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Polarized_Fractal_Efficiency_-_Erkhardt_2.mq4

Time: 2018-08-23 | Download file:Polarized_Fractal_Efficiency_-_Erkhardt_2.mq4

//------------------------------------------------------------------
#property  copyright "mladen"
#property  link      "[email protected]"
//------------------------------------------------------------------
#property  indicator_separate_window
#property  indicator_buffers 3
#property indicator_color1   LimeGreen
#property indicator_color2   Orange
#property indicator_color3   Orange
#property indicator_width1   2
#property indicator_width2   2
#property indicator_width3   2

//
//
//
//
//

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_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
};
enum enMaTypes
{
   ma_sma,     // simple moving average - SMA
   ma_ema,     // exponential moving average - EMA
   ma_dsema,   // double smoothed exponential moving average - DSEMA
   ma_dema,    // double exponential moving average - DEMA
   ma_tema,    // tripple exponential moving average - TEMA
   ma_smma,    // smoothed moving average - SMMA
   ma_lwma,    // linear weighted moving average - LWMA
   ma_pwma,    // parabolic weighted moving average - PWMA
   ma_alxma,   // Alexander moving average - ALXMA
   ma_vwma,    // volume weighted moving average - VWMA
   ma_hull,    // Hull moving average
   ma_tma,     // triangular moving average
   ma_sine,    // sine weighted moving average
   ma_linr,    // linear regression value
   ma_ie2,     // IE/2
   ma_nlma,    // non lag moving average
   ma_zlma,    // zero lag moving average
   ma_lead,    // leader exponential moving average
   ma_ssm,     // super smoother
   ma_smoo     // smoother
};

extern int       PFEPeriod    = 32;
extern enPrices  PFEPrice     = pr_close;
extern int       Smooth       = 10;
extern enMaTypes SmoothMethod = ma_smoo;

double buffer1[];
double buffer2[];
double pfeda[];
double pfedb[];
double slope[];


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

int init()
{
   IndicatorBuffers(4);
   SetIndexBuffer(0,buffer1);
   SetIndexBuffer(1,pfeda);
   SetIndexBuffer(2,pfedb);
   SetIndexBuffer(3,slope);
   IndicatorShortName("Polarized fractal efficiency Erkhardt ("+PFEPeriod+","+Smooth+","+getAverageName(SmoothMethod)+")");
   return(0);
}

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

double mom[];
double div[];
double inp[];
int start()
{
   int i,r,counted_bars=IndicatorCounted();
      if(counted_bars<0) return(-1);
      if(counted_bars>0) counted_bars--;
           int limit=MathMin(Bars-counted_bars,Bars-1);
           if (ArraySize(mom)!=Bars) ArrayResize(mom,Bars);
           if (ArraySize(div)!=Bars) ArrayResize(div,Bars);
           if (ArraySize(inp)!=Bars) ArrayResize(inp,Bars);

   //
   //
   //
   //
   //
   
   if (slope[limit]==-1) CleanPoint(limit,pfeda,pfedb);
   for(i = limit,r=Bars-i-1; i >= 0; i--,r++)
   {
      inp[r] = getPrice(PFEPrice,Open,Close,High,Low,i);

      if (ibuffer1[i+1]) slope[i] = 1;
      if (buffer1[i]=period)
          workSma[r][instanceNo+1] = workSma[r-1][instanceNo+1]+(workSma[r][instanceNo]-workSma[r-period][instanceNo])/period;
   else { workSma[r][instanceNo+1] = 0; for(int k=0; k=0; k++) workSma[r][instanceNo+1] += workSma[r-k][instanceNo];  
          workSma[r][instanceNo+1] /= k; }
   return(workSma[r][instanceNo+1]);
}

//
//
//
//
//

double workEma[][_maWorkBufferx1];
double iEma(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workEma,0)!= Bars) ArrayResize(workEma,Bars);

   //
   //
   //
   //
   //
      
   double alpha = 2.0 / (1.0+period);
          workEma[r][instanceNo] = workEma[r-1][instanceNo]+alpha*(price-workEma[r-1][instanceNo]);
   return(workEma[r][instanceNo]);
}

//
//
//
//
//

double workDsema[][_maWorkBufferx2];
#define _ema1 0
#define _ema2 1

double iDsema(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workDsema,0)!= Bars) ArrayResize(workDsema,Bars); instanceNo*=2;

   //
   //
   //
   //
   //
      
   double alpha = 2.0 /(1.0+MathSqrt(period));
          workDsema[r][_ema1+instanceNo] = workDsema[r-1][_ema1+instanceNo]+alpha*(price                         -workDsema[r-1][_ema1+instanceNo]);
          workDsema[r][_ema2+instanceNo] = workDsema[r-1][_ema2+instanceNo]+alpha*(workDsema[r][_ema1+instanceNo]-workDsema[r-1][_ema2+instanceNo]);
   return(workDsema[r][_ema2+instanceNo]);
}

