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PS: The system is purely rotational, fwiw.
--- In amibroker@xxxxxxxxxxxxxxx, "jtelang" <jtelang@xxxx> wrote:
> Pal,
>
> I certainly do not see (and understand why) the results would be
> RADICALLY different. I tried what you suggested, and obviously,
near
> the start of second period, the results were slightly different,
but
> eventually, the system caught up, and produced nearly identical
> equity curve as the first test.
>
> Jitu
>
> --- In amibroker@xxxxxxxxxxxxxxx, "palsanand" <palsanand@xxxx>
wrote:
> > Hi,
> >
> > Here is an interesting observation on system testing:
> >
> > Say you run a system test over 10,000 bars of data, then print
out
> a
> > chart of the system's equity line. Then repeat the test, but
start
> > 100 bars later. Let's say two trades were included in those 100
> bars,
> > so they've been dropped. Now print the second equity line and
> compare
> > it to the first. You'd get exactly the same equity line, but 100
> bars
> > shorter. Right? Wrong!
> >
> > When I do this I get a radically different equity line on the
> second
> > test, i.e., they are not near-mirror images of each other. My
hunch
> > is that a form of the chaotician's "butterfly-effect" has arisen:
> > changing any given trade's market position (long, short, flat)
will
> > effect in a chain reaction all the subsequent trades in complex
and
> > unexpected ways. Here dropping the first two trades could very
well
> > change the system's market position when the third trade is
> > calculated, and so on.
> >
> > I believe this observation has profound and unfortunate
> implications
> > for the robustness of system testing. It's a second and more
subtle
> > problem that lies behind the mere curve-fitting/optimization
> problem.
> >
> > If dropping a couple of early trades will always effect later
> trades,
> > then there's no truly "neutral" starting point with any test
data.
> > Where your test data starts determines the final test results
just
> as
> > much as your system does.
> >
> > The success or failure of many different mechanical systems is
> > predicated to a surprising and varying degree on the sequence of
> > events just prior to the first actual trade generated by the
> system.
> >
> > The trade setup and timing of the first trade can have a profound
> > effect on the subsequent trading results. The circumstances and
> > timing of entry into the first trade can sometimes make a huge
> > difference in the overall trading performance.
> >
> > Regards,
> >
> > Pal
> >
> > --- In amibroker@xxxxxxxxxxxxxxx, "Fred" <fctonetti@xxxx> wrote:
> > > That IS what I was trying to say. I suspect because equity
feed
> > back
> > > is like looking in a rear view mirror, great for letting us
know
> > > where we were and how we could have adjusted the past to make
it
> > > better, but that's about it.
> > >
> > > --- In amibroker@xxxxxxxxxxxxxxx, "Dave Merrill"
<dmerrill@xxxx>
> > > wrote:
> > > > don't think I get what you mean here fred.
> > > >
> > > > you can't be saying that metrics on the equity curve of a
> trading
> > > strategy
> > > > or its parameters aren't useful, right? that's the only thing
> we
> > > have to
> > > > judge the effectiveness of our methods and settings.
> > > >
> > > > so you must be saying that equity feedback isn't a useful
> concept,
> > > > regardless of how you measure "good" equity. do I have that
> right?
> > > >
> > > > if so, as I've said, my experience agrees -- none of the
> > indicators
> > > I've
> > > > tried are wonderfully profitable when auto-optimized this
way.
> I
> > > just cannot
> > > > for the life of me understand why that's the case, if
backtests
> > > tell us
> > > > anything useful about future performance.
> > > >
> > > > if I've misunderstood completely, my apoligies (:-)
> > > >
> > > > dave
> > > > Like a lot of other things that sound like they SHOULD
work,
> I
> > > have
> > > > never found metrics related to equity curve feedback to be
of
> > much
> > > > value in the determination of system parameter values.
> > > >
> > > > --- In amibroker@xxxxxxxxxxxxxxx, "Dave Merrill"
> <dmerrill@xxxx>
> > > > wrote:
> > > > > interesting as usual howard (:-). one piece I wanted to
> drill
> > > into
> > > > a bit.
> > > > >
> > > > > I wonder what the effect of using performance measures
that
> > > > concentrate on
> > > > > certain things at the expense of others actually is.
> > > > >
> > > > > for example, my auto-optimization stuff currently uses
> simple
> > > > profit per bar
> > > > > to choose parameter values. my gut-level assumption was
> that
> > > since
> > > > it was
> > > > > ignoring drawdown (among other things), the resulting
> systems
> > > might
> > > > have
> > > > > higher drawdown than I was comfortable with, but that
> profit
> > per
> > > > bar should
> > > > > be as good as the trading method could produce.
> > > > >
> > > > > maybe that's not the case. maybe by choosing a more
balanced
> > > > success metric,
> > > > > not only would the other factors not considered by my
> > simplistic
> > > > first pass
> > > > > metric be improved, but profitability might be improved
as
> > well.
> > > > >
> > > > > is this something you've investigated or thought about?
> > anyone
> > > else?
> > > > >
> > > > > dave
> > > > > Note ? it is perfectly valid to have different objective
> > > > functions for
> > > > > different purposes. For example, I might be modeling the
> > > behavior
> > > > of a
> > > > > sector, say oil services, with the intent of trading
> > individual
> > > > stocks based
> > > > > on what I learn. In this case, I want to identify
periods
> of
> > > > rising prices
> > > > > with careful attention to turning points, but without
much
> > > interest
> > > > in
> > > > > overall profit. On the other hand, I might be modeling
> > > individual
> > > > high beta
> > > > > tech stocks, in which case my model includes several stop
> loss
> > > > techniques
> > > > > and I care most about avoiding drawdowns.
> > > > >
> > > > >
> > > > >
> > > > > Thanks,
> > > > >
> > > > > Howard
> > > >
> > > >
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