Hi Nicolas, Moderators & Advanced Users,
I am really hoping that someone can come up with a clever way of converting the below John Ehlers indicator found in the September 2016 TASC magazine to PRT code. Despite my best efforts I have not come up with a way to achieve this and believe me I have tried.
I know that PRT does not support arrays but I have overcome this problem by defining multiple variables to “simulate” / “cater for” an array maximum of 60 (even 48 will do). I have also tried to remove as many loops as possible but haven’t managed to overcome the infinite loop / too many iterations problem. Any ideas or help will be greatly appreciated?
An extract of the TradeStation code that I am desperate to convert to ProRealTime is below:
Many thanks and praise to anyone willing to take on this challenge 😉
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inputs:
EnhanceResolution( false ) ;
variables:
AvgLength( 3 ), M( 0 ), N( 0 ), X( 0 ),
Y( 0 ), alpha1( 0 ), HP( 0 ), a1( 0 ),b1( 0 ),
c1( 0 ), c2( 0 ), c3( 0 ), Filt( 0 ), Lag( 0 ),
count( 0 ), Sx( 0 ), Sy( 0 ), Sxx( 0 ),
Syy( 0 ), Sxy( 0 ), Period( 0 ), Sp( 0 ),
Spx( 0 ), MaxPwr( 0 ), PeakPwr( 0 ),
DominantCycle( 0 ) ;
arrays: Corr[70]( 0 ), CosinePart[70]( 0 ),
SinePart[70]( 0 ),SqSum[70]( 0 ),
R[70, 2]( 0 ), Pwr[70]( 0 ) ;
//Highpass Filter and SuperSmoother
//Filter together form a Roofing Filter
//Highpass Filter
alpha1 = ( 1 – Sine ( 360 / 48 ) )
/ Cosine( 360 / 48 ) ;
HP = .5 * ( 1 + alpha1 )
* ( Close – Close[1] ) + alpha1 * HP[1] ;
//Smooth with a SuperSmoother Filter
a1 = ExpValue( -1.414 * 3.14159 / 8 ) ;
b1 = 2 * a1 * Cosine( 1.414 * 180 / 8 ) ;
c2 = b1 ;
c3 = -a1 * a1 ;
c1 = 1 – c2 – c3 ;
Filt = c1 * ( HP + HP[1] ) / 2
+ c2 * Filt[1] + c3 * Filt[2] ;
//Pearson correlation for each value of lag
for Lag = 0 to 48
begin
//Set the averaging length as M
M = AvgLength ;
If AvgLength = 0 then
M = Lag ;
Sx = 0 ;
Sy = 0 ;
Sxx = 0 ;
Syy = 0 ;
Sxy = 0 ;
for count = 0 to M – 1
begin
X = Filt[count] ;
Y = Filt[Lag + count] ;
Sx = Sx + X ;
Sy = Sy + Y ;
Sxx = Sxx + X * X ;
Sxy = Sxy + X * Y ;
Syy = Syy + Y * Y ;
end ;
if ( M * Sxx – Sx * Sx ) * ( M * Syy – Sy * Sy ) > 0 then
Corr[Lag] = ( M * Sxy – Sx * Sy )
/ SquareRoot( ( M * Sxx – Sx * Sx )
* ( M * Syy – Sy * Sy ) ) ;
end ;
//Compute the Fourier Transform for each Correlation
for Period = 8 to 48
begin
CosinePart[Period] = 0;
SinePart[Period] = 0;
For N = 3 to 48
Begin
CosinePart[Period] = CosinePart[Period] +
Corr[N]*Cosine(360*N / Period);
SinePart[Period] = SinePart[Period]
+ Corr[N]*Sine(360*N / Period);
End;
SqSum[Period] = CosinePart[Period]*CosinePart[Period]
+ SinePart[Period]*SinePart[Period];
End ;
For Period = 8 to 48
Begin
R[Period, 2] = R[Period, 1];
R[Period, 1] = .2*SqSum[Period]*SqSum[Period]
+ .8*R[Period,2];
End;
//Find Maximum Power Level for Normalization
MaxPwr = 0;
For Period = 8 to 48
begin
If R[Period, 1] > MaxPwr then
MaxPwr = R[Period, 1];
End;
For Period = 8 to 48
Begin
Pwr[Period] = R[Period, 1] / MaxPwr;
End;
//Optionally increase Display Resolution
//by raising the NormPwr to a higher
//mathematically power (since the maximum
//amplitude is unity, cubing all
//amplitudes further reduces the smaller ones).
If EnhanceResolution = True then
Begin
For Period = 8 to 48
Begin
Pwr[Period] = Power(Pwr[Period], 3);
End;
End;
//Compute the dominant cycle using
//the CG of the spectrum
DominantCycle = 0;
PeakPwr = 0;
For Period = 8 to 48
Begin
If Pwr[Period] > PeakPwr then
PeakPwr = Pwr[Period];
End;
Spx = 0;
Sp = 0;
For Period = 8 to 48
Begin
If PeakPwr >= .25 and Pwr[Period] >= .25 then
Begin
Spx = Spx + Period*Pwr[Period];
Sp = Sp + Pwr[Period];
End;
End;
If Sp <> 0 then
DominantCycle = Spx / Sp;
If Sp < .25 then
DominantCycle = DominantCycle[1];
if DominantCycle < 1 then DominantCycle = 1 ;
Print( Bardatetime.ToString(), ” | “, Ceiling( DominantCycle ) ) ;
