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Is 20% PA EOD good?
Run this on YHOO using Yahoo data - I have a 97 to 2006 database.
Buy = DayOfWeek() == 1;
Sell = DayOfWeek() == 5;
BuyPrice == O;
SellPrice == C;
What PA% do you get (it is the first stock I picked to try)?
Should I backtest?
It gets rid of all of my bad ideas (a Dennisism!)
If I can trade now, without a computer and backtesting, it is only
because of the thousands of hours of computer trading hours I have
under the belt.
You have to generalise before you can specialise.
brian_z
--- In amibroker@xxxxxxxxxxxxxxx, "louisprefontaine" <rockprog80@xxx>
wrote:
>
> Hi everyone,
>
> I am not at home right now, and it's really a pleasure to read you
> while drinking this marvelous Côtes-du-Rhônes. I really appreciate
> all the ideas you shared with me (and the group).
>
> I must say that everyone seem to have different visions of the
> problem, with people focusing on walk-forward optimizations, others
> on specific date backtesting and still others (someone who
contacted
> me in private) refuse to backtest and want to trade directly with
> easy to follow rules.
>
> For what I have read (and I will re-read tomorrow, at home), I need
> more data if I want to follow the « rule » of the 30 trades. Right
> now, my system is based on a major index and it issues only about 3-
> 5 signals a year (which, at a 20% portfolio ratio is 15 to 25
> trades), so I would need between 6 to 10 years of data, which of
> course is impossible to do because we all know the market 10 years
> ago as nothing to do with what it is right now.
>
> On the other side, I could use minimal backtesting, but then the
> data-mining bias would increase, considering that my system has
only
> a very limited of trades each year. Let's say that if I use only
> one year back-testing (a bull market, a sideway market, and a bear
> to sideway market), that would be about 3-5 trades. How can I say
> with certitude that the gains are not due to luck on such a small
> amount of signals on the major index? Even if I get 30 trades from
> buying the stocks linked to the index, this may still be only data-
> mining to the major index, as the stocks tend to follow that
index.
> (As an example, if I data-mine perfectly the Dow Jones, chances are
> that buying the 30 companies in the index will give a good
> result... I would have a lot more trades, but in fact they would
be
> based only on the same data-mined Dow Jones index...) --BIG
PROBLEM-
> -
>
> Finally, there is the suggestion of going intraday. I'd like to do
> this, butmy RT data provider only provides 1 year of intraday
data.
> Do you know other provider who gives more?
>
> And finally finally... Are you sure Brian one can expect 40% per
> annumm on EOD data? This seem like very very good!
>
> My strategy right now is based on a very limited number of trades
> with extra-limited drawdowns (I need to thank a member of this
board
> who helped me with this... You know who you are... Thanks again!).
> So I can put maximum margin and boost the results. But with extra-
> little trades comes the problem of significance of the results: are
> the results good because the system is good or are the results good
> be cause of good luck?
>
> That was the purpose of the first message, and so far I have new
> ideas but I am still wondering what I should do.
>
> Louis
>
>
>
> --- In amibroker@xxxxxxxxxxxxxxx, "brian_z111" <brian_z111@>
> wrote:
> >
> > > I agree that there is a serious problem when the only data that
> is
> > >available
> > > contains no period that is similar to what is expected in the
> > >future.
> > >
> >
> > Getting enough data is an issue for EOD traders.
> >
> > A few possible solutions I have mentioned in the past (I
> like 'live'
> > work but the negative is that it doesn't persist - unlike a book).
> >
> > - new traders should work in old EOD data, say 1995-2000, until
> they
> > address all of the basic issues, like length of IS versus OS etc.
> >
> > They should save up the best years (the current 5) until they
> start
> > to get good == backtests > 30-40% per annum on OOS tests and then
> > move to fresh and/or bought data for confirmation/trading.
> >
> > (of course we know that a lot of ideals will never make it to
> common
> > practice - some are just too hard to sell).
> >
> > - use other markets (that is why I highlighted the S&P global
1200
> in
> > a UKB post) - a US trader could practice on the ASX top 20 for
> > example - ASX market behaviour of the 20 most liquid stocks is
> > similar to the US top 100 or 200.
> >
> > - become an intraday trader (plenty of data then)
> >
> > - take a ten year history that included different market
> conditions,
> > filter it for liquid stock (for concept testing I like only stock
> > that trade everyday - no data holes - in real time I know when a
> > stock isn't trading) - sort the data by 10 year performance i.e.
