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While one could if one were so inclined,
write most of the factors into a single objective function expressing fitness,
I personally find it simpler to specify these things in three types of
statements.
-
Fitness
-
Constraints
-
Goals which
can result in either a penalty to fitness if not met or a bonus if exceeded
See … http://www.amibroker.org/userkb/2007/08/13/3-io-fitness-goals-and-constraints/
In addition I also want to be able to
influence the parameter values that the optimization chooses by being able to penalize
fitness for solutions that have overly sensitive parameters. Without this
capability one will only be able to see how sensitive the parameter values are
after the fact which in return results in the optimization process being hit or
miss in terms of arriving at relatively insensitive parameter values.
See … http://www.amibroker.org/userkb/2007/08/13/4-io-robustness-a-sensitive-subject/
From: amibroker@xxxxxxxxxxxxxxx [mailto:amibroker@xxxxxxxxxxxxxxx] On Behalf Of Howard B
Sent: Friday, May 09, 2008 1:46 PM
To: amibroker@xxxxxxxxxxxxxxx
Subject: Re: [amibroker] Re:
Fitness Criteria that incorporates Walk Forward Result
Greetings all --
I agree with Fred's comments just above this posting.
On the subject of calculation of an objective function. In my opinion, it is
important to consolidate all of the characteristics that are important to you
(remember -- the definition of this function is your personal choice) into a
single value. It applies to the entire test run and is best used to
choose among alternatives that are generated under the same circumstances --
all from a single optimization, for example. Some of the best objective
functions are those that reward equity growth and smoothness, while penalizing
drawdown. These calculations are made over a number of bars -- usually
the entire run. It does not make sense to me to have a value for each
bar.
Thanks,
Howard
On Fri, May 9, 2008 at 4:42 AM, Fred Tonetti
<ftonetti@xxxxxxxxxxnet>
wrote:
- The logic
behind having a sensitivity guided or influenced in sample optimization is
… Less sensitive parameters = A more robust system = A higher
likelihood of good performance OOS … The problems with a separate
guidance phase or a Test OOS that precedes a Real OOS is that by
definition this puts the real OOS that much further away from the IS
optimization and there really isn't any "guidance" per se
… What you get instead is more of a right / wrong type of answer.
2-4 I understand your questions but none of these have simple answers
per se.
5. You can't
guide IS optimization by real OOS results without the OOS in effect becoming
part of the IS.
I can see there have been a lot of discussions, mostly
centred around
the processes and/or the methodology of optimization. While these are
good discussions, and with many points of views. I wonder if there
are any quantitive research being done to verify the different points
of view. I have consumed thousands of hours my own time plus even
more computer processing time in system development, and have made a
few interesting observations. However, these are not rigourous enough
to quantify my view. However, these are interestingly enough
observation to share.
1. Does sensitivity analysis provide similar consistency as OOS
guidance walk forward analysis. I use that term now for the sake of
continuity in the discussion. Sensitivity merely takes a different
path to the same data set that you optimize on. But OOS takes a
completely data set. My own observation is that I definitely need the
guidance of OOS. They dont do the same thing.
2. How does the definition of fitness affect the result of the
optimized system in terms of robustness, performance and stability
through time and with different markets? As the old saying
goes, the answer is only important if the right question is asked.
Defining a fitness criteria is like framing a question. The answer
comes in the form of result and will be different of course depending
the fitness criteria. Even for a single goal inside one's mind, it
can be expressed in term of different fitness criteria. How is that
different intrepretation going to affect us in terms of equity curves
comparsions. Eg CAR/MDD vs UPI vs some of the more complicated
variety?
3. Related to above, How about instead of defining fitness as a
single number, we define fitness as a time series. E.g, UPI as a
series of UPI every six month. We now have a distribution of UPI. How
does different distributions affect our In sample optimization. It is
like doing rolling optimization, and collating the results.
4. How does artifical division of data set affect our results? For
example, If I optimized a system based on all the stocks on the ASX
exchange, including both current and delisted stocks, How would it
perform if I run it on ASX100 (The top 100 Australian company as
defined by S&P)
5. What is the real difference between Guiding the optimization with
OOS vs merly validating our optimization through OOS in terms of
results? Is it really better to skip Guidance and go straight to
Validation, or in my case, skip Validation and stick with Guidance.
Food for thought, I'll be trying to answer some of them myself.
Cheers
Paul.
--- In amibroker@xxxxxxxxxps.com,
"Howard B" <howardbandy@...> wrote:
>
> Hi Brian --
>
> I tend to agree with Fred. I, personally, do not use the guidance
data
> set. If you want to use it, and you are looking for consistency
between the
> two data sets, that might be valuable. But another measure of that
is
> simply the equity curve and other performance stats over both
periods.
>
> Another approach is to look at the robustness of the system by
perturbing
> each of the perturbable variables (not all of them are), computing
the
> scores for nearby points and rewarding "plateaus" in preference
to "peaks."
