Price Data Components
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Rank_sautopcorrelation
//------------------------------------------------------------------
#property copyright "www.forex-tsd.com"
#property link "www.forex-tsd.com"
//------------------------------------------------------------------
#property indicator_separate_window
#property indicator_buffers 6
#property indicator_plots 5
#property indicator_label1 "Spearman levels"
#property indicator_type1 DRAW_FILLING
#property indicator_color1 clrLimeGreen,clrPaleVioletRed
#property indicator_label2 "Spearman level up"
#property indicator_type2 DRAW_LINE
#property indicator_color2 clrLimeGreen
#property indicator_style2 STYLE_DOT
#property indicator_label3 "Spearman middle level"
#property indicator_type3 DRAW_LINE
#property indicator_color3 clrSilver
#property indicator_style3 STYLE_DOT
#property indicator_label4 "Spearman level down"
#property indicator_type4 DRAW_LINE
#property indicator_color4 clrPaleVioletRed
#property indicator_style4 STYLE_DOT
#property indicator_label5 "Spearman"
#property indicator_type5 DRAW_LINE
#property indicator_color5 clrDimGray
#property indicator_width5 2
#property indicator_minimum -1
#property indicator_maximum +1
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 enCorrType
{
cor_spe, // Spearman rank correlation
cor_pea // Pearson rank correlation
};
input int Rank = 32; // Rank period
input enCorrType CorType = cor_spe; // Correlation type
input enPrices Price = pr_close; // Price to use
input int flLookBack = 25; // Floating levels look back period (<0 for fixed levels, 0 to use rank period)
input double flLevelUp = 90; // Floating levels up level %
input double flLevelDown = 10; // Floating levels down level %
//
//
//
//
//
double sr[],levelup[],levelmi[],leveldn[],fill1[],fill2[];
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
int OnInit()
{
SetIndexBuffer(0,fill1 ,INDICATOR_DATA);
SetIndexBuffer(1,fill2 ,INDICATOR_DATA);
SetIndexBuffer(2,levelup,INDICATOR_DATA);
SetIndexBuffer(3,levelmi,INDICATOR_DATA);
SetIndexBuffer(4,leveldn,INDICATOR_DATA);
SetIndexBuffer(5,sr ,INDICATOR_DATA);
IndicatorSetString(INDICATOR_SHORTNAME,getCorrelationName(CorType)+" rank (auto)correlation ("+(string)Rank+","+(string)+flLookBack+")");
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 flperiod = flLookBack; if (flperiod==0) flperiod = Rank;
for (int i=(int)MathMax(prev_calculated-1,0); i<rates_total && !IsStopped(); i++)
{
sr[i] = iCorrelation(CorType,getPrice(Price,open,close,high,low,i,rates_total),Rank,i,rates_total);
if (flperiod>0)
{
double min = sr[i];
double max = sr[i];
for (int k=1; k<flperiod && (i-k)>=0; k++)
{
min = MathMin(sr[i-k],min);
max = MathMax(sr[i-k],max);
}
double range = max-min;
levelup[i] = min+flLevelUp*range/100.0;
leveldn[i] = min+flLevelDown*range/100.0;
levelmi[i] = min+0.5*range;
}
else
{
levelup[i] = 2*flLevelUp /100.0-1;
leveldn[i] = 2*flLevelDown/100.0-1;
levelmi[i] = (levelup[i]+leveldn[i])*0.5;
}
fill1[i] = fill2[i] = sr[i];
if (sr[i]>levelup[i]) fill2[i] = levelup[i];
if (sr[i]<leveldn[i]) fill2[i] = leveldn[i];
}
return(rates_total);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
//
#define priceInstances 1
double workHa[][priceInstances*4];
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); int r=i; instanceNo*=4;
//
//
//
//
//
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 <haClose) { workHa[r][instanceNo+0] = haLow; workHa[r][instanceNo+1] = haHigh; }
else { workHa[r][instanceNo+0] = haHigh; workHa[r][instanceNo+1] = haLow; }
workHa[r][instanceNo+2] = haOpen;
workHa[r][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);
}
//------------------------------------------------------------------
//
//------------------------------------------------------------------
//
//
//
//
//
string getCorrelationName(int mode)
{
switch (mode)
{
case cor_pea : return("Pearson");
case cor_spe : return("Spearman");
default : return("");
}
}
#define correlationInstances 4
double iCorrelation(int mode, double price, double length, int r, int bars, int instanceNo=0)
{
switch (mode)
{
case cor_pea : return(iPearson(price,(int)length,r,bars,instanceNo));
case cor_spe : return(iSpearman(price,(int)length,r,bars,instanceNo));
default : return(price);
}
}
//
//
//
//
//
double workPearson[][correlationInstances];
double iPearson(double value, int period, int i, int bars, int instanceNo=0)
{
if (ArrayRange(workPearson,0)!=bars) ArrayResize(workPearson,bars); workPearson[i][instanceNo]=value;
//
//
//
//
//
double SumXY=0; double SumXX=0; double SumYY=0; double SumY=0; double SumX=0;
for(int k=0; k<period && (i-k)>=0; k++)
{
double val = workPearson[i-k][instanceNo];
SumX += val;
SumY += k;
SumXX += val*val;
SumYY += k*k;
SumXY += val*k;
}
double SXY = period*SumXY-SumY*SumX;
double SXXYY = (period*SumXX-SumX*SumX)*(period*SumYY-SumY*SumY);
double result = (SXXYY!=0) ? -SXY/(MathSqrt(MathAbs(SXXYY))) : 0;
return(result);
}
//
//
//
//
//
double workSpearman[][correlationInstances];
double iSpearman(double value, int period, int i, int bars, int instanceNo=0)
{
if (ArrayRange(workSpearman,0)!=bars) ArrayResize(workSpearman,bars); workSpearman[i][instanceNo]=value;
//
//
//
//
//
double total=0;
double data[]; ArrayResize(data, period); ArrayInitialize(data,0);
for (int k=0; k<period && (i-k)>=0; k++) data[k] = workSpearman[i-k][instanceNo];
for (int k=0; k<period; k++) { int max = ArrayMaximum(data); total += (max-k)*(max-k); data[max] = 0; }
return(1.0-6.0*total/(period*(period*period-1.0)));
}
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