Re: [amibroker] Re: Expectancy - and related--specifically K-rato, AmiBroker Email List Archive

On some particular index or tradable something on the order of a ZigZag with a Linear Regression Channel on the individual legs is probably of more value as the context in which to use something like K-Ratio although I personally have only used K-Ratio as an evaluation for how straight the equity curve is. In the latter scenario when ones system is doing any compounding over the life of the equity curve then having the standard arithmetic scaling of equity that is typically used in K-Ration as opposed to Log of Equity is not particularly meaningful.

----- Original Message -----
From: brian_z111
Date: Sunday, May 10, 2009 10:37 pm
Subject: [amibroker] Re: Expectancy - and related--specifically K-rato
To: amibroker@xxxxxxxxxxxxxxx

> > Near Random ?
> > You're kidding right ? ... I'd thought you had beeen around
> longer >than that ...
>
> All of your points aren't entirely clear to me but:
>
> I'm talking about daily bars (say the US S&P500) - my testing
> shows that non-randomness is not significant.
>
> Inefficiencies do exist but we have to look for them elsewhere.
> Opportunities do exist but we have to exploit them in ways other
> than using structural models.
>
> > On another level the number of trades has no relevance in
> calculating K-Ratio
>
> I take it you want me to think again about the K-ratio.
>
> However... K-ratio is a slope/stderror comparison ... so slope
> doesn't vary with N or the order of the trades? .... stderror is
> stdev inclusive ... stdev tails out in both directions with time?
>
> >and as calculated in AB is useless for anything but systems
> that >trade constant dollar amounts ...
>
> I haven't delved into the K-ratio in detail.
> I am not a fan of equity curve analysis so I haven't thought
> about the K-ratio in depth.
>
> I noticed that Kestner based his calcs on a constant contract
> basis and then used logN to equate compounded eqcurves to a
> constant contract basis ... are you saying that there is a
> mistake in his thinking or math at that point?
>
>
> I recall you mentioned this before but I didn't follow up
> because last time I looked the K-ration in AB was exactly as
> written in KL's last book?
>
> How do you calculate it and how do you use it?
>
>
>
> >By definition that would imply no degree of success over a
> long >period of time.
>
> With methods that are dependent on predicting the future? ....
> yes it does!
>
>
> --- In amibroker@xxxxxxxxxxxxxxx, ftonetti@xxx wrote:
> >
> > Near Random ?
> >
> > You're kidding right ? ... I'd thought you had beeen around
> longer then that ...
> >
> > On another level the number of trades has no relevance in
> calculating K-Ratio and as calculated in AB is useless for
> anything but systems that trade constant dollar amounts ... By
> definition that would imply no degree of success over a long
> period of time.
> >
> > ----- Original Message -----
> > From: brian_z11
> > Date: Sunday, May 10, 2009 8:58 pm
> > Subject: [amibroker] Re: Expectancy - and related--
> specifically K-rato
> > To: amibroker@xxxxxxxxxxxxxxx
> >
> > > My understanding of Bob Pardo's book is that he feels
> > > that the length of the
> > > out-of-sample period can be determined by a calculation
> based on
> > > the length
> > > of the in-sample period. If that were true, then the ratio
> > > would be a
> > > little easier to compute. But, in my experience, there is no
> > > relationshipbetween the length of the out-of-sample period
> and
> > > the length of the
> > > in-sample period. And gathering the data and performing the
> > > calculation of
> > > the ratio for some objective functions would be difficult.
> > >
> > > I don't believe that it would be worth the effort but
> setting
> > > tbe BT the task of collecting the same number of trades in
> the
> > > OOS test as were collected in the IS test is the way to
> > > standardise and simplify calculating this ratio.
> > >
> > > N == the number of closed trades in a test run;
> > > Where IS(N) == OOS(N) the ratio IS(N bars)/OOS(N bars) will
> > > always be defined as a probability distribution.
> > >
> > > No constant relationsip, between IS and OOS length (in bars)
> can
> > > exist because of the (near) random nature of the markets.
> > >
