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Mike,
how do calculate SQN? I've tried this:
SQN=sqrt(st.GetValue("AllQty"))*st.GetValue("AllAvgProfitLoss")/StDev(st.GetValue("NetProfit"));
bo.AddCustomMetric("SQN",SQN);
but I'm not sure if this is correct.
What do you think?
Regards,
Thomas
On 05.10.2009, 09:29:41 Mike wrote:
> Ton,
>
> Are you saying that you have not found an IS/OOS pair that works well? What
> measure are you using to judge "stability" of the walk forward process
> (i.e. what measure are you using to judge the process as random)?
>
> After testing with multiple IS periods, and with multiple OOS periods, I
> was able to identify "fixed" window lengths that proved more consistent
> than the others tested.
>
> I reached this conclusion by charting a distribution curve of CAR for the
> OOS results. My fitness function is currently based on UPI, and thus my
> walk forward is driven by that value. However, ultimately my interest is
> in how consistent CAR would be which is why I used that for evaluating the
> goodness of fit for the IS/OOS period lengths.
>
> In my case, over a 13 year period, a 2 year IS and 6 month OOS (for a total
> of 26 OOS data points) produced the most normal looking distribution of
> CAR results (i.e. central peak, smallest standard deviation). Excluding
> the results from all of 1999 and the first half of 2000 (during which
> results were abnormally strong), the distribution curve looks even better.
>
> Also, have you tried working with different fitness functions? Perhaps your
> fitness function doesn't adequately identify the "signal" and thus
> misguides the walk forward, regardless of IS/OOS window lengths.
>
> I am in the process of running a new walk forward over the last 7.5 years
> using Van Tharp's System Quality Number (SQN) as my fitness function. I
> have kept the same 2 year IS/6 months OOS for a total of 15 OOS data
> points. My system strives to generate a minimum average of 2 trades per
> day, so each IS period generally has 1000 or more trades from which to
> calculate the fitness.
>
> It has not run to completion yet. But, for the periods that have produced
> results, the results look promising (at least with respect to the SQN of
> the OOS relative to the SQN of the IS, I have not yet created the
> distribution of CAR for OOS).
>
> Assuming that the remainder of the results are equally strong, I will walk
> forward further back in history to get the full 26 data points to compare
> against the results produced using my UPI fitness. If the CAR distribution
> is more normal using SQN as fitness, then I will officially start using
> SQN for generating optimal values for my next live OOS.
>
> If you are willing to share, I would be curious to hear if SQN as a fitness
> function was able to produce a more stable walk forward for you, and what
> measure you are using to judge "stable".
>
> Mike
>
> --- In amibroker@xxxxxxxxxxxxxxx, "Ton Sieverding" <ton.sieverding@xxx>
wrote:
> > Hi Howard,
> >
> > I still am struggling with the following sentence from David Aronson
> > : "The decision about how to apportion the data between the IS and OOS
> > subsets is arbitrary. There is no theory that suggests what fraction of
> > the data should be assigned to training ( IS ) and testing ( OOS ).
> > Results can be very sensitive to these choices ... ". Because this is
> > exactly what I am seeing. WalkFoward results are more then sensitive to
> > the IS/OOS relation and in many cases a pure random story. I am getting
> > more and more the feeling that WalkForward is not the correct or better
> > objective way to test trading systems. With all respect to Robert Pardo's
> > idea's about this topic and what you are writing in QTS ...
> >
> > Regards, Ton.
> >
> >
> > ----- Original Message -----
> > From: Howard B
> > To: amibroker@xxxxxxxxxxxxxxx
> > Sent: Monday, October 05, 2009 12:48 AM
> > Subject: Re: [amibroker] Re: Is the Walk forward study useful?
> >
> >
> > Greetings all --
> >
> > My point of view on the length of the in-sample and out-of-sample may
> > be a little different.
> >
> > The logic of the code has been designed to recognize some pattern or
> > characteristic of the data. The length of the in-sample period is
> > however long it takes to keep the model (the logic) in synchronization
> > with the data. There is no one answer to what that length is. When the
> > pattern changes, the model fits it less well. When the pattern changes
> > significantly, the model must be re-synchronized. The only person who
> > can say whether the length is correct or should be longer or shorter is
> > the person running the tests.
> >
> > The length of the out-of-sample period is however long the model and
> > the data remain in sync. That must be some length of time beyond the
> > in-sample period in order to make profitable trades. It could be a long
> > time, in which case there is no need to modify the model at all during
> > that period. There is no general relationship between the length of the
> > in-sample period and the length of the out-of-sample period -- none.
> > There is no general relationship between the performance in-sample and
> > the performance out-of-sample. The greater the difference between the
> > two, the better the system has been fit to the data over the in-sample
> > period. But that does not necessarily mean that the out-of-sample
> > results are less meaningful.
> >
> > You can perform some experiments to see what the best in-sample length
> > is. And then to see what the typical out-of-sample length is. Knowing
> > these two, set up a walk forward run using those lengths. After the run
> > is over, ignore the in-sample results. They have no value in estimating
> > the future performance of the system. It is the out-of-sample results
> > that can give you some idea of how the system might act when traded with
> > real money.
