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Hi All,
About Larry Williams' contribution in support of Walk Forward
Testing: Whatever Larry says has great validity, as it's coming from
probably the most famous and most knowledgeable trader of modern
times.
It's certainly correct that if done properly, walk forward testing
has great value. For those of you not aware of walk forward testing,
it's first setting your system parameters and then testing the
results in the future using those pre-set parameters without benefit
of additional or new optimization. Some people refer to that
as "hypothetical real-time trading."
However, walk forward testing can in fact be a trap if done
incorrectly. That's because there's a problem in deciding what pre-
set algorithm or parameters to use prior to the so-called walk
forward test. If we arrive at those parameters by an optimization
process, then we may be guilty of optimizing the walk forward test
without even realizing we have done that. Another pitfall, is the
great tendency to optimize the walk forward testing time period
itself.
Possibly the only way to do it correctly, is to first arrive at a set
of parameters and algorithm based on logic, experience, or sound
trading principals that won't be subject to change. Then do a walk
forward with no attempt to improve results via optimization.
Regards,
Pal
--- In amibroker@xxxxxxxxxxxxxxx, "palsanand" <palsanand@xxxx> wrote:
> Hi,
>
> I came across something interesting from a famous trader:
>
> Walk Forward Tested System Is Better than the Best
> Optimized One - Larry Williams
>
> It looks like the history of technical analysis has been largely
> influenced by optimization. That is, we studied the past, found
> something that looked significant, then optimized rules and
> procedures to trade the observation in the future.
>
> Sometimes that has worked. Often it has not. That's our dilemma.
What
> are we to do? In the past, we answered these questions by doing
more
> optimization, more curve fitting. Indeed, we treated historical
data
> like prisoners of war. Our thesis was, if you beat them often
enough
> they would reveal anything. Which is true, but you want them to
> reveal everything, not anything.
>
> This brings me to one point. I think we will all make much more
> headway with system development by spending less time on
optimization
> and more time on walking systems and procedures forward.
>
> If on a walk forward test, the system holds up, we probably have
> something. And for sure, what we have will be better than the very
> best optimized system when it comes to real time trading. Hence,
> let's see what we can learn from each other about conducting walk
> forward tests. Any ideas will be appreciated by all, I am certain.
>
> Regards,
>
> Pal
> --- In amibroker@xxxxxxxxxxxxxxx, "Howard Bandy" <howardbandy@xxxx>
> wrote:
> > Hi Dingo -
> >
> >
> >
> > It's not quite that bad.
> >
> >
> >
> > My last paragraph is a warning for the common technique that many
> people try
> > - looking at out-of-sample results so much that they become in-
> sample data.
> > That is OK, if there is another set of data that will be used for
> further
> > testing. Sometimes model development is done using three sets of
> data -
> > learning, testing, and validation. The learning data is
extensively
> > searched and used to select parameter values. The testing data
is
> used to
> > determine when to stop searching and use the results found to be
> best so
> > far. For each set of parameters, a run is made on the learning
> data, and
> > the value of the objective function is noted. Periodically, the
> model using
> > the best parameters is run against the test data. When the
> performance over
> > the test data falls off, that tells the optimizer to stop and
> return the
> > parameters that worked best over the test data. One (or a Very
few)
> > additional run is made against the validation data. If results
are
> > acceptable, the model is used. If the validation data produces
poor
> > results, do not use the model. Rather, go back to the model
design
> stage
> > and come up with something new to try.
> >
> >
> >
> > If the model is able to recognize profitable situations, and the
> parameters
> > are stable, a wide range of parameters will be profitable.
> >
> >
> >
> > One of my points in the posting is that the walk-forward testing
> with
> > automatic selection of parameter values should be taken all
> together as a
> > process. If the results are good, then the process has validity
> and there
> > is a reasonable expectation that trading the model and parameter
set
> > resulting from the last optimization will be profitable.
> >
> >
> >
> > Thanks,
> >
> > Howard
> >
> >
> >
> > -----Original Message-----
> > From: dingo [mailto:dingo@x...]
> > Sent: Thursday, October 16, 2003 12:02 PM
> > To: amibroker@xxxxxxxxxxxxxxx
> > Subject: RE: [amibroker] Optimization -- again
> >
> >
> >
> > So according to your last paragraph it is impossbile to develop a
> system
> > that is consistently successful since just looking for it voids
> it. Right?
> >
> >
> >
> >
> > d
> >
> > -----Original Message-----
> > From: Howard Bandy [mailto:howardbandy@x...]
> > Sent: Thursday, October 16, 2003 2:20 PM
> > To: amibroker@xxxxxxxxxxxxxxx
> > Subject: [amibroker] Optimization -- again
> >
> > Greetings --
> >
> > In my opinion, anything we do in development of trading systems
> involves a
> > search for a pattern than precedes a profitable trading
> opportunity. Any
> > time we examine the results of alternative systems, we are
involved
> in
> > searching; and when we select the most promising of those
> alternatives, we
> > are optimizing. Only a system based on truly random entries and
> exits would
> > not be the result some optimization. So the question of "should
we
> > optimize?" is moot -- we have no choice but to optimize.
> Consequently, we
> > should be aware of our optimization techniques.
> >
> > Chuck referred to an optimization technique recommendation I made
> to the
> > company we both worked for in Denver a few years ago. This is a
> short
> > description of it.
> >
> > The company is a Commodity Trading Advisor which traded futures,
not
> > individual stocks, but the procedures are equally valid for both.
