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Hi Mike --
I have read both of Bob Pardo's books. Bob and I had a telephone conversation several years ago, and we have exchanged emails recently. I agree with much of what he writes, but have different views in some areas.
In my opinion, the only way to determine how long the in-sample period should be is to run tests, varying the length of the in-sample period and observing the performance of the system on the following out-of-sample data. The "sweet spot" will depend on both the trading system logic and the data series that it is processing.
The data being processed is composed of one or more of the following components: long-term trend, long-term cycle, short term cycle, pattern, seasonality, and noise. There may be other components as well, just include them in the list. Long-term and short-term are relative. The trading system logic is written to identify some component (usually just one of the components) that precedes profitable trading opportunities, hope that those features persist beyond the in-sample period over which the system is developed, and can be profitably traded. The feature(s) being identified are the signal portion of the data. Everything else is noise. Even if some part of the "everything else" contains features that some other trading system can identify and profitably trade, it is noise to any system being tested that does not identify and remove it or compensate for it.
To shorten the explanation, the system is looking for the signal among the noise. The ease with which the signal can be identified depends on both the logic of the system and the characteristics of the data. It is not possible to generalize without knowing both.
Similarly, the only way to determine how long the out-of-sample period should be is to run tests. The systems we write are static. They may have logic that allows the parameters and the logic to adjust themselves, but the system does not change. The characteristics of the data being processed are dynamic. A trading system remains profitable only as long as the system and the data it models remain in synchronization. Clever systems can (but not always will) remain synchronized better than simple systems, but the synchronization is required for the trades to be profitable. The period of time that the system remains profitable is the period of time that the system and the market remain in sync. That period determines the schedule for re-optimization (the maximum time between re-optimizations) and that is the length of the out-of-sample period. There is no way to determine that length without testing the specific system on the specific data. The length of out-of-sample profitability will not remain constant, but will vary. There is no relationship between the length of the in-sample period and the length of the out-of-sample period.
The best we can hope for is a high level of confidence that our newly designed, newly optimized, newly re-optimized system performs well in real trading. There are no guarantees. The best way to gain confidence is to observe as many in-sample to out-of-sample transitions as possible and learn what to expect. The best way to do that is to run automated walk forward testing with fairly short out-of-sample periods. In an automated walk forward test, the length of the out-of-sample period is often the same as the re-optimization schedule.
Once the system has passed the validation procedure, and the designer understands what to expect in the period immediately following the re-optimization, re-optimization is permitted at any time. There is no need to wait the previously defined out-of-sample length time.
Although you did not raise the question in your posting, there is another component of system design and testing that is critically important. My opinion is that the whole process begins with the person or organization that is going to trade the system defining the criteria by which the acceptability of each system, or alternative system, is judged. Choice of this "objective function" is very personal, and it incorporates most of those features that the psychology of trading experts talk about when they help us learn to accept a trading system. With the correct choice of the objective function, every system that passes as acceptable is already one that the trader will be comfortable with.
And, importantly, it is the score on the objective function that determines which of the alternative systems will be chosen as "best" and used to trade forward in the out-of-sample period.
Consequently, I recommend that the objective function be chosen first. Then the system designed, tested, and validated using the walk forward process, and letting the system and the data it is reading determine how long the in-sample period is and how long the out-of-sample period is.
Thanks for listening,
Howard
www.quantitativetradingsystems.com
On Wed, Apr 9, 2008 at 10:08 AM, Howard B < howardbandy@xxxxxxxxx> wrote:
Hi Louis --
I agree that there is a serious problem when the only data that is available contains no period that is similar to what is expected in the future.
Artificial data has no value.
Using data that is earlier in time than the in-sample period has limited value. You can test earlier data, but you will over-estimate the performance that you can expect in the future.
Are there other tickers that are closely related that have data for the periods you would like to test?
In the end, you will need to make a decision on whether to place actual trades. And that decision must be based on your understanding of and confidence in your system. The only way to gain that confidence is by observing the transitions from in-sample testing to out-of-sample simulated trading.
Thanks,
HowardOn Tue, Apr 8, 2008 at 10:37 PM, Mike < sfclimbers@xxxxxxxxx> wrote:
Howard's comments are consistent with those of Robert Pardo (The
Evaluation and Optimization of Trading Strategies, Wiley 2008), with
respect to training periods.
