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Herman,
Here is the historical Range1.
RANGE1=0;
for(Q=300;Q<LastValue(Cum(1));Q++)
{
DAT=LastValue(ValueWhen(Cum(1)==Q,DateNum()));
bestequity = 0;bestrange = 0;
for( range = 10; range < 100; range =RANGE+10 )
{
Buy = Cross( Close, EMA( Close, range ) );
Sell = Cross( EMA( Close, range ), Close );
LastEquity = LastValue( Equity(1,3,1000103,DAT) );
if( LastEquity > bestequity )
{
bestequity = LastEquity;
bestrange = range;
}
}
Range1[Q] = BestRange;
Buy = Cross( Close, EMA( Close, range ) );
Sell = Cross( EMA( Close, range ), Close );
}
Plot(range1,"Historical Range",1,1);
Flat or almost flat stocks are interesting.
It is slow but interesting.
Dimitris Tsokakis
--- In amibroker@xxxxxxxxxxxxxxx, "Herman vandenBergen" <psytek@xxxx>
wrote:
> Hello DT,
>
> The procedure I suggested simply cascades inline optimization loops
(one
> stage only) as shown below, carrying forward optimized variables. I
just
> copied this code from my archive without verification but hopefully
it
> conveys the idea.
>
> herman
>
> // Inline optimization
> bestequity = 0;
> bestrange = 0;
> // optimization loop
> for( range = 10; range < 20; range ++ )
> {
> Buy = Cross( Close, EMA( Close, range ) );
> Sell = Cross( EMA( Close, range ), Close );
> LastEquity = LastValue( Equity() );
> if( LastEquity > bestequity )
> {
> bestequity = LastEquity;
> bestrange = range;
> }
> E=Equity(1);
> AddColumn( BestRange, "R="+WriteVal(range,1.0));
> AddColumn( BestEquity, "E="+WriteVal(range,1.0));
> }
> Range = BestRange;
> Buy = Cross( Close, EMA( Close, range ) );
> Sell = Cross( EMA( Close, range ), Close );
> Filter = Status("LastBarInTest");
> herman.
>
> -----Original Message-----
> From: DIMITRIS TSOKAKIS [mailto:TSOKAKIS@x...]
> Sent: November 13, 2003 4:56 PM
> To: amibroker@xxxxxxxxxxxxxxx
> Subject: [amibroker] Re: Help on automating optimization (genetic
> algorithms)
>
>
> Herman,
> How did you do it ?
> Because:
> The
> x=Optimize("x",10,10,50,10);b=Optimize("b",10,10,50,10);
> Buy=Cross(StochD(x),b);
> Sell=Cross(80,StochD(x));Short=Sell;Cover=Buy;
> needs 25 steps.
> The
> x=Optimize("x",10,10,50,10);
> for(b=10;b<=50;b=b+10)
> {
> Buy=Cross(StochD(x),b);
> Sell=Cross(80,StochD(x));Short=Sell;Cover=Buy;
> }
> needs 5 steps, because the results will have *only* the last b=50
> choice.
> The
> for(b=10;b<50;b=b+10)
> {
> x=Optimize("x",10,10,50,10);
> Buy=Cross(StochD(x),b);
> Sell=Cross(80,StochD(x));Short=Sell;Cover=Buy;
> }
> needs 625 [!!] steps and *does not* give the optimal solution.
> Is it possible to give an example?
> Dimitris Tsokakis
> --- In amibroker@xxxxxxxxxxxxxxx, "Steve Dugas" <sjdugas@xxxx>
wrote:
> > Hi,
> >
> > True, unfortunately. A simple way to look at it is by counting the
> number of
> > combinations tested. For example, say I am optimizing on 2
> variables, each
> > with 10 steps. If I optimize them together, I am testing 10 x 10
or
> 100
> > combinations. If I optimize them one at a time, I am testing 10 +
> 10 or 20
> > combinatins. The second way will certainly be faster, because I am
> only
> > testing 20% of the possible combinations. The true optimal results
> are
> > likely to lie somewhere in the 80% of combinations that I have not
> tested..
> > The more variables used, the % of possible combinations actually
> tested
> > continues to drop. For 3 variables, 30/1000 or only 3%. For 4
> variables,
> > 40/10,000 or only 0.4 %, etc...
> >
> > Steve
> >
> > ----- Original Message -----
> > From: "DIMITRIS TSOKAKIS" <TSOKAKIS@xxxx>
> > To: <amibroker@xxxxxxxxxxxxxxx>
> > Sent: Thursday, November 13, 2003 12:47 AM
> > Subject: [amibroker] Re: Help on automating optimization (genetic
> > algorithms)
> >
> >
> > > Gary,
> > > The procedure you describe does not make any sense.