>
> Thanks,
> Howard
>
>
> On Thu, May 8, 2008 at 7:38 PM, Fred Tonetti
<ftonetti@xxx> wrote:
>
> > Personally I couldn't find any value in the guidance phase
which I
> > allowed for in IO for a couple of years and have since removed the
> > capability.
> >
> >
> > ------------------------------
> >
> > *From:* amibroker@xxxxxxxxxps.com
[mailto:amibroker@xxxxxxxxxps.com]
*On
> > Behalf Of *brian_z111
> > *Sent:* Thursday, May 08, 2008 8:32 PM
> > *To:* amibroker@xxxxxxxxxps.com
> >
> > *Subject:* [amibroker] Re: Fitness Criteria that incorporates
Walk Forward
> > Result
> >
> >
> >
> > Howard,
> >
> > Thanks for a very nice summary of the framework.
> >
> > I would say that, since the training search is exhaustive
(therefore
> > we must have identified all possible candidates for the strategy)
the
> > best we can hope for, in the guidance phase, is to change our
choice
> > of top model to one or another of the 'training top models', or
> > abandon the strategy altogether.
> >
> > Also I wonder, if the training model/guidance model combination,
that
> > passes a minimum requirement in both phases, and shows less
variance
> > between the training and guidance results, is the most generic
model
> > of them all i.e. suited to a wider range of conditions but not
> > necessarily returning the highest possible result in any
particular
> > market?
> >
> > brian_z
> >
> > --- In amibroker@xxxxxxxxxps.com
<amibroker%
40yahoogroups.com>,
"Howard B"
> > <howardbandy@> wrote:
> > >
> > > Greetings all --
> > >
> > > I am coming to this discussion a little late. I just returned
from
> > giving a
> > > talk at the NAAIM conference in Irvine. Some of the discussions
I
> > had with
> > > conference attendees was exactly the topic of this thread.
> > >
> > > If you are using some data and results to guide the selection of
> > logic and
> > > parameter values (as described in the earliest postings as OOS
> > data), that
> > > incorporates that data into the In-Sample data set. In this
case,
> > there
> > > must be three data sets. They go by various names -- Training,
> > Guiding, and
> > > Validation will be adequate for now.
> > >
> > > Optimization, by itself, begins by generating a lot of
alternatives.
> > > Optimization with selection of the "best" alternatives
means
using
> > an
> > > objective function (or fitness function) to assign a score to
each
> > > alternative.
> > >
> > > The method of searching for good trading systems used in
AmiBroker's
> > > automated walk forward procedure uses a series of: search over
an
> > in-sample
> > > period, select the best using the score, test over the out-of-
sample
> > > period. Use the concatenated results from the out-of-sample
> > periods to
> > > decide whether to trade the system or not.
> > >
> > > Another method of searching for good systems (that might be what
> > some of the
> > > posters to this thread were suggesting) is to perform extensive
> > searches of
> > > the data and manipulations of the logic using the Training data,
> > then
> > > evaluate using the Guiding data. Repeat this process as desired
or
> > required
> > > as long as the results using the Guiding data continue to
improve.
> > When
> > > they show signs of having peaked, roll back to the system that
> > produced the
> > > best result up to that point. Then make one evaluation using the
> > Validation
> > > data. Now, step forward in time and repeat the process. It is
now
> > the
> > > concatenated results of the Validation data sets that are used
to
> > decide
> > > whether to trade the system or not.
> > >
> > > Thanks,
> > > Howard
> > >
> > > On Thu, May 8, 2008 at 9:24 AM, Edward Pottasch
<empottasch@>
> > > wrote:
> > >
> > > > thanks. Will have a look,
> > > >
> > > > Ed
> > > >
> > > >
> > > >
> > > > ----- Original Message -----
> > > > *From:* Fred <ftonetti@>
> > > > *To:* amibroker@xxxxxxxxxps.com <amibroker%40yahoogroups.com>
> > > > *Sent:* Thursday, May 08, 2008 5:42 PM
> > > > *Subject:* [amibroker] Re: Fitness Criteria that incorporates
> > Walk Forward
> > > > Result
> > > >
> > > > There's a simple example of this in the UKB under
Intelligent
> > > > Optimization ...
> > > >
> > > > --- In amibroker@xxxxxxxxxps.com <amibroker%
40yahoogroups.com>,
> > "Edward Pottasch" <empottasch@>
> > > > wrote:
> > > > >
> > > > > hi,
> > > > >
> > > > > "While optimization can be employed to search for
a good
system
> > via
> > > > > methods utilizing automated rule creation, selection
and
> > > > combination
> > > > > or generic pattern recognition"
> > > > >
> > > > > anyone care to explain how this works? Some kind of
inversion
> > > > technique? Here is what I want now give me the rules to get
> > there :)
> > > > >
> > > > > thanks,
> > > > >
> > > > > Ed
> > > > >
> > > > >
> > > > >
> > > > > ----- Original Message -----
> > > > > From: Fred
> > > > > To: amibroker@xxxxxxxxxps.com <amibroker%
40yahoogroups.com><amibroker%
> > 40yahoogroups.com>
> > > > > Sent: Thursday, May 08, 2008 2:37 PM
> > > > > Subject: [amibroker] Re: Fitness Criteria that
incorporates
Walk
> > > > Forward Result
> > > > >
> > > > >
> > > > > While optimization can be employed to search for a
good
system
> > > > via
> > > > > methods utilizing automated rule creation, selection
and
> > > > combination
> > > > > or generic pattern recognition most people typically
use
> > > > optimization
> > > > > to search for a good set of parameter values. The
success
of the
> > > > > latter of course assumes one has a good rule set i.e.
system to
> > > > begin
> > > > > with.
