Hi Howard,
I still am struggling with the following sentence from David Aronson : "The
decision about how to apportion the data between the IS and OOS subsets is
arbitrary. There is no theory that suggests what fraction of the data should be
assigned to training ( IS ) and testing ( OOS ). Results can be very sensitive
to these choices ... ". Because this is exactly what I am seeing. WalkFoward
results are more then sensitive to the IS/OOS relation and in many cases a pure
random story. I am getting more and more the feeling that WalkForward is not
the correct or better objective way to test trading systems. With all respect
to Robert Pardo's idea's about this topic and what you are writing in QTS ...
Regards, Ton.
----- Original Message -----
From: Howard B
To: [email protected]
Sent: Monday, October 05, 2009 12:48 AM
Subject: Re: [amibroker] Re: Is the Walk forward study useful?
Greetings all --
My point of view on the length of the in-sample and out-of-sample may be a
little different.
The logic of the code has been designed to recognize some pattern or
characteristic of the data. The length of the in-sample period is however long
it takes to keep the model (the logic) in synchronization with the data. There
is no one answer to what that length is. When the pattern changes, the model
fits it less well. When the pattern changes significantly, the model must be
re-synchronized. The only person who can say whether the length is correct or
should be longer or shorter is the person running the tests.
The length of the out-of-sample period is however long the model and the data
remain in sync. That must be some length of time beyond the in-sample period
in order to make profitable trades. It could be a long time, in which case
there is no need to modify the model at all during that period. There is no
general relationship between the length of the in-sample period and the length
of the out-of-sample period -- none. There is no general relationship between
the performance in-sample and the performance out-of-sample. The greater the
difference between the two, the better the system has been fit to the data over
the in-sample period. But that does not necessarily mean that the
out-of-sample results are less meaningful.
You can perform some experiments to see what the best in-sample length is.
And then to see what the typical out-of-sample length is. Knowing these two,
set up a walk forward run using those lengths. After the run is over, ignore
the in-sample results. They have no value in estimating the future performance
of the system. It is the out-of-sample results that can give you some idea of
how the system might act when traded with real money.
It is nice to have a lot of closed traded in the out-of-sample period, but
you can run statistics on as few as 5 or 6. Having fewer trades means that it
will be more difficult to achieve statistical significance. The number 30 is
not magic -- it is just conventional.
I think it helps to distinguish between the in-sample and out-of-sample
periods this way -- in-sample is seeing how well the model can be made to fit
the older data, out-of-sample is seeing how well it might fit future data.
Ignore the television ads where person after person exclaims "backtesting!"
as though that is the key to system development. It is not. Backtesting by
itself, without going on to walk forward testing, will give the trading system
developer the impression that the system is good. In-sample results are always
good. We do not stop fooling with the system until they are good. But
in-sample results have no value in predicting future performance -- none.
There are some general characteristics of trading systems that make them
easier to validate. Those begin with having a positive expectancy -- no system
can be profitable in the long term unless it has a positive expectancy. Then
going on to include trade frequently, hold a short time, minimize losses. Of
course, there have been profitable systems that trade infrequently, hold a long
time, and suffer deep drawdowns. It is much harder to show that those were
profitable because they were good rather than lucky.
There is more information about in-sample, out-of-sample, walk forward
testing, statistical validation, objective functions, and so forth in my book,
"Quantitative Trading Systems."
http://www.quantitativetradingsystems.com/
Thanks for listening,
Howard
On Sun, Oct 4, 2009 at 10:56 AM, Bisto <[email protected]> wrote:
Yes, I believe that you should increase the IS period
as general rule is not true "the shortest the best" trying to catch every
market change because it's possible that a too short IS period produces a too
low number of trades with no statistical robustness --> you will find
parameters that are more likely candidated to fail in OS
try a longer IS period and let's see what will happen
I read an interesting book on this issue: "The evaluation and optimization
of trading strategies" by Pardo. Maybe he repeated too much times the same
concepts nevertheless I liked it
if anyone could suggest a better book about this issue it would be very
appreciated
Bisto
--- In [email protected], "Gonzaga" <gonzag...@...> wrote:
>
> Oh, sorry, I am lost in translation ... ;-)
> Yes I meant trades of my IS period.
> I've got about 70 trades in my IS period, three months.
> BUT, I buy stocks in a multiposition way.This means, that my hole capital
divides among several stocks purchased simultaneously.
> So, in my statistics, I use to average my trades. When I use
maxopenpositions=7, I use to average my results every 7 trades.
> Considering that, my trades in three months are not 70, but less ( not
exactly 70/7, but less than 70)
>
> If I use maxopenposition=1, which is, invest all my capital every trade,
in three months I would have about 29 trades.
> So I suppose I have to increase the IS period.. isn`t it?
>
>
> --- In [email protected], "Bisto" <bistoman73@> wrote:
> >
> > What do you mean with "I don't have many buyings and sellings"?
> >
> > If you have less than 30 trades in an IS period, IMHO, you are using a
too short period due to not statistical robustness --> WFA is misleading, try a
longer IS period
> >
> > Bisto
> >
> > --- In [email protected], "Gonzaga" <gonzagags@> wrote:
> > >
> > > Thanks for the answers
> > > To Keith McCombs :
> > >
> > > I use 3 months IS test and 1 month step, this is, 1 month OS test. My
system is an end-of day-system, so I don't have many buyings and sellings..
> > > Perhaps I should make bigger the IS period?
> > >
> > > anyway, my parameter behaves well in any period. Of course it is an
optimized variable, but it doesn't fail in ten years, in none of those ten
years, over 500 stocks.. a very long period..
> > > So, couldn't it be better, on the long run, than the parameters
optimized with the WF study?
> > > (In fact, I am using it now, the optimized variable)
> > > That's my real question..
> > >
> > > To dloyer123:
> > > I haven't understood the meaning of the Walk Forward Efficency, and
seems interesting.
> > > can you explain it better, please..?
> > >
> > >
> > >
> > > --- In [email protected], "dloyer123" <dloyer123@> wrote:
> > > >
> > > > I have had similar experiences. I like to use WFT to estimate what
Pardo call's his "Walk Forward Efficency", or the ratio of the out of sample WF
profits to just optimizing over the entire time period.
> > > >
> > > > A good system should have as high a WFE as posible. Systems with a
poor WFE tend to do poorly in live trading.
> > > >
> > > > If you have a parm set that works well over a long period of live
trading, then you are doing well!
> > > >
> > >
> >
>