Pardo recognizes that there is a tradeoff between more robust
strategies which require longer in sample training periods, require
fewer reoptimizations, trade for longer out of sample periods and are
generally less profitable, vs. more responsive strategies which
require shorter in sample training periods, require more frequent
reoptimizations, can only trade for shorter out of sample periods and
are generally more profitable.
Pardo suggests that strategies generating more frequent signals can
use shorter in sample training windows since they generate the
minimum 30+ trades sooner than strategies that generate less frequent
signals. But, that in any case, one should try to use an in sample
period sufficiently long to capture bull, bear, and sideways markets.
Further, when first trying to evaluate the worth of the strategy,
Pardo suggests backtesting the in sample history in segments rather
than one shot (e.g. 10 year history divided into five 2 year
segments). This gives you better insight as to whether the results
are due to a single segment or are consistent accross segments, and
provides insight to your eventual in sample/out of sample periods for
Walk Forward Optimization.
Finally, Pardo suggests that regardless of whether a long or short
training period is used, a rule of thumb for in sample vs. out of
sample is for out of sample to be between 1/8 to 1/3 of the in sample
period (e.g. 24/8 = 3 and 24/3 = 8, so it would be "safe" to trade
out of sample for 3 - 8 months based on a system backtested over 24
months.
Yet another good book covering the topic. I reccomend it.
Mike
--- In amibroker@xxxxxxxxxxxxxxx, "Howard B" <howardbandy@xxx> wrote:
>
> Hi Louis, and all --
>
> I know David Aronson, respect him, and like and recommend his book.
>
> My view is that the in-sample period should be as short as
practical. My
> thought is that: the system we are testing / trading is trying to
recognize
> the signal from among the noise; and the signal patterns are
changing over
> time. So the length of the in-sample period is a tradeoff -- short
to be
> able to change as the characteristics of the underlying market
change, but
> not so short that the system is over-fit to the noise rather than
learns the
> signal.
>
> You can test this in AmiBroker. Have your system ready to buy and
sell. In
> the Automatic Analysis window, use Settings and set up the Walk
Forward
> parameters. Try an in-sample period of 10 years, an out-of-sample
period of
> 6 months or 1 year. Run Optimize > Walk Forward and look at the in-
sample
> and out-of-sample equity curves. Shorten the length of the in-
sample period
> to 9, then 8, then 7, ... then 1 year, keeping the out-of-sample
period
> unchanged. Depending on your system and the market it is trading,
you may
> find that there is a sweet spot in the length of the in-sample
data. If so,
> that is the amount of data that allows your system to recognize the
signal
> without being overwhelmed by the noise.
>
> Thanks,
> Howard
>
>
> On Tue, Apr 8, 2008 at 8:56 AM, Louis Préfontaine <rockprog80@xxx>
> wrote:
>
> > Hi,
> >
> > I've been thinking a lot lately, and here is something I would
like to
> > have your opinion on.
> >
> > I've been introduced to automated systems by a trend following
book which
> > related how some trend followers built their systems in the 70s
or 80s and
> > got rich with them, and how their system did not really change
all this
> > time. They didn't change their system because they say the
market does NOT
> > change. They looked at historic market data from the 1800s and
the market
> > was as it is right now. So they say.
> >
> > On the other side, lately I have been introduced to the concept of
> > ever-changing markets and have had a hard time trying to build my
system.
> > Got a very promising start with a system getting around 15-20%
average for
> > April 2007 to April 2008 (with little drawdown, which mean that
with
> > leverage I can boost this a lot). In any variation over
thousands of stocks
> > the results were nearly all positives. But then, I tested that
same system
> > for the years 2000 to 2008, and that was disappointing. Even more
> > disappointing from 2001 to 2003, another troubled market like the
one we are
> > in right now.
> >
> > So here I am, wondering where to go from now. Aronson's
excellent book
> > talk about the importance of having a very large sample of data.
But the
> > problem is: the larger the data, the more "historic" it gets and
the less it
> > seems to work.
> >
> > Is my system not working, or did the markets really change? Do I
need to
> > make it more robust (that is, it MUST make profit even from 2001
to 2003),
> > or can I have complete faith in what happened in the last year?
> >
> > All those questions... Would be nice to read what you think
about this.
> >
> > Louis
> >
> >
>
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