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Whatever Larry says has great validity, as it's
coming from probably the most famous and most knowledgeable trader of modern
times.
Certainly, you jest.
Take care,
Steve
<BLOCKQUOTE
>
----- Original Message -----
<DIV
>From:
palsanand
To: <A title=amibroker@xxxxxxxxxxxxxxx
href="">amibroker@xxxxxxxxxxxxxxx
Sent: Sunday, October 19, 2003 10:45
AM
Subject: [amibroker] Re: Optimization --
again
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@xxxx] > > 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@xxxx] > > 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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