//
//
//
//
//

double workDema[][_maWorkBufferx2];
#define _dema1 0
#define _dema2 1

double iDema(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workDema,0)!= Bars) ArrayResize(workDema,Bars); instanceNo*=2;

   //
   //
   //
   //
   //
      
   double alpha = 2.0 / (1.0+period);
          workDema[r][_dema1+instanceNo] = workDema[r-1][_dema1+instanceNo]+alpha*(price                         -workDema[r-1][_dema1+instanceNo]);
          workDema[r][_dema2+instanceNo] = workDema[r-1][_dema2+instanceNo]+alpha*(workDema[r][_dema1+instanceNo]-workDema[r-1][_dema2+instanceNo]);
   return(workDema[r][_dema1+instanceNo]*2.0-workDema[r][_dema2+instanceNo]);
}

//
//
//
//
//

double workTema[][_maWorkBufferx3];
#define _tema1 0
#define _tema2 1
#define _tema3 2

double iTema(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workTema,0)!= Bars) ArrayResize(workTema,Bars); instanceNo*=3;

   //
   //
   //
   //
   //
      
   double alpha = 2.0 / (1.0+period);
          workTema[r][_tema1+instanceNo] = workTema[r-1][_tema1+instanceNo]+alpha*(price                         -workTema[r-1][_tema1+instanceNo]);
          workTema[r][_tema2+instanceNo] = workTema[r-1][_tema2+instanceNo]+alpha*(workTema[r][_tema1+instanceNo]-workTema[r-1][_tema2+instanceNo]);
          workTema[r][_tema3+instanceNo] = workTema[r-1][_tema3+instanceNo]+alpha*(workTema[r][_tema2+instanceNo]-workTema[r-1][_tema3+instanceNo]);
   return(workTema[r][_tema3+instanceNo]+3.0*(workTema[r][_tema1+instanceNo]-workTema[r][_tema2+instanceNo]));
}

//
//
//
//
//

double workSmma[][_maWorkBufferx1];
double iSmma(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workSmma,0)!= Bars) ArrayResize(workSmma,Bars);

   //
   //
   //
   //
   //

   if (r=0; k++)
      {
         double weight = period-k;
                sumw  += weight;
                sum   += weight*workLwma[r-k][instanceNo];  
      }             
      return(sum/sumw);
}

//
//
//
//
//

double workLwmp[][_maWorkBufferx1];
double iLwmp(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workLwmp,0)!= Bars) ArrayResize(workLwmp,Bars);
   
   //
   //
   //
   //
   //
   
   workLwmp[r][instanceNo] = price;
      double sumw = period*period;
      double sum  = sumw*price;

      for(int k=1; k=0; k++)
      {
         double weight = (period-k)*(period-k);
                sumw  += weight;
                sum   += weight*workLwmp[r-k][instanceNo];  
      }             
      return(sum/sumw);
}

//
//
//
//
//

double workAlex[][_maWorkBufferx1];
double iAlex(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workAlex,0)!= Bars) ArrayResize(workAlex,Bars);
   if (period<4) return(price);
   
   //
   //
   //
   //
   //

   workAlex[r][instanceNo] = price;
      double sumw = period-2;
      double sum  = sumw*price;

      for(int k=1; k=0; k++)
      {
         double weight = period-k-2;
                sumw  += weight;
                sum   += weight*workAlex[r-k][instanceNo];  
      }             
      return(sum/sumw);
}

//
//
//
//
//

double workTma[][_maWorkBufferx1];
double iTma(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workTma,0)!= Bars) ArrayResize(workTma,Bars);
   
   //
   //
   //
   //
   //
   
   workTma[r][instanceNo] = price;

      double half = (period+1.0)/2.0;
      double sum  = price;
      double sumw = 1;

      for(int k=1; k=0; k++)
      {
         double weight = k+1; if (weight > half) weight = period-k;
                sumw  += weight;
                sum   += weight*workTma[r-k][instanceNo];  
      }             
      return(sum/sumw);
}

//
//
//
//
//

double workSineWMA[][_maWorkBufferx1];
#define Pi 3.14159265358979323846264338327950288

double iSineWMA(double price, int period, int r, int instanceNo=0)
{
   if (period<1) return(price);
   if (ArrayRange(workSineWMA,0)!= Bars) ArrayResize(workSineWMA,Bars);
   
