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Hello Leslie,
As Dennis Miller says, "I could be wrong but..." it would be a lot of
work but it seems you could test your walk forward method directly
rather than rely on assumptions. As an example:
optimize a system on 1990 data.
check its results on 1991 data.
optimize on 1991.
check its results on 1992.
etc etc.
The results from "checking" would give you the actual performance of
the "system"--system now defined as trading rules plus
re-optimization.
Best regards,
Jim Johnson mailto:jejohn@xxxxxxxxxxx
--
Thursday, October 31, 2002, 10:56:33 PM, you wrote:
LW> Mark:
LW> Perhaps if you rethink your question more in terms of 'what is
LW> the hypothesis that I am attempting to test', you might find
LW> more enlightenment than if you were searching for
LW> "producing more reliable results".
LW> The theory behind walk forward optimization is that the market
LW> inefficiency you plan to trade in the near future has existed
LW> only for limited periods in the past and may manifest themselves
LW> for only a little while longer. Certain markets, say SP day
LW> trade SEEM to display this characteristic.
LW> If this theory were true in a strict sense then rolling window
LW> would give you the best results assuming that (a) you selected
LW> the in sample size correctly; and (b) you selected the out of
LW> sample size correctly. This might a a bit hard to do :-)
LW> A weaker version of this theory is that DUH something happened
LW> that changed the market and the 'old' market efficiency was
LW> replaced by the 'new' market inefficiency that you are going to
LW> get rich on -- assuming you find the magic DUH moment
LW> to begin your in sample optimization at the right time.
LW> Notice that both the strong and the weak form of the walk forward
LW> optimization hypothesis contain untestable auxiliary hypothesis
LW> about beginning periods, in sample, and out of sample size.
LW> So, it this a 'good theory'?
LW> A philospher of science would have serious reservations about it.
LW> A philosopher would want to know how your system perform
LW> using 'regular' optimization versus 'walk forward optimization'
LW> using TRUE OUT OF SAMPLE DATA.
LW> For the philosopher the 'goodness of a theory' is relative to
LW> the goodness of other theories.
LW> If you cannot even determine how two theories compare to each
LW> other, a philosopher might advise you to abandon your useless
LW> theory, (or write a book about it to collect royalties).
LW> Enjoy,
LW> Leslie
LW> mark.keenan@xxxxxxxxxxxxxx wrote:
>>
>> Any opinions or experience on whether anchored forward optimization
>> (keeping the start date fixed) produces more reliable results than the more
>> common walk forward optimization, where the "in sample window is rolled
>> forward"
>>
>> Most walk forward examples in books describe optimizing over 2 years for
>> example - trading the optimized parameters over the next six months - and
>> the rolling the whole two year window forward by six months and the
>> repeating.
>>
>> My own testing has found anchored to be much more effective - I guess
>> building on the concept that if your going to optimize anyway then use as
>> much data as possible.
>>
>> I have been doing some work on a Double Linear Regression slope system on
>> index futures where I optimize over the whole data range (3 years - as
>> before three years the market was traded on the floor - is now screen
>> traded for the last 3 years) trade the result on the next month and then
>> add this recent "traded" month to the data series - re-optimize, trade on
>> the following month etc. Therefore as time passes my in sample data length
>> keeps getting bigger and bigger.
>>
>> On OUT OF SAMPLE trading - WITHOUT any stops yet - still doing the MAE
>> analysis the system is showing the following results using anchored
>> analysis.
>>
>> Total Trades 143
>> % Winning 51.05%
>>
>> Average Losing 2.95%
>> Average Winning 3.35%
>> Average Trade 0.31%
>>
>> Largest Winner 18.63%
>> Largest Loser 10.6% (would obviously be stopped out as below)
>>
>> Profit Factor 1.27
>> MAE average 2.31% (will be re-running results with a 2.50% stop
>> I think)
>>
>> Any views on the above??
>>
>> MK
>>
>> (sorry about disclaimer)
>>
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