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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@xxx> 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@xxx>:
> >
> > 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@xxx>
> > 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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