> >
> > It is nice to have a lot of closed traded in the out-of-sample period,
> > but you can run statistics on as few as 5 or 6. Having fewer trades
> > means that it will be more difficult to achieve statistical significance.
> > The number 30 is not magic -- it is just conventional.
> >
> > I think it helps to distinguish between the in-sample and out-of-sample
> > periods this way -- in-sample is seeing how well the model can be made to
> > fit the older data, out-of-sample is seeing how well it might fit future
> > data.
> >
> > Ignore the television ads where person after person exclaims
> > "backtesting!" as though that is the key to system development. It is
> > not. Backtesting by itself, without going on to walk forward testing,
> > will give the trading system developer the impression that the system is
> > good. In-sample results are always good. We do not stop fooling with
> > the system until they are good. But in-sample results have no value in
> > predicting future performance -- none.
> >
> > There are some general characteristics of trading systems that make
> > them easier to validate. Those begin with having a positive expectancy
> > -- no system can be profitable in the long term unless it has a positive
> > expectancy. Then going on to include trade frequently, hold a short
> > time, minimize losses. Of course, there have been profitable systems
> > that trade infrequently, hold a long time, and suffer deep drawdowns. It
> > is much harder to show that those were profitable because they were good
> > rather than lucky.
> >
> > There is more information about in-sample, out-of-sample, walk forward
> > testing, statistical validation, objective functions, and so forth in my
> > book, "Quantitative Trading Systems."
> > http://www.quantitativetradingsystems.com/
> >
> > Thanks for listening,
> > Howard
> >
> >
> >
> > On Sun, Oct 4, 2009 at 10:56 AM, Bisto <bistoman73@xxx> wrote:
> >
> >
> > Yes, I believe that you should increase the IS period
> >
> > as general rule is not true "the shortest the best" trying to catch
> > every market change because it's possible that a too short IS period
> > produces a too low number of trades with no statistical robustness -->
> > you will find parameters that are more likely candidated to fail in OS
> >
> > try a longer IS period and let's see what will happen
> >
> > I read an interesting book on this issue: "The evaluation and
> > optimization of trading strategies" by Pardo. Maybe he repeated too much
> > times the same concepts nevertheless I liked it
> >
> > if anyone could suggest a better book about this issue it would be
> > very appreciated
> >
> >
> >
> > Bisto
> >
> > --- In amibroker@xxxxxxxxxxxxxxx, "Gonzaga" <gonzagags@> wrote:
> > > Oh, sorry, I am lost in translation ... ;-)
> > > Yes I meant trades of my IS period.
> > > I've got about 70 trades in my IS period, three months.
> > > BUT, I buy stocks in a multiposition way.This means, that my hole
> > > capital divides among several stocks purchased simultaneously. So,
> > > in my statistics, I use to average my trades. When I use
> > > maxopenpositions=7, I use to average my results every 7 trades.
> > > Considering that, my trades in three months are not 70, but less (
> > > not exactly 70/7, but less than 70)
> > >
> > > If I use maxopenposition=1, which is, invest all my capital every
> > > trade, in three months I would have about 29 trades. So I suppose I
> > > have to increase the IS period.. isn`t it?
> > >
> > > --- In amibroker@xxxxxxxxxxxxxxx, "Bisto" <bistoman73@> wrote:
> > > > What do you mean with "I don't have many buyings and sellings"?
> > > >
> > > > If you have less than 30 trades in an IS period, IMHO, you are
> > > > using a too short period due to not statistical robustness -->
> > > > WFA is misleading, try a longer IS period
> > > >
> > > > Bisto
> > > >
> > > > --- In amibroker@xxxxxxxxxxxxxxx, "Gonzaga" <gonzagags@> wrote:
> > > > > Thanks for the answers
> > > > > To Keith McCombs :
> > > > >
> > > > > I use 3 months IS test and 1 month step, this is, 1 month OS
> > > > > test. My system is an end-of day-system, so I don't have many
> > > > > buyings and sellings.. Perhaps I should make bigger the IS
> > > > > period?
> > > > >
> > > > > anyway, my parameter behaves well in any period. Of course it
> > > > > is an optimized variable, but it doesn't fail in ten years, in
> > > > > none of those ten years, over 500 stocks.. a very long period..
> > > > > So, couldn't it be better, on the long run, than the parameters
> > > > > optimized with the WF study? (In fact, I am using it now, the
> > > > > optimized variable)
> > > > > That's my real question..
> > > > >
> > > > > To dloyer123:
> > > > > I haven't understood the meaning of the Walk Forward Efficency,
> > > > > and seems interesting. can you explain it better, please..?
> > > > >
> > > > > --- In amibroker@xxxxxxxxxxxxxxx, "dloyer123" <dloyer123@>
wrote:
> > > > > > I have had similar experiences. I like to use WFT to estimate
> > > > > > what Pardo call's his "Walk Forward Efficency", or the ratio
> > > > > > of the out of sample WF profits to just optimizing over the
> > > > > > entire time period.
> > > > > >
> > > > > > A good system should have as high a WFE as posible. Systems
> > > > > > with a poor WFE tend to do poorly in live trading.
> > > > > >
> > > > > > If you have a parm set that works well over a long period of
> > > > > > live trading, then you are doing well!
>
> ------------------------------------
>
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**** IMPORTANT PLEASE READ ****
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