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Thanks, Howard for your knowledge: I've read your book, I think is very good.
But after all, one have to deal with his own systems and problems.
In my case, after these discussions, I have increased the IS period from 3 months to 5 months.
The CAR in the WF simulation have increased from 30% to 45%!!!!
the OS period is still in 3 months. I will reduce to 1 month, which will probably increase the CAR, but the computer takes soooo muuuuch time to perform such a simulation..
greetings
--- In amibroker@xxxxxxxxxxxxxxx, Howard B <howardbandy@xxx> wrote:
>
> 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 <bistoman73@xxx> 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 amibroker@xxxxxxxxxxxxxxx <amibroker%40yahoogroups.com>, "Gonzaga"
> > <gonzagags@> 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 amibroker@xxxxxxxxxxxxxxx <amibroker%40yahoogroups.com>, "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 amibroker@xxxxxxxxxxxxxxx <amibroker%40yahoogroups.com>,
> > "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 amibroker@xxxxxxxxxxxxxxx <amibroker%40yahoogroups.com>,
> > "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!
> > > > > >
> > > > >
> > > >
> > >
> >
> >
> >
>
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