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Mmmmmm, the gloves are coming off it appears.
If I look at the site mentioned, I see a spectral chart used to
illustrate the concept of optimisation of strategies. I see an
example of the space one has to deal with for a simple, cross
over strategy. I don't really care about the trading costs at
this stage, since I am interested in the specific topology I
have to deal with. Dealing costs will be added once I understand
this, as will proper tests to ensure I am not overfitting and so on.
The spectral chart indicates the dangers of just using the Fibonacci
numbers, very popular at that time for that particular market,
and the benefits that can be obtained by still using a very simple
strategy but just fine tuning it a bit.
Interesting to note that the site you refer to was cancelled in the
previous century some time and basically lives on for historical
purposes. I can assure you the weaponry used today are much more
sophisticated, though it derives from what must have taken you an
awful lot of time to find on the internet, and for which I have to
congratulate you!
One thing I have to agree with, and, trying to steer this back to
the Theta model somehow, is that overfitting is dangerous. In the
Theta model that we utilise, we do not optimise, we simply use it
with an arbitrary period selection.
I am well aware of the high volatility in higher frequency data, but
this does not mean that it can not be used profitably. In a geared
environment, you often simply do not have the luxury of riding out a
position for a couple of days. Even if you were spot on say a week
from now, market movements in the interim may have wiped out your
margin and closed you out at a huge loss - that will cost you plenty
a shirt. So sometimes you have to use shorter term data. We have
tested a variety of series on 5, 10, 15, 30, 60 and 120 and above
minute bars and, interestingly, many can be traded profitably at 30
minutes. We thus prefer this bar length when working with intraday
data.
Finally, let us try and keep this to the Theta model in particular
and in general to technical indicators of interest.
Regards
MG Ferreira
TsaTsa EOD Programmer and trading model builder
http://tsatsaeod.ferra4models.com
http://www.ferra4models.com
--- In equismetastock@xxxxxxxxxxxxxxx, "Jose" <josesilva22@xxxx> wrote:
>
> "...and optimise on periods. When this line goes above zero, buy, and
> when below, sell. Please let us know the results."
>
> And please don't trade the above, because you are sure to lose your
> shirt on it.
>
> Whilst it's taken for granted in trading circles that optimization to
> historical data (curve-fitting) very seldom eventuates into a
> profitable strategy in the real world of trading, apparently this is
> not something yet discovered in the theoretical & jargon-laden quant
> world.
>
> Here is a perfect example of theoretical/optimization modeling gone
> wrong:
> http://www.quant.co.za/OptStrat2.html
>
> Basically, the author claims to have optimized a profitable trading
> strategy based on optimized SMA crossovers (and no transaction costs),
> and comes to the naive conclusion that the shorter-term indicators
> generate a much higher profit:
>
> Daily Data (Four Years) 1,335 points total 334 points a year
> Hourly Data (Two Years) 1,380 points total 690 points a year!
>
> The problem with the above - as anyone with any basic backtesting &
> trading experience will know - is that short-term trades generate a
> prodigious amount of whipsaw and brokerage/slippage/spread costs,
> turning any theoretical strategy into a real-life losing strategy.
> Both examples above are very likely to produce major losses once
> transaction costs are factored in.
>
> The above mistaken conclusion could be forgiven if it was the work of
> some inexperienced bright-eyed young system developer straight out of
> school, but the it actually came from someone that claims a very
> impressive CV.
>
>
> In the laboratory-like world of theoretical system optimizing/
> modeling, transaction costs and liquidity issues that overwhelm
> profits in the real world are often ignored. Numerous essays and
> white papers are written by academics & pseudo-academics each year,
> trying to model a system more complex than the most complex of weather
> patterns, yet offering very little insight into the mass psychology
> and real-life vagaries of the markets.
>
> Caveat Emptor.
>
>
> jose '-)
> http://www.metastocktools.com
>
>
>
> --- In equismetastock@xxxxxxxxxxxxxxx, "MG Ferreira" <quant@xxxx>
> wrote:
> >
> > Hi Andrew,
> >
> > Let us just backtrack a bit. I noted, when coding the T3 and IE/2,
> > that the IE/2 appeared to be similar to the Theta model, which I
> > know to be a good model. So I did not bother to test it, assuming
> > it also to be a good model. The Theta model implementation provided
> > is based on what can be done in Metastock in a very short time span,
> > and was given on request, and has maybe too many shortcuts in it.
> > If anybody has ever tested the IE/2, I think we can safely use that
> > as a proxy for the Theta's performance and vice versa.
> >
> > Now, since I appear to be the defender of the Theta model. We (yes,
> > I happen to be part of a team) use the Theta model extensively to
> > prepare short term forecasts of monthly data, such as M3, CPI,
> > wholesale trade and so on. We use it as part of an array of models
> > and we never use the results of just one model, but the Theta model
> > shines in this capacity as a good performer and often has a fairly
> > large dynamic weight allocated to it. Here performance is measured
> > in forecasting accuracy, which usually is a poor indication of
> > whether it will work in a trading environment.
> >
> > But we also use this model, for end-of-day data, in a trading
> > environment as part of yet again a suite of models. This is quite
> > fashionable and dicated by theory as well - using a suite of models,
> > and I am in a way recommending this to the group and also
> > recommending the inclusion of the Theta in such a suite.
> >
> > Now, let us not run away from the real point, testing the Theta
> > model as a singular trading model. I note your observation, as well
> > as that of some other members of this group, and can well believe it
> > - that the Theta did not perform well when you tested it.
> >
> > This is true of prediction models in general, so allow me to expand
> > a bit. A good prediction model is supposed to predict where the
> > market will be in future, say tomorrow. Now, if it is a good
> > projection model, then it will be unbaised, so that the market will
> > be above it about 50% and below it about 50% of the time. The
> > residual or error for a good model will be random. So if we use a
> > prediction model as is, we are trading white noise, and should not
> > get good results. So we have to apply our minds a bit. I am
> > thinking aloud, why is the Theta not performing as I would expect,
> > so please bear with me.
> >
> > In our trading model, we do use the Theta model's prediction as well
> > as its slope. So we extrapolate the model and note the slope of
> > this extrapolation and we use both in the model. We have noted that
> > when the Theta long term line (theta = 0) turns, it often indicates
> > a turnaround in trend. This could be a better way to build a
> > trading model, using the slope of the long term component. The
> > slope of the extrapolation is in fact half the slope of the long
> > term component, since the extrapolated short term is constant and
> > the Theta is
> >
> > ( lt + st ) / 2
> >
> > so
> >
> > d( lt + st ) / 2 = dlt / 2
> >
> > since
> >
> > dst = 0
> >
> > Another note, we often take the log of the series before we
> > calculate the slope, but this should not make a big difference in
> > many cases.
> >
> > Anyhow, try the following test
> >
> > linregslope(log(CLOSE),periods)
> >
> > and optimise on periods. When this line goes above zero, buy, and
> > when below, sell. Please let us know the results.
> >
> > Note that the parameter should be on the long side. It should
> > ideally be above 30 for a number of statistical reasons that I'd
> > rather avoid for now. I think a good starting point would be 50
> > days and test up to at least 250.
> >
> > Regards
> > MG Ferreira
> > TsaTsa EOD Programmer and trading model builder
> > http://tsatsaeod.ferra4models.com
> > http://www.ferra4models.com
> >
> > PS : I *really* appreciate your opening sentence.
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