   //
   //
   //
   //
   //
   
   workSineWMA[r][instanceNo] = price;
      double sum  = 0;
      double sumw = 0;
  
      for(int k=0; k=0; k++)
      { 
         double weight = MathSin(Pi*(k+1.0)/(period+1.0));
                sumw  += weight;
                sum   += weight*workSineWMA[r-k][instanceNo]; 
      }
      return(sum/sumw);
}

//
//
//
//
//

double workWwma[][_maWorkBufferx1];
double iWwma(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workWwma,0)!= Bars) ArrayResize(workWwma,Bars);
   
   //
   //
   //
   //
   //
   
   workWwma[r][instanceNo] = price;
      int    i    = Bars-r-1;
      double sumw = Volume[i];
      double sum  = sumw*price;

      for(int k=1; k=0; k++)
      {
         double weight = Volume[i+k];
                sumw  += weight;
                sum   += weight*workWwma[r-k][instanceNo];  
      }             
      return(sum/sumw);
}

//
//
//
//
//

double workHull[][_maWorkBufferx2];
double iHull(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workHull,0)!= Bars) ArrayResize(workHull,Bars);

   //
   //
   //
   //
   //

      int HmaPeriod  = MathMax(period,2);
      int HalfPeriod = MathFloor(HmaPeriod/2);
      int HullPeriod = MathFloor(MathSqrt(HmaPeriod));
      double hma,hmw,weight; instanceNo *= 2;

         workHull[r][instanceNo] = price;

         //
         //
         //
         //
         //
               
         hmw = HalfPeriod; hma = hmw*price; 
            for(int k=1; k=0; k++)
            {
               weight = HalfPeriod-k;
               hmw   += weight;
               hma   += weight*workHull[r-k][instanceNo];  
            }             
            workHull[r][instanceNo+1] = 2.0*hma/hmw;

         hmw = HmaPeriod; hma = hmw*price; 
            for(k=1; k=0; k++)
            {
               weight = HmaPeriod-k;
               hmw   += weight;
               hma   += weight*workHull[r-k][instanceNo];
            }             
            workHull[r][instanceNo+1] -= hma/hmw;

         //
         //
         //
         //
         //
         
         hmw = HullPeriod; hma = hmw*workHull[r][instanceNo+1];
            for(k=1; k=0; k++)
            {
               weight = HullPeriod-k;
               hmw   += weight;
               hma   += weight*workHull[r-k][1+instanceNo];  
            }
   return(hma/hmw);
}

//
//
//
//
//

double workLinr[][_maWorkBufferx1];
double iLinr(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workLinr,0)!= Bars) ArrayResize(workLinr,Bars);

   //
   //
   //
   //
   //
   
      period = MathMax(period,1);
      workLinr[r][instanceNo] = price;
         double lwmw = period; double lwma = lwmw*price;
         double sma  = price;
         for(int k=1; k=0; k++)
         {
            double weight = period-k;
                   lwmw  += weight;
                   lwma  += weight*workLinr[r-k][instanceNo];  
                   sma   +=        workLinr[r-k][instanceNo];
         }             
   
   return(3.0*lwma/lwmw-2.0*sma/period);
}

//
//
//
//
//

double workIe2[][_maWorkBufferx1];
double iIe2(double price, double period, int r, int instanceNo=0)
{
   if (ArrayRange(workIe2,0)!= Bars) ArrayResize(workIe2,Bars);

   //
   //
   //
   //
   //
   
      period = MathMax(period,1);
      workIe2[r][instanceNo] = price;
         double sumx=0, sumxx=0, sumxy=0, sumy=0;
         for (int k=0; k0)
   {
      double sum = 0;
           for (k=0; k < nlmvalues[_len][instanceNo]; k++) sum += nlmalphas[k][instanceNo]*nlmprices[r-k][instanceNo];
           return( sum / nlmvalues[_weight][instanceNo]);
   }
   else return(0);           
}

//+------------------------------------------------------------------
//|                                                                  
//+------------------------------------------------------------------
//
//
//
//
//
//

double workHa[][4];
double getPrice(int price, const double& open[], const double& close[], const double& high[], const double& low[], int i, int instanceNo=0)
{
  if (price>=pr_haclose && price<=pr_hatbiased)
   {
      if (ArrayRange(workHa,0)!= Bars) ArrayResize(workHa,Bars);
         int r = Bars-i-1;
         
         //
         //
         //
         //
         //
         
         double haOpen;
         if (r>0)
                haOpen  = (workHa[r-1][instanceNo+2] + workHa[r-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  haOpen)
                     return((haHigh+haClose)/2.0);
               else  return((haLow+haClose)/2.0);        
         }
   }
   
   //
   //
   //
   //
   //
   
   switch (price)
   {
      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);        
   }
   return(0);
}

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