/2;

         double haClose = (open[i] + high[i] + low[i] + close[i]) / 4.0;

         double haHigh  = MathMax(high[i], MathMax(haOpen,haClose));

         double haLow   = MathMin(low[i] , MathMin(haOpen,haClose));



         if(haOpen  <haClose) { workHa[i][instanceNo+0] = haLow;  workHa[i][instanceNo+1] = haHigh; } 

         else                 { workHa[i][instanceNo+0] = haHigh; workHa[i][instanceNo+1] = haLow;  } 

                                workHa[i][instanceNo+2] = haOpen;

                                workHa[i][instanceNo+3] = haClose;

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         switch (tprice)

         {

            case pr_haclose:     return(haClose);

            case pr_haopen:      return(haOpen);

            case pr_hahigh:      return(haHigh);

            case pr_halow:       return(haLow);

            case pr_hamedian:    return((haHigh+haLow)/2.0);

            case pr_hamedianb:   return((haOpen+haClose)/2.0);

            case pr_hatypical:   return((haHigh+haLow+haClose)/3.0);

            case pr_haweighted:  return((haHigh+haLow+haClose+haClose)/4.0);

            case pr_haaverage:   return((haHigh+haLow+haClose+haOpen)/4.0);

            case pr_hatbiased:

               if (haClose>haOpen)

                     return((haHigh+haClose)/2.0);

               else  return((haLow+haClose)/2.0);        

            case pr_hatbiased2:

               if (haClose>haOpen)  return(haHigh);

               if (haClose<haOpen)  return(haLow);

                                    return(haClose);        

         }

   }

   

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   switch (tprice)

   {

      case pr_close:     return(close[i]);

      case pr_open:      return(open[i]);

      case pr_high:      return(high[i]);

      case pr_low:       return(low[i]);

      case pr_median:    return((high[i]+low[i])/2.0);

      case pr_medianb:   return((open[i]+close[i])/2.0);

      case pr_typical:   return((high[i]+low[i]+close[i])/3.0);

      case pr_weighted:  return((high[i]+low[i]+close[i]+close[i])/4.0);

      case pr_average:   return((high[i]+low[i]+close[i]+open[i])/4.0);

      case pr_tbiased:   

               if (close[i]>open[i])

                     return((high[i]+close[i])/2.0);

               else  return((low[i]+close[i])/2.0);        

      case pr_tbiased2:   

               if (close[i]>open[i]) return(high[i]);

               if (close[i]<open[i]) return(low[i]);

                                     return(close[i]);        

   }

   return(0);

}