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Thanks for the great response.
With so many options available, I like the idea of have a "best"
solution with some logical or at least empirical reasoning.
I am doing some walkforward testing now with UPI, to see how it works.
So far, Car/Mdd and Pessimistic Car/Mdd seem to have good results in
the walk forward testing. k-ratio was disappointing.
Mdd does have a "long tail" on the positive side, like a bell curve
with one end pulled out. The car distribution is much
more "normal".
Great link to Howards's slides. I enjoyed his book and look forward
to the enxt one.
I dont mind sharing the dll for Pesimistic Car/Mdd. There are also
some performance hacks to speed up DayOfWeek(), RoundPenny() and
IsOptionsWeek() that togeher, shaved 20% of the execution time off my
system.
--- In amibroker@xxxxxxxxxxxxxxx, "Mike" <sfclimbers@xxx> wrote:
>
> Hi,
>
> I think that the answer that you are most likely to hear is that
> there is no 'best' fitness function, since no two traders are
exactly
> alike, and the function has to suit the trader.
>
> However, generalizations can be made with regards to
characteristics
> that some fitness functions appear to posess. For example; K-Ratio
> and UPI tend to have a smooth steady slope.
>
> ***
>
> Currently, I have been striving to minimize the size, and more
> importantly to me - the duration, of a drawdown. As such, I find
that
> UPI serves as a good base. A nice description written by Peter
> Martin, the creator of the Ulcer Index and UPI (also known as
Martin
> Ratio), can be found here: http://www.tangotools.com/ui/ui.htm
>
> I combine UPI with filters unique to my needs (with regards to
> holding periods and number of trades). For exmaple, I deduct from
the
> UPI on a sliding scale when there are too few trades or when trades
> are on average held too long. I produce a custom metric, using
custom
> backtester code, which I then use as the fitness function for walk
> forward.
>
> ***
>
> Howard Bandy regularly cites CAR/MDD, RAR/MDD, RRR, Ulcer
Performance
> Index, and K-Ratio as strong starting points from which to form
your
> personalized metric. He gave a nice example in his Las Vegas
workshop
> (slides for a similar workshop can be found in this post:
> http://finance.groups.yahoo.com/group/amibroker/message/123602 )
> where he outlined a custom metric based on the following criteria:
>
> "My goal is to make a 15% annual profit trading common stocks,
> control drawdowns, cherry-pick trades, hold about one week, and be
> tradable without interfering with my day job."
>
> Howard also offered a suggestion for evaluating your fitness
function
> by running several different functions against the same data,
> printing out the graphs, and then eyeballing the equity curves to
see
> which one was most appealing to you. In practice you may be
surprised
> at what ultimately appeals to you.
>
> ***
>
> In his most recent book (The Evaluation and Optimization of Trading
> Strategies), Robert Pardo offers up a suggested metric that he
calls
> PROM (Pessimistic return on margin) which is very much like what
you
> are describing in that he adjusts downwards the number of winning
> trades (by the square root of the number of winners) before
> multiplying by the average win. Similarly, he adjusts upwards (by
the
> square root) the number of losing trades before multiplying by the
> average loss. Using this adjusted gross return, he then calculates
> the annualized rate of return over margin (assuming futures
trading).
>
> Pardo also advocates measuring performance against perfect profit
> where perfect profit is defined as the sum total of all of the
> potential profit that could be realized by buying every bottom and
> selling every top. He refers to the ratio of Net Profit/Perfect
> Profit as "model efficiency".
>
> ***
>
> In direct contrast to many of the measures above, Ralph Vince (The
> Handbook of Portfolio Mathematics) belittles any calculation using
> MDD as being delusional. 50 tosses of a fair coin can and will
result
> in 50 straight losses from time to time. Does that imply that the
> probabilities have changed at all just because your backtesting
only
> saw 25 losing tosses earlier?
>
> He states that two systems are best compared by determining their
> geometric means as calculated at their "optimal f", and the
> comparrison of the optimal f itself, on a two dimensional scale
such
> that the higher geometric mean with the lower optimal f is the
better
> system.
>
> I haven't finished this book yet, but he introduces a revised
> approach later which addresses the very real problem of optimal f
in
> that the drawdowns can be severe, thereby precluding most traders
> from following through on the principle.
>
> ***
>
> Your DLL sounds appealing. Would you be willing to share it with
> others?
>
> Mike
>
> --- In amibroker@xxxxxxxxxxxxxxx, "dloyer123" <dloyer123@> wrote:
> >
> > Has anyone done quantitative testing to pick the best fitness
> > function?
> >
> > Car/mdd seems to work well, better than k-ratio or sharpe in my
> > personal testing.
> >
> > Haven't tried Ulcer performance index.
> >
> > I just woundered what others have found.
> >
> > Since the fitness function has such a major impact on the system
> that
> > results, it seems that a good fitness function should help the
> system
> > perform well in walk forward testing. This could be measured by
> walk
> > forward testing the same system with different fitness functions
> and
> > comparing how they do in the out of sample data.
> >
> > I have been experimenting with a new fitness function I
> > call "pessimistic car/mdd". It uses a dll to resample the trade
> list
> > after each run 10,000 times, similar to a bootstrap method. It
> finds
> > the histogram of the resampled car and mdd values, then
calculates
> > car/mdd based on average car - 1 stdev and mdd + 1 stdev.
> >
> > The goal of this fitness function is to minimize the impact of
data
> > mining.
> >
>
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