> > > Believe that such a relationship does exist, and hence can
> be
> > > calculated/exploited, is an artefact of the belief that the
> > > markets are non random i.e. have a structure that we can
> > > identify, model and therefore use to make predictions about
> the
> > > future of the markets.
> > >
> > > Any attempt to improve OOS performance by 'tuning' the
> length of
> > > the sample periods is a futile excercise.
> > >
> > > The exception to this, as you point out in your book, is
> that
> > > the markets do change (they are not quite random .... human
> > > behaviour leaves a faint trail of non randomness) and so
> current
> > > data may be more relevant than non- current data e.g.
> increased
> > > use of computers, and access to information, by traders may
> have
> > > changed the markets in the last decade or two.
> > >
> > > Whether these changes are strong enough, and occur over
> short
> > > enough time periods or identifiable time periods, to justify
> the
> > > view that we can/must account for this, in our design
> processes,
> > > is arguable i.e. it is questionable that we can "synchronise
> the
> > > market and our trading system by shortening (varying) the
> time
> > > periods used in our walk forward testing", as you suggested
> in
> > > your QTS book (P261).
> > >
> > > Granted that we are attempting to synchronise our systems to
> > > market behaviour but this is done within our system rules,
> not
> > > by altering the N bars tested.
> > >
> > > The fundamental premise of trading is that the system will
> > > perform, in the future, irrespective of market conditions or
> the
> > > time period involved.
> > >
> > > The OOS walk through is a test to find out if the patterns
> we
> > > identified in the IS data still exist in the future and that
> the
> > > code we used to synchronise to that pattern(s) is effective.
> > >
> > > The only time that will be time dependent is when the
> patterns
> > > are time based e.g. the number of times the price of oil
> goes up
> > > on the first day of the month is statistically significant.
> > >
> > > Very few exploitable inefficiencies in the markets are time based.
> > >
> > >
> > > On another point:
> > >
> > > Theoretically the OOS results should be better/worse than
> the IS
> > > tests on a 50/50 basis.
> > >
> > > Continual skewing of that ratio to the downside is an
> indicator
> > > of curve fitting at the IS stage?
> > >
> > >
> > >
> > >
> > >
> > > --- In amibroker@xxxxxxxxxxxxxxx, Howard B wrote:
> > > >
> > > > Greetings all --
> > > >
> > > > In-sample results and out-of-sample results can be, and
> > > usually are, very
> > > > different in their characteristics.
> > > >
> > > > My experience is that the ratio (OOS/(IS+OOS)), where
> these
> > > are the
> > > > in-sample and out-of-sample results, is difficult to
> compute
> > > and often even
> > > > difficult to define. I have not found it to be of value in
> > > estimating the
> > > > future performance of the system.
> > > >
> > > > My understanding of Bob Pardo's book is that he feels that
> the
> > > length of the
> > > > out-of-sample period can be determined by a calculation
> based
> > > on the length
> > > > of the in-sample period. If that were true, then the ratio
> > > would be a
> > > > little easier to compute. But, in my experience, there is
> no
> > > relationship> between the length of the out-of-sample period
> and
> > > the length of the
> > > > in-sample period. And gathering the data and performing
> the
> > > calculation of
> > > > the ratio for some objective functions would be difficult.
> > > >
> > > > Isn't it the out-of-sample results we are trying to
> estimate?
> > > Do we care
> > > > what the in-sample results look like? If the out-of-sample
> > > results are
> > > > terrible, why bother computing the ratio. If the out-of-
> > > sample results are
> > > > good, why bother computing the ratio -- how will that
> > > information be used to
> > > > improve the system? And if it is used to modify the
> system,
> > > then the
> > > > previously out-of-sample data has become in-sample.
> > > >
> > > > Do any of the forum member have examples they can
> contribute
> > > where computing
> > > > the ratio is helpful?
> > > >
> > > > Thanks,
> > > > Howard
> > > >
> > >
> > >
> > >
> >
>
>
>