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"...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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