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Thanks for the code and your thoughts. On MCS, think you should take
another look. If I were limited to just one statistical tool, MCS
would be it. Almost no limit to what one can do with it, if applied
properly.
--- In amibroker@xxxxxxxxxxxxxxx, "Fred" <fctonetti@xxxx> wrote:
> 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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