> > > In the simple example
> > > x=Optimize("x",10,10,100,10);
> > > b=Optimize("b",10,10,50,10);
> > > s=Optimize("s",50,50,90,10);
> > > Buy=Cross(StochD(x),b);
> > > Sell=Cross(s,StochD(x));
> > > the optimal solution is [30,20,80]
> > > If I optimize first the x, ie
> > > x=Optimize("x",10,10,100,10);
> > > b=10;//Optimize("b",10,10,50,10);
> > > s=50;//Optimize("s",50,50,90,10);
> > > Buy=Cross(StochD(x),b);
> > > Sell=Cross(s,StochD(x));
> > > [optimal x=80]
> > > then the b, ie
> > > x=80;//Optimize("x",10,10,100,10);
> > > b=Optimize("b",10,10,50,10);
> > > s=50;//Optimize("s",50,50,90,10);
> > > Buy=Cross(StochD(x),b);
> > > Sell=Cross(s,StochD(x));
> > > [optimal b=10]
> > > and finally the s, ie
> > > x=80;//Optimize("x",10,10,100,10);
> > > b=10;//Optimize("b",10,10,50,10);
> > > s=Optimize("s",50,50,90,10);
> > > Buy=Cross(StochD(x),b);
> > > Sell=Cross(s,StochD(x));
> > > [optimal s=90]
> > > I will have the [80,10,90] solution, which is far from the
actual
> > > optimal [30,20,80]
> > > Unfortunately, the optimal[x,b,s] *is not* the optimal[optimal
> [optimal
> > > [x],b],s], consequently this procedure is wrong from the
> beginning.
> > > There is no chain rule for composite optimizations and you will
> have
> > > very few probabilities to find the optimal.
> > > Another basic issue: You do not even have the
> > > optimal[optimal[x],y] equal to optimal[optimal[y],x]
> > > property, both will be far from the optimal[x,y] and you can not
> even
> > > interchange the priorities.
> > >
> > > Herman,
> > > The results of cascade optimizations will not give the optimal
> > > result, even if you save some time. As for this last property, I
> do
> > > not think you save any time, optimization is an internal loop,
as
> far
> > > as I understand...
> > > Dimitris Tsokakis
> > >
> > >
> > > --- In amibroker@xxxxxxxxxxxxxxx, "Herman vandenBergen"
> <psytek@xxxx>
> > > wrote:
> > > > You can optimize using for() loops and examples of this method
> have
> > > been
> > > > posted on the list. This would allow you to optimize one
> parameter
> > > first in
> > > > the first loop-optimization and then go on the the next loop-
> > > optimization
> > > > using the result of the first loop-optimization in your
> > > calculations. This
> > > > way you will save lots of time and you can optimize an
unlimited
> > > number of
> > > > parameters without looking at billions of iterations.
> > > >
> > > > happy tinkering,
> > > > herman.
> > > >
> > > >
> > > > -----Original Message-----
> > > > From: Gary A. Serkhoshian [mailto:serkhoshian777@x...]
> > > > Sent: November 13, 2003 7:00 AM
> > > > To: amibroker@xxxxxxxxxxxxxxx
> > > > Subject: [amibroker] Help on automating optimization
(genetic
> > > algorithms)
> > > >
> > > >
> > > > Hi all,
> > > >
> > > > I should preface this e-mail by saying that I'm not trying
to
> > > create a
> > > > Rube Goldberg as I can go through the process ourlined below
> > > manually. So,
> > > > if what I'm suggesting requires a Rube Goldberg type solution
> let
> > > me know,
> > > > and I'll let it go.
> > > >
> > > > Essentially what I'm doing with optimizations over multiple
> > > variables is
> > > > prioritizing variables on importance, and optimizing one
> variable
> > > at a time
> > > > leaving the others static. I just keep iterating through all
> the
> > > variables
> > > > until things stabilize in terms of standard measures. The
> cocktail
> > > party
> > > > term is genetic algorithms, but between us it's necesary as
> > > optimizing over
> > > > multiple variables (4+) will either take too long or worse
> cause
> > > AmiBroker
> > > > to crash due to memory issues.
> > > >
> > > > Here's the punchline. Can you all give some input on ways
to
> > > program this
> > > > in afl so as to automate the process outlined above rather
than
> me
> > > manually
> > > > iterating through as it is labor intensive.
> > > >
> > > > Many thanks,
> > > > Gary
> > > >
> > > >
> > > > --------------------------------------------------------------
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