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Ton,
I was referring to the newer version published earlier this year "The
Evaluation and Optimization of Trading Strategies".
I have not read the first publication. However, it is my
understanding that the newer one includes the primary ideas
originally published in the older one.
Mike
--- In amibroker@xxxxxxxxxxxxxxx, "Ton Sieverding"
<ton.sieverding@xxx> wrote:
>
> Hi Mike,
>
> About which book of Pardo are you talking. The first one from 1992
or the newest version from the beginning of this year ? I have found
two books. Any suggestion ? I am looking for a little bit more
general background music concerning the Optimization topic and
therefore have the feeling that his first book called 'Design,
Testing, And Optimization Of Trading Systems' could bring me some
extra light in the tunnel ...
>
> Regards, Ton.
>
>
> ----- Original Message -----
> From: Mike
> To: amibroker@xxxxxxxxxxxxxxx
> Sent: Tuesday, April 22, 2008 8:50 PM
> Subject: [amibroker] Re: Difference betwee OOS and IS
>
>
> Louis,
>
> Pardo's book is dedicated to the process of designing a strategy
that
> can then be validated using walk forward analysis.
>
> The sole purpose of the book is teach about the use, and
importance,
> of walk forward. If you are having trouble understanding the
> concepts, this book may help to clarify things for you.
>
> Pardo's book has very little code at all, and none of it is
> AmiBroker. It is strictly a book on the concept, not an
> implementation. The book tells you what to expect, and how to
> interpret the results.
>
> If you are having trouble separating the concepts from the
mechanics
> as presented in Howard's book, or just need more detail about
what
> the process is used for, then Pardo's book may help to answer
your
> questions. You will then better understand the mechanics that
Howard
> presents, and appreciate that AmiBroker now automates the entire
> process.
>
> Mike
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Louis Préfontaine"
> <rockprog80@> wrote:
> >
> > Hi Howard,
> >
> > Thanks for the code and the information. What exactly is doing
the
> CMO
> > Oscillator? I tried to look for information on the web but did
not
> find
> > anything convincing. It sure looks interesting and I will try
to
> find more
> > about it, but right now this look like mystery to me.
> >
> > Something I wasn't sure in your book, is what you mean by
objective
> > function. Do you mean choosing between RAR, or CAR, or K-Ratio,
> etc.? I
> > know this sound like a stupid question after I've read 75% of
the
> book,
> > but... well... I wasn't sure.
> >
> > I will try to follow the steps as you write them below.
However, I
> am still
> > worried about MCS; I mean, in the steps you don't use any random
> > optimization and you said not to worry about them. I say that
> because it
> > seems to me that in the walk-forward it can be easy to get
lucky
> with some
> > very good curve-fitting results. And the more complex the
rules,
> the more
> > chances there is to get a very lucky result! Well, this was my
> whole point:
> > in the walk-forward I only get to see the absolute best return,
and
> if there
> > is no random optimization I can't rule out the luck factor!
> >
> > Thanks!
> >
> > Louis
> >
> > p.s. Mike, thanks for the suggestion. Is Pardo's book really
good
> and is
> > using afl code or codes that can be implemented easily to
Amibroker?
> >
> > 2008/4/22, Howard B <howardbandy@>:
> > >
> > > Hi Louis --
> > >
> > > If the system you are working with is actually the crossover
of
> two simple
> > > moving averages, the results you get will probably not be
very
> good. I
> > > often suggest a simple system when I am trying to make a
point
> that requires
> > > a system and I do not want the definition of the system to
> confuse the other
> > > point. You will need a system that is more sophisticated to
show
> good
> > > results. Try the CMO Oscillator in the code posted below.
> > >
> > > // CCT CMO Oscillator.afl
> > > //
> > > // A CMO Oscillator
> > > //
> > > //
> > >
> > > // Two variables are set up for optimizing
> > > CMOPeriods=Optimize("pds",61,1,101,5);
> > > AMAAvg=Optimize("AMAAvg",36,1,101,5);
> > >
> > > // The change in the closing price is summed
> > > // into two variables -- up days and down days
> > > SumUp = Sum(IIf(C>Ref(C,-1),(C-Ref(C,-1)),0),CMOPeriods);
> > > SumDown = Sum(IIf(C<Ref(C,-1),(Ref(C,-1)-C),0),CMOPeriods);
> > >
> > > // The CMO Oscillator calculation
> > > CMO = 100 * (SumUp - SumDown) / (SumUp + SumDown);
> > >
> > > //Plot(CMO,"CMO",colorGreen,styleLine);
> > >
> > > // Smooth the CMO Oscillator
> > > CMOAvg = DEMA(CMO,AMAAvg);
> > > // And smooth it again to form a trigger line
> > > Trigger = DEMA(CMOAvg,3);
> > > // Buy when the smoothed oscillator crosses
> > > // up through the trigger line
> > > Buy = Cross(CMOAvg,Trigger);
> > > // Sell on a downward cross, or 6 days,
> > > // whichever comes first
> > > Sell = Cross(Trigger,CMOAvg) OR BarsSince(Buy)>=6;
> > >
> > > Buy = ExRem(Buy,Sell);
> > > Sell = ExRem(Sell,Buy);
> > >
> > > Plot(C,"C",colorBlack,styleCandle);
> > >
> > > PlotShapes(Buy*shapeUpArrow+Sell*shapeDownArrow,
> > > IIf(Buy,colorGreen,colorRed));
> > > Plot (CMOAvg,"CMOAvg",colorGreen,
> > > style=styleLine|styleOwnScale|styleThick,-100,100);
> > > //Figure 20.2 CMO Oscillator
> > >
> > > Now -- back to the issue of validating a trading system --
> > >
> > > Tomorrow is out-of-sample. You want to increase your
confidence
> that your
> > > trading system will be profitable when you trade it tomorrow.
In
> order to
> > > do this, observe what happens after you have optimized a
system
> over an
> > > in-sample period, then tested it on the immediately following
out-
> of-sample
> > > data. The automated walk forward process helps you do this.
> Every step
> > > gives one more observation of the in-sample to out-of-sample
> transition. If
> > > the cumulative out-of-sample results are satisfactory to you,
> then you have
> > > increased confidence that your real trades are likely to be
> profitable. No
> > > guarantees. The best we can hope for is a high level of
> confidence.
> > >
> > > At this point, do not worry about Monte Carlo.
> > >
> > > Just concentrate on:
> > >
> > > 1. Select the objective function that You feel most
comfortable
> with.
> > > 2. Design and test the systems of interest to You.
> > > 3. Experiment to find the length of the in-sample period.
> > > 4. Perform the automated walk forward analysis.
> > > 5. Examine the out-of-sample results.
> > > 6. Decide whether or not to trade your system.
> > >
> > > Thanks for listening,
> > > Howard
> > > www.quantitativetradingsystems.com
> > >
> > >
> > >
> > > On Tue, Apr 15, 2008 at 7:03 PM, Louis Préfontaine
> <rockprog80@>
> > > wrote:
> > >
> > > > Hi,
> > > >
> > > > I've been experimenting with walking-forward, and I have
some
> questions
> > > > regarding how it works.
> > > >
> > > > I ran a complete random optimization or buying/selling
using the
> > > > variables I set (a MCS in fact), and systematically OOS
results
> were worst
> > > > than IS. I don't understand how it works, because whatever
if
> the sampling
> > > > is IS or OOS it is always the same variables that are in
place.
> > > >
> > > > Anyone could explain how this work?
> > > >
> > > > Thanks,
> > > >
> > > > Louis
> > > >
> > >
> > >
> > >
> >
>
------------------------------------
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