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> To make sure I have been clear on this ----
> It does not matter At All how many trades or what length of time the
> in-sample period covers. Results from the in-sample runs have no
value in
> estimating the future performance.
Howard,
In an earlier post you stated that the number of IS observations will
impact the error bands of the backtested statistics.
Given that these same statistics are then used in the calculation of
the objective function, which in turn will dictate the parameter
values to use in the next OOS period. Wouldn't it be a logical
extension that an IS period should have sufficient observations to
allow the error bands to stabilize? Not for "estimating the future
performance". But rather for estimating the best parameter values.
Or, are you satisfied that performant OOS walk forward periods is all
that counts? How many OOS periods do you like to have before making
your final judgement?
As always, thanks for sharing.
Mike
--- In amibroker@xxxxxxxxxxxxxxx, "Howard B" <howardbandy@xxx> wrote:
>
> Hi Louis, and all --
>
> Select the period of time for the in-sample period that works for
the system
> you are using.
> Select the period of time for the out-of-sample period and
reoptimization
> period that is sufficient for the system and the market to stay in
sync and
> to give you several walk forward steps.
> Perform the walk forward analysis.
> Look at the out-of-sample results from the combined walk forward
steps.
> Decide from there whether to trade or go back to the drawing board.
>
> To make sure I have been clear on this ----
> It does not matter At All how many trades or what length of time the
> in-sample period covers. Results from the in-sample runs have no
value in
> estimating the future performance.
>
> Thanks for listening,
> Howard
>
>
> On Tue, Apr 22, 2008 at 8:22 PM, Louis Préfontaine <rockprog80@xxx>
> wrote:
>
> > Hi Howard,
> >
> > What would you consider to be a sufficiently large sample for IS
and then
> > for OOS? If I develop a system that makes 250 trades a year,
then if I
> > select IS-OOS of 2-3 weeks then it's no more than 10-15 trades.
Is this
> > enough?
> >
> > Regards,
> >
> > Louis
> >
> > 2008/4/22, Howard B <howardbandy@xxx>:
> > >
> > > Hi Simon --
> > >
> > > From your description, the system was developed on a set of
data, but
> > > not tested on any data that was not used during development.
The data used
> > > during development is called the in-sample data. Data used for
testing that
> > > was not used during development is called the out-of-sample
data.
> > >
> > > The in-sample results always look good -- we do not stop
playing with
> > > the system until they look good. The in-sample results have no
value in
> > > estimating the future out-of-sample results. In order to
estimate what the
> > > likely profitability will be when traded with real money, out-
of-sample
> > > testing is necessary.
> > >
> > > I have documented systems that have over 1,300,000 closed
trades and
> > > reasonable looking results for the in-sample period, but were
not profitable
> > > out-of-sample.
> > >
> > > There is no substitute for out-of-sample testing.
> > >
> > > Thanks for listening,
> > > Howard
> > > www.quantitativetradingsystems.com
> > >
> > >
> > > On Thu, Apr 17, 2008 at 2:29 AM, si00si00 <si00si00@xxx> wrote:
> > >
> > > > Hi all,
> > > >
> > > > I have a friend who has developed a trading system. It is an
intraday
> > > > system that makes on average around 5 futures trades per day.
We were
> > > > discussing it the other day and a point of disagreement arose
between
> > > > us. He claims that there is no necessity for him to test the
strategy
> > > > on out of sample data because he has back tested it using
over 8 years
> > > > of historical intraday data, and the patterns the strategy
predicts
> > > > occur 70% of the time or more.
> > > >
> > > > My question is, does anyone know if the data-mining bias can
be
> > > > considered irrelvant when the sample size is so large? (in
this case,
> > > > the sample size is roughly 8400 trades). Put another way,
with so many
> > > > observations, how many different rules would have to be back
tested in
> > > > order for data-mining bias to creep in?
> > > >
> > > > Thanks in advance for any thoughts you might have!
> > > >
> > > > Simon
> > > >
> > > >
> > >
> >
> >
>
------------------------------------
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