_SECTION_BEGIN("Runs Test for Randomness");
/* Implementation of Runs Test for Randomness based on javascript implementation
   by Dr. Hossein Arsham through google search. All Credits duly acknowledged.

	Ported & re-written to AFL by sundarr
*/
function k(x)
{ return 1 / (1+0.2316419*x); }

function HASpre(x)
{ return 1 - (exp(-0.5*x^2)/sqrt(2*3.141592654))*(0.31938153*k(x)-
0.356563782*k(x)^2+1.781477937*k(x)^3-1.821255978*k(x)
^4+1.330274429*k(x)^5); }

function HAS(x)
{ return IIf(x<-6, 0, IIf(x>6, 1, IIf( x>0, HASpre(x), 1 - HASpre(-x)))) ; }

function NORMSDIST(z1)
{
	z=(z1);
	if(z>0) 
		t=z;
	else 
		t=-z;
				
	P1=(1+t*(0.049867347 + t*(0.0211410061 
		+ t*(0.0032776263 + t*(0.0000380036 
		+ t*(0.0000488906 + t*(0.000005383)))))))
		^-16;
	//This is P-value
	p=1-P1/2;
	
	//Rounding the value
	//p=round(100000*p)/100000;
	
	if(z>0)
		t=1-p;
	else
		t=1-(1-p);

	//rounding the value
	t1=round(100000*t);
	t2=t1/100000;	//Rounded P-Value

	return t2;
}

function GetRunsP(Values, E)
{
	//E= Total number of inputs to run the hypothesis on
	//values is array holding the actual input array to process

	N=0;
	N1=0;
	N2=0;
	S=0.0;
	R=1;

	retValue=1;
	RunsP=Null;

	//Calculate the mean
	Mean=MA(Values,E);
	
	//Error check
	if (E<=BarCount)	{
		for(j=E-1;j<BarCount;j++)	{
			x=Mean[j];
			for(i=j-E+1;i<=j;i++)	{
				//if more than mean
				if(Values[i]>x)	{
					N1[j]++;
					a=i;
					while (a>0)	{
						a--;
						if(Values[a]!=x)
							break;
					}//while a>0
					if(Values[a]<x)
						R[j]++;
				}//if values>x		
				// if less than mean
				else if(Values[i]<x)
				{
					N2[j]++;
					a=i;
					while (a>0)	{
						a--;
						if(Values[a]!=x)
							break;
					}//while a>0
					if(Values[a]>x)
						R[j]++;					
				} //else if values<x
			}//for i
			//Compute the expected mean and vaiance of R
			EM=1+(2*N1[j]*N2[j])/(N1[j]+N2[j]); 	//Expected Mean or Mu
			SD1=2*N1[j]*N2[j]*(2*N1[j]*N2[j]-N1[j]-N2[j]);
			SD2=(N1[j]+N2[j])^2;
			SD3=N1[j]+N2[j]-1;
			SD4=SD1/(SD2*SD3);	//Standard Deviation or Sigma
			SD=sqrt(SD4);

			z1=(R[j]-EM)/SD;
			RunsP[j]=NORMSDIST(abs(z1));
			//RunsP[j]=HAS((z1));

		}//for j
	}//if (E<=BarCount)


	return RunsP;

/*
		Conclusions
		t2<0.01 indicates very strong evidence against randomness (strong trend exists)
		t2>=0.01 AND t2<0.05 indicates moderate evidence against randomness (trend exists)
		t2>=0.05 AND t2<0.10 indicates suggestive evidence against randomness (mild trend may exist)
		t2>=0.10 indicates little or no real evidences against randomness (no trend exists)
		Any other (else) t2 value (like negative perhaps?) indicates Strong evidence against randomness (strong trend exists)
*/

}//function GetRunsP()


//Prob1=GetRunsP((O+H+L+C)/4,Param("Prob Period",14,8,200,1));
Prob1=GetRunsP(C,Param("Prob Period",14,8,200,1));

//Plot(((Prob1)),"Prob Slow",ParamColor("Prob Color",colorGrey50),ParamStyle("Prob Style",styleArea));
Plot(0.05,"1",colorRed);
Plot(0.1,"1",colorGrey50);
Plot(0.2,"2",colorGrey50);

AP= MA(Prob1,8);
Plot(AP,"MA of Prob1",colorRed,styleLine|styleArea);

//Plot(log(0.10),"",colorGrey40);
//Plot(log(0.05),"",colorGrey40);

//Prob2=GetRunsP(C,8);
//Plot(Prob2,"Prob Fast",colorRed,styleDashed|styleOwnScale);

//Plot(NORMSDIST(-0.85),"",colorBlue, styleOwnScale);
//Plot(z1,"Z1",colorRed,styleOwnScale);

_SECTION_END();