> >
> > When I joined the company, they were using very long data series
> when
> > developing their models. They used a technique sometimes called
> folding or
> > jackknifing, where the data was divided into several periods --
say
> ten.
> > The modeling process made ten passes. During each pass, one
period
> was held
> > back to be used as out-of-sample data, the other nine were used
to
> select
> > the best parameter values. After all ten passes, the results
were
> gathered
> > together and the parameter values that scored best overall were
> chosen.
> > There are several problems with this method. One is the
difficulty
> with the
> > "ramp up" period at the start of each segment, another is that it
> is not
> > valid to use older data for out-of-sample testing than was used
for
> > in-sample development, and another is that the data series were
too
> long.
> > Chuck and I and others had many interesting discussions about how
> long the
> > in-sample data should be.
> >
> > My background is strong in both the theory and the practice of
> modeling and
> > simulation, and includes a great deal of experience with analysis
of
> > financial time series. I proposed the following method, which I
> continue to
> > believe is valid.
> >
> > First, before any modeling begins. Using judgment of management
and
> > comparison of trading profiles of many trading runs (real,
> simulated, or
> > imagined), pick an objective function by which the "goodness" of
a
> trading
> > system will be measured. This is important, it is a personal or
> corporate
> > judgment, and it should not be subject to optimization.
> >
> > Divide each data series into a sequence of in-sample and out-of-
> sample
> > periods. The length of the out-of-sample period is
> the "reoptimization"
> > period. Say there are about ten years of historical data
available
> > (1/1/1993 through 1/1/2003. Set the in-sample period to two
years
> and the
> > out-of-sample period to one year. Run the following sequence:
> Search /
> > optimize using 1993 and 1994; pick the "best" model for 1993-
1994;
> forward
> > test this model for 1995 and save the results; step forward one
> > reoptimization period and repeat until all the full in-sample
> periods have
> > been used. The final optimization will have been 2001 and 2002,
> with no
> > out-of-sample data to test. Ignore all in-sample results!!
> Examine the
> > concatenated out-of-sample equity curve. If it is acceptable,
you
> have some
> > confidence that the parameters select by the final optimization
> (2001 and
> > 2002) will be profitable for 2003. No guarantees -- only some
> confidence.
> >
> > How did I pick two years for in-sample and one year for out-of-
> sample? That
> > was just an example. The method is to set up an automated search
> where the
> > length of the in-sample period and the length of the out-of-
sample
> period --
> > the reoptimization period -- are variables, and then search
through
> that
> > space.
> >
> > Trading systems work because they identify inefficiencies in
> markets. Every
> > profitable trade reduces the inefficiency until, finally, the
> trading system
> > cannot overcome the frictional forces of commission and
slippage.
> This is
> > the same phenomenon that physicists talk about as entropy.
> >
> > My feeling -- and it may be different than Chuck's -- is that the
> market is
> > not only non-stationary, but that the probability that it will
> return to a
> > previous state is near zero.
> >
> > Being non-stationary means that market conditions change with
> respect to our
> > trading systems. If I am modeling a physical process, such as a
> chemical
> > reaction, I can count on a predictable modelable output for a
given
> set of
> > inputs. If I am modeling a financial time series, the output
> following a
> > given set of inputs changes over time. If a market were
stationary
> with
> > respect to an RSI oscillator system, I could always buy a rise of
> the RSI
> > through the 20 percent line, to use a very simplistic example.
> >
> > I feel that the introduction of microcomputers, trading system
> development
> > software, inexpensive individual brokerage accounts, and
discussion
> groups
> > such as this one have permanently changed the realm of trading.
> One,
> > everyone who is interested can afford to buy a computer, run
> AmiBroker, and
> > design and test trading systems. Two, if someone develops a
> profitable
> > system and trades it, the profits it takes reduce the potential
> profits
> > available to anyone else who trades it. Consequently, the
> characteristics
> > of the market change in a way that moves the market away from
that
> model
> > until that trading system is no longer profitable enough to
overcome
> > commission and slippage. Three, a new person beginning to study
> trading
> > system development typically tests a lot of old systems. If one
is
> found to
> > be profitable and they start trading it, the market moves back to
> being
> > efficient. Consequently, trading systems that used to work, but
no
> longer
> > work, are very unlikely to ever work again.
> >
> > So, I feel that the in-sample period should be short so that the
> market
> > conditions do not change much over that period. That is, I am
> looking for a
> > data series that is stationary relative to my model. The
stationary
> > relationship must extend beyond the in-sample period far enough
> that the
> > model will be profitable when used for trading in the out-of-
sample
> data.
> > The length of the extension determines the reoptimization
period.
> It could
> > be years, months, or even one day. Note that the holding period
of
> a
> > typical trade is very much related to the length of both the in-
> sample and
> > out-of-sample periods. The typical trade should be much shorter
> than the
> > in-sample period and somewhat shorter than the out-of-sample
period.
> >
> > The important point in all this is that the only results being
> analyzed are
> > the concatenated out-of-sample trades.
> >
> > As with all model development, every time I look at the out-of-
> sample
> > results in any way, I reduce the probability that future trading
> results
> > will be profitable. That means that I should not perform
thousands
> of tests
> > of model parameters, in-sample periods, and out-of-sample
periods,
> on the
> > same data series and then pick the best model base on my
> examination of
> > thousands of out-of-sample results. In effect, I will have just
> converted
> > all those out-of-sample results into in-sample data for another
> step in the
> > development. That is legitimate, just be aware of what is
> happening.
> >
> > Thanks for listening,
> > Howard
> >
> >
> >
> >
> >
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