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Re: Rolling Window Forward Optimization or Anchored ForwardOptimization - System results



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Mark:
Perhaps if you rethink your question more in terms of 'what is 
the hypothesis that I am attempting  to test',  you might find 
more enlightenment than if you were searching for 
"producing more reliable results".

The theory behind walk forward optimization is that the market
inefficiency you plan to trade in the near future has existed
only for limited periods in the past and may manifest themselves
for only a little while longer.  Certain markets, say SP day
trade SEEM to display this characteristic.

If this theory were true in a strict sense then rolling window
would give you the best results assuming that (a) you selected
the in sample size correctly; and (b) you selected the out of 
sample size correctly.  This might a a bit hard to do :-)

A weaker version of this theory is that DUH something happened
that changed the market and the 'old' market efficiency was 
replaced by the 'new' market inefficiency that you are going to 
get rich on -- assuming you find the magic DUH moment 
to begin your in sample optimization at the right time.

Notice that both the strong and the weak form of the walk forward
optimization hypothesis contain untestable auxiliary hypothesis
about beginning periods, in sample, and out of sample size.

So, it this a 'good theory'?
A philospher of science would have serious reservations about it.
A philosopher would want to know how your system perform 
using 'regular' optimization versus 'walk forward optimization' 
using TRUE OUT OF SAMPLE DATA.
For the philosopher the 'goodness of a theory' is relative to 
the goodness of other theories.
If you cannot even determine how two theories compare to each 
other, a philosopher might advise you to abandon your useless
theory, (or write a book about it to collect royalties).

Enjoy,
Leslie


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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-- 
Regards,
Leslie Walko
610-688-2442
--
 "Life is a tragedy for those who feel, a comedy for those who
think"
	Horace Walpole, 4th earl of Orford, in a letter dated about 1770