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True, not to mention that many are based on distribution assumptions
that don't apply. However, I was referring to applying them to the
*interaction* of a robust system with a nonstationary time series
(which is what criteria 3-5 are) which acts as a transformation to
nearly achieve wide sense stationarity. So that, applied with
understanding, will as I said give *insight* into whether or not a
system's out of control.
--- In amibroker@xxxxxxxxxxxxxxx, "Howard Bandy" <howardbandy@xxxx>
wrote:
> Hi Mark -
>
>
>
> Thanks for the confirmation. There is one point in your posting
that I'd
> like to comment on.
>
>
>
> "Then there are several Statistical Process Control (SPC)
> techniques that, given criteria 3-5 results, will give you insight
> into whether or not the system's out of control. "
>
>
>
> I agree that there are SPC techniques that can apply to evaluation
of
> trading systems. But it is important to keep in mind that we are
dealing
> with non-stationary, time series. Many of the SPC techniques were
developed
> for stationary, non-time series, which are Much easier to model and
Much
> easier to monitor.
>
>
>
> Howard
>
>
>
> -----Original Message-----
> From: quanttrader714 [mailto:quanttrader714@x...]
> Sent: Monday, November 10, 2003 9:44 AM
> To: amibroker@xxxxxxxxxxxxxxx
> Subject: [amibroker] Re: On Robustness, Post #1 : TO HOWARD
>
>
>
> Not to be a naysayer, but... I agree with Howard in principle,
> however, the devil's in the details. My view: the OOS I'm concerned
> with are actual trades. I personally consider existing data in
sample
> so applying what Howard suggested in this context is reconciling
real
> time results with expected results to see if the system is still
> performing acceptably. And that's more difficult in practice than
> developing robustness criteria and finding robust systems. IMO the
> results from criteria 3-5 are sufficient to use as expected results
if
> done keeping the cautions I posted in 4 & 5 in mind and applying an
> appropriate adjustment to the simulations as I also mentioned in
that
> post because, as Howard said, OOS results are almost always less
> profitable. Then there are several Statistical Process Control
(SPC)
> techniques that, given criteria 3-5 results, will give you insight
> into whether or not the system's out of control. Or, given *a lot*
of
> experience, one could even eyeball it. But it certainly ain't
simple.
>
> Several have commented here and privately that all this seems
awfully
> complicated. So instead of another Edison quote, let me suggest
> looking at (find w/google) some of Caxton's research (Bruce Kovner,
> first Market Wizards book, started by borrowing $3,000 and now #111
on
> Forbes 400 list of wealthiest Americans).
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Gary A. Serkhoshian"
> <serkhoshian777@xxxx> wrote:
> > Thanks Howard. Makes sense, and seems simple to implement. With
> Tomasz adding MCS into AmiBroker, life will only get sweeter : )
> >
> > Kind Regards,
> > Gary
> >
> > Howard Bandy <howardbandy@xxxx> wrote:
> >
> > Hi Gary -
> >
> >
> >
> > I was thinking of looking at the recent trades in the out of
sample
> period. We can get an idea of what the possible distribution of
> various metrics are by looking at the in sample results. But the
out
> of sample results are (almost) always less profitable, have a lower
> ratio of wins to losses, etc than the in sample results. One
> technique I use is to run a quick and dirty monte carlo program I
> wrote in Basic that gives the likelihood of various metrics -
> such as
> the proportion of winning versus losing trades. If the out of
sample
> results start falling in the area of the distribution that is
> "unlikely", then I have a warning that the system may be
> broken.
> >
> >
> >
> > Howard
> >
> > <<<<SNIP>>>>
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