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Khamsina11 wrote:
>...I just can't translate it into AFL :-( .
> Unfortunately, it far more difficult for me (loop, array).
here it is (i think). hopefully you have something to compare with.
let me know if it is wrong and i'll have another go at it. use ctrl-R
to call up the PARAM feature. adjust between 1 and 100.
brian and joe, this is not a linear regression and there is no need to
have it explained. it's just code.
\jeff
------------------------------------------------
Len=Param("Len",10,1,100,1);
k=1.253314;
Mean = MA(Avg, Len);
SumSqr=0;
for (i=Len-1;i<BarCount;i++)
{
for (j =0 ;j<Len;j++)
{
X[j] = C[i-j] - Mean[i];
if(j==0) MaxY=MinY=Y[j]=X[j];
else
{
Y[j] = Y[j-1] + X[j];
if(Y[j] > MaxY) MaxY = Y[j];
if (Y[j] < MinY) MinY = Y[j];
}
SumSqr[i] = SumSqr[i] + X[j]^2;
}
Rng[i]= MaxY - MinY;
}
scale=sqrt(SumSqr/Len);
Estmtr = log(Rng/(k * Scale)) / log(Len);
Plot(Estmtr,"Estmtr",colorBlack,styleLine);
---------------------------------------------------------
original code:
> Inputs: Len(Numeric);
> Vars: Mean(0), j(0), k(1.253314), SumSqr(0), Scale(0), MaxY(0),
> MinY(0), Rng(0);
> Arrays: X[100](0), Y[100](0);
>
> Mean = Average((h+c+l)/3, Len);
> SumSqr = 0;
> for j = 0 to Len - 1 begin
> X[j] = C[j] - Mean;
> SumSqr = SumSqr + X[j] * X[j];
> end;
> Scale = SquareRoot(SumSqr / Len);
> Y[0] = X[0];
> MaxY = X[0];
> MinY = X[0];
> for j = 1 to Len - 1 begin
> Y[j] = Y[j - 1] + X[j];
> if Y[j] > MaxY then MaxY = Y[j];
> if Y[j] < MinY then MinY = Y[j];
> end;
> Rng = MaxY - MinY;
> Estmtr = Log(Rng/(k * Scale)) / Log(Len);
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