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Hello Simon,
Great question.
I have an interest in Single Sample Testing (SST) and pushing the
boundaries there. It is a big NO, NO to the 'defenders of the faith'.
I also have a strong bias to simple systems. No, or few, indicators
with lookback periods etc (I don't use many rules/lose degrees of
freedom) hence my interest in the subject.
My gut feeling tells me I can do it but I haven't got far with the
proof (however that doesn't mean much since there are terabytes of
books and academic research, out there, that I am totally unaware of).
Personally, I think SST only has academic interest.
I am following it because I am curious, I learn from the enquiry and
I love to confound my critics.
So, possibly your friend is correct but if s/he is absolutely certain
about it s/he would be capable of writing a book on evaluation - in
fact if that is the case, I wish s/he would, thereby saving me a lot
of time and trouble.
Anyway, over to the here and now.
> My question is, does anyone know if the data-mining bias can be
> considered irrelvant when the sample size is so large? (in this
>case,
> the sample size is roughly 8400 trades).
Possibly I can ride my motorbike, at 200mph, going the wrong way up a
6 lane highway but what is the point if I just want to get from A to
B - am I going somewhere or thrill seeking?
Here are some rules from my notebook:
- good data, relevant to current conditions, is scarce. Why waste it?
- sample error is real
- around 300 to 400 trades is the minimum, with no further
substantial minimization of sample error beyond, around 10,000
- there is a sweet spot around 1,000 - 5,000 trades
- if data is short then work with no less than 3-400
- if data is in plentiful supply (intraday?) then use more
- one sample might be good enough (in exceptional circumstances/for
exceptional traders) but why not reduce risk and use more (if you
have the data)
- 1 IS and 1 OOS is better than 1 IS alone
- even though I am interested in SST, and more likely than most to
succeed with it, I am actually using several OOS, of optimum length,
whenever I can.
No, 8400 trades, in a single IS test, does not guarantee success (it
is very easy to find rare cases, on a computer, because we can work
our way through such large datasets in a relatively short space of
time - 1 in a million chance in real life === 1 in a backtest chance
on a computer).
We can't rely on stats alone - they never give a definitive answer.
Different story if your friend has observed a persistent, and
predictable, market inefficiency and the stats are just confirming
and quantifying that.
>Put another way, with so many
> observations, how many different rules would have to be back tested
>in
> order for data-mining bias to creep in?
I am still mulling over this point.
What is the least number of rules that a useful system could be
described in? Perhaps three rules would be the least that anyone is
successfully using (I don't know - I am wondering how many is the
least possible).
Say I have a system with only three rules - if I test it IS and
change 1 rule a little bit I am still tuning the system to that data,
aren't I?
If I have a system with only three rules, test in IS, and it is
successful, then test it OS and it is successful, all I am doing is
confirming that the system is tuned to those two particular datasets,
aren't I .
Based on those observations I would say that, since we can't avoid
data mining, even with simplistic methods, then we are always 'data
mining' when we use historical data.
The only time we are not datamining is when we are live trading.
OOS testing is the historical surrogate for live trading, in that at
least the data is unknown, to the system, prior to walkforward or OOS.
The only thing about datamining that varies, when we are using
historical data, is the degree.
The more rules + the greater the range of adjustble parameters within
the rules == the more likely we are to be 'fooled by randomness'.
In short - no matter what we do we can never achieve 100% certainty
but OOS and live paper trading will minimize the risk compared to SST
alone.
Some food for thought:
Data mining, per se, is not the only thing on the list of 'rocks that
traders dash their ships on' - there's more on the same list (most of
them receive a lot less publicity).
brian_z
brian_z
--- In amibroker@xxxxxxxxxxxxxxx, "si00si00" <si00si00@xxx> wrote:
>
> Hi all,
>
> I have a friend who has developed a trading system. It is an
intraday
> system that makes on average around 5 futures trades per day. We
were
> discussing it the other day and a point of disagreement arose
between
> us. He claims that there is no necessity for him to test the
strategy
> on out of sample data because he has back tested it using over 8
years
> of historical intraday data, and the patterns the strategy predicts
> occur 70% of the time or more.
>
> My question is, does anyone know if the data-mining bias can be
> considered irrelvant when the sample size is so large? (in this
case,
> the sample size is roughly 8400 trades). Put another way, with so
many
> observations, how many different rules would have to be back tested
in
> order for data-mining bias to creep in?
>
> Thanks in advance for any thoughts you might have!
>
> Simon
>
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