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smoother_std_adaptive_4
//------------------------------------------------------------------
#property copyright "mladen @ mladenfx@gmail.com"
#property link "www.forex-station.com"
//------------------------------------------------------------------
#property indicator_chart_window
#property indicator_buffers 2
#property indicator_plots 1
#property indicator_label1 "Smoother std adaptive"
#property indicator_type1 DRAW_COLOR_LINE
#property indicator_color1 clrLimeGreen,clrOrange
#property indicator_style1 STYLE_SOLID
#property indicator_width1 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_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
};
enum enStdMethods
{
std_custSam, // Custom - with sample correction
std_custNos // Custom - without sample correction
};
input double SmtPeriod = 15; // Calculation period
input enPrices Price = pr_close; // Price to use
input int AdaptivePeriod = 25; // Period for adapting
input enStdMethods DeviationType = std_custSam; // Deviation calculation type
input int ColorSteps = 50; // Color steps for drawing
input color ColorFrom = clrOrange; // Color down
input color ColorTo = clrLime; // Color Up
double smt[],colorBuffer[];
int cSteps;
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
int OnInit()
{
SetIndexBuffer(0,smt,INDICATOR_DATA);
SetIndexBuffer(1,colorBuffer,INDICATOR_COLOR_INDEX);
cSteps = (ColorSteps>1) ? ColorSteps : 2;
PlotIndexSetInteger(0,PLOT_COLOR_INDEXES,cSteps+1);
for (int i=0;i<cSteps+1;i++)
PlotIndexSetInteger(0,PLOT_LINE_COLOR,i,gradientColor(i,cSteps+1,ColorFrom,ColorTo));
return(0);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
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& volume[],
const int& spread[])
{
if (Bars(_Symbol,_Period)<rates_total) return(-1);
//
//
//
//
//
int i=(int)MathMax(prev_calculated-1,0); for (; i<rates_total && !_StopFlag; i++)
{
double price = getPrice(Price,open,close,high,low,i,rates_total);
double dev = iDeviation(price,AdaptivePeriod,DeviationType==std_custSam,i,rates_total);
double avg = iSma(dev,AdaptivePeriod,rates_total,i,1);
double period = (dev!=0) ? MathMax(SmtPeriod*avg/dev,2) : MathMax(SmtPeriod,1);
//
//
//
//
//
smt[i] = iSmooth(price,period,i,rates_total,0);
int start = MathMax(i-ColorSteps+1,0);
double min = smt[ArrayMinimum(smt,start,ColorSteps)];
double max = smt[ArrayMaximum(smt,start,ColorSteps)];
double col = (max-min)!=0 ? 100*(smt[i]-min)/(max-min) : 50;
colorBuffer[i] = MathFloor(col*cSteps/100.0);
}
return(i);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
double workDev[];
double iDeviation(double value, int length, bool isSample, int i, int bars)
{
if (ArraySize(workDev)!=bars) ArrayResize(workDev,bars); workDev[i] = value;
//
//
//
//
//
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)));
}
//
//
//
//
//
double workSma[][4];
double iSma(double price, int period, int totalBars, int r, int instanceNo=0)
{
if (ArrayRange(workSma,0)!= totalBars) ArrayResize(workSma,totalBars); instanceNo *= 2;
//
//
//
//
//
int k;
workSma[r][instanceNo] = price;
if (r>=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(k=0; k<period && (r-k)>=0; k++) workSma[r][instanceNo+1] += workSma[r-k][instanceNo];
workSma[r][instanceNo+1] /= k; }
return(workSma[r][instanceNo+1]);
}
//
//
//
//
//
double workSmooth[][5];
double iSmooth(double price,double length,int r, int bars, int instanceNo=0)
{
if (ArrayRange(workSmooth,0)!=bars) ArrayResize(workSmooth,bars); instanceNo *= 5;
if(r<=2) { workSmooth[r][instanceNo] = price; workSmooth[r][instanceNo+2] = price; workSmooth[r][instanceNo+4] = price; return(price); }
//
//
//
//
//
double alpha = 0.45*(length-1.0)/(0.45*(length-1.0)+2.0);
workSmooth[r][instanceNo+0] = price+alpha*(workSmooth[r-1][instanceNo]-price);
workSmooth[r][instanceNo+1] = (price - workSmooth[r][instanceNo])*(1-alpha)+alpha*workSmooth[r-1][instanceNo+1];
workSmooth[r][instanceNo+2] = workSmooth[r][instanceNo+0] + workSmooth[r][instanceNo+1];
workSmooth[r][instanceNo+3] = (workSmooth[r][instanceNo+2] - workSmooth[r-1][instanceNo+4])*MathPow(1.0-alpha,2) + MathPow(alpha,2)*workSmooth[r-1][instanceNo+3];
workSmooth[r][instanceNo+4] = workSmooth[r][instanceNo+3] + workSmooth[r-1][instanceNo+4];
return(workSmooth[r][instanceNo+4]);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
//
#define _pricesInstances 3
#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;
//
//
//
//
//
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;
//
//
//
//
//
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);
}
}
//
//
//
//
//
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);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
//
//
//
//
//
color getColor(int stepNo, int totalSteps, color from, color to)
{
double stes = (double)totalSteps-1.0;
double step = (from-to)/(stes);
return((color)round(from-step*stepNo));
}
color gradientColor(int step, int totalSteps, color from, color to)
{
color newBlue = getColor(step,totalSteps,(from & 0XFF0000)>>16,(to & 0XFF0000)>>16)<<16;
color newGreen = getColor(step,totalSteps,(from & 0X00FF00)>> 8,(to & 0X00FF00)>> 8) <<8;
color newRed = getColor(step,totalSteps,(from & 0X0000FF) ,(to & 0X0000FF) ) ;
return(newBlue+newGreen+newRed);
}
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