Time: 2018-08-23 | Download file:Polarized_Fractal_Efficiency_-_Erkhardt_2.mq4
//------------------------------------------------------------------
#property copyright "mladen"
#property link "mladenfx@gmail.com"
//------------------------------------------------------------------
#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[];
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
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//
//
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);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
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//
//
//
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]);
}
//
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//
//
//
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;
//
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//
//
//
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);
}