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I'll
probably only confuse things, but I'll describe how my walk-forward code
works. I would share the program with you, but it is some 2 million
lines of C++ and has nothing to do with AFL, AB, ABTool, etc. I use
this approach only for futures markets, where I do have different system
parameters for each commodity.
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size=2>
I
WOULD NEVER DREAM OF USING DIFFERENT PARAMETERS FOR STOCKS, EITHER UNIQUE
TO EACH STOCK OR UNIQUE TO DIFFERENT TIME PERIODS.
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size=2>
In any
event, I can tell my backtester a start point, an in-sample window size and an
out-of-sample window size. I also tell it which of over 200 objective
functions I want to use when making the decision as to which parameter set to
use for the next out-of-sample data. An objective function might be
something as simple as KRatio, or Netprofit/Drawdown,
whatever. The in-sample window is used for "learning" a
parameter set and then my software measures how well the system does
out-of-sample. I can tell you now that more than 95% of the systems tested
in this manner are rejected at this stage.
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size=2>
I
cannot envisage another way to do walk-forward testing, but then I've been stuck
in the mud using this approach since 1960.
<BLOCKQUOTE
>
<FONT face="Times New Roman"
size=2>-----Original Message-----From: Fred
[mailto:fctonetti@xxxxxxxxx]Sent: Wednesday, June 18, 2003 2:06
AMTo: amibroker@xxxxxxxxxxxxxxxSubject: [amibroker] Re:
Walk-Forward Out of Sample (OOS)
Testingb,Actually you bring up some
interesting points one of which I hadn't initially thought of but of
course that is why I was asking the questions I was i.e. 1.
Are we testing some optimizable system oriented parameters for some fixed
user selectable lookback period or window which is either based on an
anchored first data point or is a moving window as time goes along ... or
...2. Are we testing a static system for some optimizable
lookback window ? ... or ...3. All of the above ? ... or
...4. Something else ?I suspect it's #1 as I'm not sure
optimizing the lookback period has merit ... or at least not more than
once per full run i.e. we might want to see how changing the size of the
lookback period or window size affects the results overall as we might
want to see how using different lengths of out of smaple data affects the
results before we reoptimize but I don't think having varying lookback
periods or window sizes has merit, do you ? This "feels" way to
curve fit.Fred--- In amibroker@xxxxxxxxxxxxxxx, "Fred"
<fctonetti@xxxx> wrote:> It's okay ... I think Mark thinks it's
over my head ...> > --- In amibroker@xxxxxxxxxxxxxxx, "b519b"
<b519b@xxxx> wrote:> > Fred,> > > > I
think you and Mark have misunderstood each other over the use of >
> the terms "segment" and "period". Like you, I first read these as
> > synonyms, but after re-reading Mark's post, it appears they are
> > completely distinct.> > > > Reading them as
synonyms gives the following:> > Test 3 time segments. Select the
best period (=time segment) and > use > > the parameter
setting from that segment. As you point out such an > > approach
would problematic.> > > > Reading period and segment as
being distinct gives:> > Test the 3 time segments. Select the best
period (=parameter > > setting), ie, select the look-back period in
the sample code which > > gives the best profit factor for all 3
of the time segments. Use > > this parameter (ie, look back
"period") for time segment 4. And so > > on.> >
> > I think Mark has the second approach in mind. But Mark can
correct > > me if I am wrong.> > > >
bSend
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