> > > > >
> > > > > As far as your prediction is concerned ... I suspect
there
are
> > > > lots
> > > > > of people, some of who post here, who could
demonstrate
> > otherwise
> > > > if
> > > > > they chose to ...
> > > > >
> > > > > --- In amibroker@xxxxxxxxxps.com <amibroker%
40yahoogroups.com><amibroker%
> > 40yahoogroups.com>,
> > > > "brian_z111" <brian_z111@>
> > > > wrote:
> > > > > >
> > > > > > "IS metrics are always good because we keep
optimizing
until
> > > > they
> > > > > > are" (or words to that effect by HB) which
is true.
> > > > > >
> > > > > > It is not until we submit the system to an
unknown sample,
> > > > either
> > > > > an
> > > > > > OOS test, paper or live trading that we validate
the
system.
> > > > > >
> > > > > > Discussing your points:
> > > > > >
> > > > > > IMO we are talking about two different trading
approaches, or
> > > > > styles
> > > > > > (there is no reason we can't understand both very
well).
> > > > > >
> > > > > > One is the search for a good system, via
optimization,
with
> > the
> > > > > > attendant subsequent tuning of the system to
match a
changing
> > > > > market.
> > > > > >
> > > > > > If I understand Howard correctly he is an
exponent of this
> > > > style.
> > > > > >
> > > > > > It is my prediction that where we are optimising,
using
> > > > lookback
> > > > > > periods, that the max possible PA% return will be
around
30,
> > > > maybe
> > > > > > 40, for EOD trading.
> > > > > >
> > > > > > Do we ever optimise anything other than
indicators with
> > > > lookback
> > > > > > periods?
> > > > > > If so that might be a different story.
> > > > > >
> > > > > > Bastardising Marshall McCluhans famous line I
could
say "the
> > > > > > optimization is the method".
> > > > > >
> > > > > > It is also possible to conceptually optimize the
system,
> > before
> > > > > > testing, to the point that little, or no,
optimization is
> > > > required
> > > > > > (experienced traders with a certain disposition
do this
quite
> > > > > > comfortably but it doesn't suit the inexperienced
and/or
those
> > > > who
> > > > > > don't have the temperament for it).
> > > > > >
> > > > > > So, if a system has a sound reason to exist, and
it is not
> > > > > optimized
> > > > > > at all, and it has a statistically valid IS test
then it
his
> > > > highly
> > > > > > likely to be a robust system, especially if it is
robust
> > across
> > > > a
> > > > > > range of stocks/instruments.
> > > > > > The chances that this is due to pure luck are
probably
longer
> > > > than
> > > > > > the chance that an optimized IS test, with a
confirming
OOS
> > > > test,
> > > > > is
> > > > > > also a chance event.
> > > > > >
> > > > > > However, if I had plenty of data e.g. I was an
intraday
> > trader,
> > > > > then
> > > > > > I would go ahead and do an OOS test anyway (since
the
cost is
> > > > > > negligible)
> > > > > >
> > > > > > Re testing on several stocks.
> > > > > >
> > > > > > If the system is 'good' on one symbol, (the
sample size is
> > > > valid)
> > > > > and
> > > > > > it is also good on a second symbol (also with a
valid
sample
> > > > size)
> > > > > is
> > > > > > that any different from performing an IS and an
OOS test?
> > > > > >
> > > > > > For stock trading, I call the relative
performance, on a
set
> > of
> > > > > > symbols, 'vertical' testing as compared to
'horizontal'
> > testing
> > > > > > (where horizontal testing is an equity curve).
> > > > > >
> > > > > > Yes, if an IS test, with no optimization, beat
the buy &
hold
> > > > on
> > > > > > every occasion (or a significant number of times)
in a
> > vertical
> > > > > test
> > > > > > and the sum of that test was statistically valid
and the
> > > > horizontal
> > > > > > test (the combined equity curve) was 'good' it
would give
you
> > > > > > something to think about for sure.
> > > > > > If some of the symbols, in the vertical stack,
had
contrary
> > > > > returns,
> > > > > > compared to the bias of my system, I probably
would start
to
> > > > get a
> > > > > > little excited.
> > > > > >
> > > > > > (I think perhaps you were alluding to something
along
those
> > > > lines).
> > > > > >
> > > > > > BTW did you know that the Singapore Slingers play
in the
> > > > Australian
> > > > > > basketball league?
> > > > > >
> > > > > > Cheers,
> > > > > >
> > > > > > brian_z
> > > > > >
> > > > >
> > > >
> > > >
> > > >
> > >
> >
> >
> >
>
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