> 10
> > year % return - assign them an ordinal number - then put every
> even
> > stock in an IS testing watchlist and every odd stock in an OOS
> > testing watchlist.
> >
> > Now you have a 10 year IS and OOS database with a range of
> conditions
> > and equal numbers of bullish and bearish stock.
> >
> > I have done that with the most liquid stock in Jim's Yahoo
> database
> > and I am comfortable working with it like that.
> >
> > > Artificial data has no value.
> >
> > One exception is for training.
> >
> > I have learnt a lot using (crude) randomly generated data as a
> > training benchmark - I regard the Black Swan as my adversary so I
> > have studied his/her habits in depth.
> >
> > The beauty of RGD is that, while it is not real, it is
> lifelike,and
> > more importantly, we know in advance what it's real performance
is
> > (W/L ratio, %period returns).
> >
> > I can't recommend that type of synthetic trading highly enough.
> >
> > In all other trading tests we never ever have certainty about
> those
> > numbers - I love the certainty of simulated data for comparing
> real
> > behaviour to theoretical behaviour (if they don't mactch then I
am
> > not confident my theories will stand up in real life).
> >
> >
> > brian_z
> >
> >
> >
> > --- In amibroker@xxxxxxxxxxxxxxx, "Howard B" <howardbandy@> wrote:
> > >
> > > Hi Louis --
> > >
> > > I agree that there is a serious problem when the only data that
> is
> > available
> > > contains no period that is similar to what is expected in the
> > future.
> > >
> > > Artificial data has no value.
> > >
> > > Using data that is earlier in time than the in-sample period
has
> > limited
> > > value. You can test earlier data, but you will over-estimate
the
> > > performance that you can expect in the future.
> > >
> > > Are there other tickers that are closely related that have data
> for
> > the
> > > periods you would like to test?
> > >
> > > In the end, you will need to make a decision on whether to
place
> > actual
> > > trades. And that decision must be based on your understanding
> of
> > and
> > > confidence in your system. The only way to gain that
confidence
> is
> > by
> > > observing the transitions from in-sample testing to out-of-
> sample
> > simulated
> > > trading.
> > >
> > > Thanks,
> > > Howard
> > > On Tue, Apr 8, 2008 at 10:37 PM, Mike <sfclimbers@> wrote:
> > >
> > > > Howard's comments are consistent with those of Robert Pardo
> (The
> > > > Evaluation and Optimization of Trading Strategies, Wiley
> 2008),
> > with
> > > > respect to training periods.
> > > >
> > > > Pardo recognizes that there is a tradeoff between more robust
> > > > strategies which require longer in sample training periods,
> > require
> > > > fewer reoptimizations, trade for longer out of sample periods
> and
> > are
> > > > generally less profitable, vs. more responsive strategies
which
> > > > require shorter in sample training periods, require more
> frequent
> > > > reoptimizations, can only trade for shorter out of sample
> periods
> > and
> > > > are generally more profitable.
> > > >
> > > > Pardo suggests that strategies generating more frequent
> signals
> > can
> > > > use shorter in sample training windows since they generate the
> > > > minimum 30+ trades sooner than strategies that generate less
> > frequent
> > > > signals. But, that in any case, one should try to use an in
> sample
> > > > period sufficiently long to capture bull, bear, and sideways
> > markets.
> > > >
> > > > Further, when first trying to evaluate the worth of the
> strategy,
> > > > Pardo suggests backtesting the in sample history in segments
> > rather
> > > > than one shot (e.g. 10 year history divided into five 2 year
> > > > segments). This gives you better insight as to whether the
> results
> > > > are due to a single segment or are consistent accross
> segments,
> > and
> > > > provides insight to your eventual in sample/out of sample
> periods
> > for
> > > > Walk Forward Optimization.
> > > >
> > > > Finally, Pardo suggests that regardless of whether a long or
> short
> > > > training period is used, a rule of thumb for in sample vs.
out
> of
> > > > sample is for out of sample to be between 1/8 to 1/3 of the
in
> > sample
> > > > period (e.g. 24/8 = 3 and 24/3 = 8, so it would be "safe" to
> trade
> > > > out of sample for 3 - 8 months based on a system backtested
> over
> > 24
> > > > months.
> > > >
> > > > Yet another good book covering the topic. I reccomend it.
