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Overall the concept seems fine ...
It shouldn't be any secret from prior posts I've made, some of which
are at more than medium temperature, that my view of systems that are
designed, built, tested and optimized on relatively small time
segements of market data can't possibly be robust except by the
greatest stroke of luck. I've seen people make this mistake in the
past and get burned badly by it. In this regard I think this is one
area where most walk forward testing falls down as it's typically set
up to look at some arbitrary relatively short time segment of recent
market data hoping to captue the essence of what is currently going
on but like a long term SMA it hopelessly lags and has a very short
memory.
So this is where I believe the strengths lie in the process in that
you are ensuring that your initial testing of candidate systems
covers bull/bear/flat market segments.
I was getting a little uneasy with your description of manually
nominating candidates which I'd rather be via some mechanical method
whether it be %/bar or what have you. When you ask about a fast
method do this I am assuming you mean faster than by looping through
and multiplying each bars ROC times the previous bars product while
in trades which is the long part of the process and then taking the
nth root of the result where n is the number of bars you were in
trades. If entries and exits are at Open or Close there are other
ways to do this quickly i.e. for long side only trades ...
Buy = DateNum() == 1030312;
Sell = DateNum() == 1030908 OR
DateNum() == 1030311;
OnBuy = Ref(BarsSince(Buy), -1) < Ref(BarsSince(Sell), -1);
BarsIn = IIf(OnBuy, BarsSince(Buy), 0);
Factor = IIf(OnBuy, C / Ref(C, -1), 1);
CumFact = 100 * (exp(Cum(log(Factor))) ^ (1 / BarsIn) - 1);
Filter = 1;
AddColumn(BarsIn, "BarsIn", 1.0);
AddColumn(CumFact, "CumFact", 1.6);
The above is a little sloppy but demonstrates the idea and you should
be able to check the explore against the backtest to see that it
produces the same result more or less.
As far as the rest is concerned, I don't particularly care for MCS
but I'd also have to admit I haven't done anything with it in a long
time so it's probably best I not comment further on it.
--- In amibroker@xxxxxxxxxxxxxxx, "quanttrader714"
<quanttrader714@xxxx> wrote:
> Not picky, I enjoy the questions and peer review because it forces
me
> to rethink things I've taken for granted in enough detail to (try)
to
> answer coherently. That can only be good, for me at least.
>
> I think there's some confusion because this started out as an
> explanation of *my personal* robustness criteria and bled over into
> other topics such as issue selection, not that that's bad. And oh
by
> the way, some of the robustness tools are great for *part* of issue
> selection as I see it but not all. I think we're lost in the trees,
> so let's step back and look at the forest.
>
> You said you're trying to understand my thinking. OK, but remember
> you asked for it! Let's take robustness and issue selection one at
> time.
>
> 1. Robustness. You've seen criteria 1-5. Criteria 1 and 2 use the
> same 6 years of bull, bear and sideways data. Criteria 3-5 use
*all*
> data that I have for those same stocks and this varies mostly from
> early 80s to sometimes much less, especially w/small caps. So you
> could say I'm doing some sort of OOS during robustness criteria 3-5
by
> looking at performance graphs and simulated metrics based on all
> data, for stocks that were initially selected by their performance
> over only 6 years of data. But with that said, I'm actually trying
to
> use robustness to simplify and get away from all the OOS stuff once
> and for all. With the simple idea being: find systems that work
> most of the time on most stocks and then be concerned with the only
> OOS that really matters anyway, the future. Keep things simple, use
> blunt tools like graphs and the bootstrap and try to minimize
> unhelpful human influence. Want to know what one of
my "proprietary"
> end dates is for one of my 2 year periods? My grandfather's 100th
> birthday. Why? I believe picking a date that way is more robust
than
> thinking too much about it and somehow screwing up the range.
>
> 2. Issue selection. Purpose is to find the best stocks to trade
with
> the robust systems from the previous (robustness) phase. I
personally
> divide issue selection into 2 steps, nomination and confirmation.
> Nomination is the creative part and Steve K could give you much
better
> input on this than me, such as rank ordering stocks by %profit/bar
> (anyone know how to quickly do this for a large list)? But I am
> researching this and trying to do better. Now once nominated by
> whatever metric (perhaps only a single number) I like to confirm
each
> candidate by running them through full criteria 3-5. That gives me
a
> really good feel for potential future performance.
>
> Now to try to answer your question about the anchoring in 1971.
Note
> that I had a caveat about more data being better. This is always
true
> for data from some processes but not always true for market derived
> data. And recall that here we're concerned with the interaction
> between a system and a nonstationary time series. To make a long
> story short there's a tradeoff between having enough datapoints in
the
> basket to provide robust estimators and having so many that they're
> biased toward a distant past that won't re-occur. I am however
> personally more biased toward adding more recent data because I
> believe they're more likely to be more relevant. The best place to
> draw the line is literally a billion dollar question but my approach
> has been a robust one... to simply go with what I have in my
database
> which, so far, has proven sufficient.
>
> Now aren't you glad you asked?
>
> A few questions for you... now that you've seen the robustness
> criteria, what do you think overall and what would you say is the
> concept's greatest strength? Weakness?
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Fred" <fctonetti@xxxx> wrote:
> > I'm not sure I understand the difference in why one would want to
> add
> > data to the confirmation phase and not the selection phase or is
> that
> > your flavor of OOS testing ? That aside for a moment if as you
say
> > criteria 3-5 work better with more data then wouldn't one want to
> > anchor some beginning point in time long ago like 1971 or
whatever
> > and use all data from then up through current POSSIBLY leaving
some
> > segment out for OOS testing ? and then as time goes along include
> > more recent data as in sample for re-evaluation of the systems
> > robustness and issue selection and confirmation ?
> >
> > Note: I'm not trying to be picky here, I'm only trying to
> understand
> > your thinking which in at least SOME ways appears to be getting
> > closer to my own.
> >
> > > When you say issue selection, are you talking about the
> nomination
> > or
> > > the confirmation part? If confirmation, it would be better to
> *add*
> > > the new data in as they become available because criteria 3-5
> (when
> > > used for robustness or confirmation) inherently work better
> (within
> > > limits) with more data. If nomination (for example using an
> > > algorithm)maybe but I doubt it because I've never been able to
> wring
> > > much advantage out of OOS techniques (with data that exists)
and
> > that
> > > sounds like a twist on OOS if I understand you correctly. But
> still
> > > worth looking into. I've been surprised before.
> > >
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