> > > --- In
amibroker@xxxxxxxxxxxxxxx <amibroker%
40yahoogroups.com>, "Howard
> > B" <howardbandy@> wrote:
> > > >
> > > > 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@>
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,
> > > > > Howard
> > > > >
> > > > > On Tue, Apr 8, 2008 at 10:37 PM, Mike <sfclimbers@> 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 <amibroker%