> > > >
> > > > Mike
> > > >
> > > >
> > > > --- In amibroker@xxxxxxxxxxxxxxx <amibroker%
> > 40yahoogroups.com>, "Howard B"
> > > > <howardbandy@> wrote:
> > > > >
> > > > > Hi Louis, and all --
> > > > >
> > > > > I know David Aronson, respect him, and like and recommend
> his
> > book.
> > > > >
> > > > > My view is that the in-sample period should be as short as
> > > > practical. My
> > > > > thought is that: the system we are testing / trading is
> trying
> > to
> > > > recognize
> > > > > the signal from among the noise; and the signal patterns are
> > > > changing over
> > > > > time. So the length of the in-sample period is a tradeoff --
> > short
> > > > to be
> > > > > able to change as the characteristics of the underlying
> market
> > > > change, but
> > > > > not so short that the system is over-fit to the noise
rather
> > than
> > > > learns the
> > > > > signal.
> > > > >
> > > > > You can test this in AmiBroker. Have your system ready to
> buy
> > and
> > > > sell. In
> > > > > the Automatic Analysis window, use Settings and set up the
> Walk
> > > > Forward
> > > > > parameters. Try an in-sample period of 10 years, an out-of-
> > sample
> > > > period of
> > > > > 6 months or 1 year. Run Optimize > Walk Forward and look at
> the
> > in-
> > > > sample
> > > > > and out-of-sample equity curves. Shorten the length of the
> in-
> > > > sample period
> > > > > to 9, then 8, then 7, ... then 1 year, keeping the out-of-
> sample
> > > > period
> > > > > unchanged. Depending on your system and the market it is
> > trading,
> > > > you may
> > > > > find that there is a sweet spot in the length of the in-
> sample
> > > > data. If so,
> > > > > that is the amount of data that allows your system to
> recognize
> > the
> > > > signal
> > > > > without being overwhelmed by the noise.
> > > > >
> > > > > Thanks,
> > > > > Howard
> > > > >
> > > > >
> > > > > On Tue, Apr 8, 2008 at 8:56 AM, Louis Préfontaine
> <rockprog80@>
> > > >
> > > > > wrote:
> > > > >
> > > > > > Hi,
> > > > > >
> > > > > > I've been thinking a lot lately, and here is something I
> would
> > > > like to
> > > > > > have your opinion on.
> > > > > >
> > > > > > I've been introduced to automated systems by a trend
> following
> > > > book which
> > > > > > related how some trend followers built their systems in
> the
> > 70s
> > > > or 80s and
> > > > > > got rich with them, and how their system did not really
> change
> > > > all this
> > > > > > time. They didn't change their system because they say the
> > > > market does NOT
> > > > > > change. They looked at historic market data from the
1800s
> and
> > > > the market
> > > > > > was as it is right now. So they say.
> > > > > >
> > > > > > On the other side, lately I have been introduced to the
> > concept of
> > > > > > ever-changing markets and have had a hard time trying to
> > build my
> > > > system.
> > > > > > Got a very promising start with a system getting around
15-
> 20%
> > > > average for
> > > > > > April 2007 to April 2008 (with little drawdown, which
mean
> > that
> > > > with
> > > > > > leverage I can boost this a lot). In any variation over
> > > > thousands of stocks
> > > > > > the results were nearly all positives. But then, I tested
> that
> > > > same system
> > > > > > for the years 2000 to 2008, and that was disappointing.
> Even
> > more
> > > > > > disappointing from 2001 to 2003, another troubled market
> like
> > the
> > > > one we are
> > > > > > in right now.
> > > > > >
> > > > > > So here I am, wondering where to go from now. Aronson's
> > > > excellent book
> > > > > > talk about the importance of having a very large sample
of
> > data.
> > > > But the
> > > > > > problem is: the larger the data, the more "historic" it
> gets
> > and
> > > > the less it
> > > > > > seems to work.
> > > > > >
> > > > > > Is my system not working, or did the markets really
> change?
> > Do I
> > > > need to
> > > > > > make it more robust (that is, it MUST make profit even
> from
> > 2001
> > > > to 2003),
> > > > > > or can I have complete faith in what happened in the last
> > year?
> > > > > >
> > > > > > All those questions... Would be nice to read what you
think
> > > > about this.
> > > > > >
> > > > > > Louis
> > > > > >
> > > > > >
> > > > >
> > > >
> > > >
> > > >
> > >
> >
>
------------------------------------
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