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Mike,
in spite of my critical remark I'm actually using zig, too, to calculate ME.
Something simple like
amount = Param("ZigZag-Amount", 1, 0.1, 1.5, 0.1 );
zz0 = Zig( c, amount );
up=zz0>Ref(zz0,-1);
down=zz0<Ref(zz0,-1);
Longprofit=IIf(up,zz0-Ref(zz0,-1),0);
Longprofit=Cum(Longprofit);
Shortprofit=IIf(down,Ref(zz0,-1)-zz0,0);
Shortprofit=Cum(Shortprofit);
if( ParamToggle("Long AND Short Trades?", "No|Yes", 0 ) )
PP=Longprofit + Shortprofit;
else PP=Longprofit;
It's not a "perfect" calculation of Perfect Profit but - as you've put it -
still a reasonable measure.
Regards
Thomas
On 26.10.2009, 22:10:11 Mike wrote:
> A valid point.
>
> You would still be compounding based on the Zig pivot points. And,
> depending on what value you used for Zig, the difference might be small.
> But, true, over the long run there would likely be a difference due to
> compounding.
>
> Though, given that the calculation is meant solely as a benchmark, it's
> probably still a reasonable measure (and perhaps a less discouraging one)
> to compare against.
>
> It is unrealistic to think that we could possibly capture every single
> pivot. But, getting pretty close to a larger Zig value... maybe, just
> maybe ;)
>
> Mike
>
> --- In amibroker@xxxxxxxxxxxxxxx, Thomas Ludwig <Thomas.Ludwig@xxx> wrote:
> > On 20.10.2009, 08:47:38 Mike wrote:
> > > In "The Evaluation and Optimization of Trading Strategies", Robert
> > > Pardo describes Perfect Profit as "buying at every valley and selling
> > > at every peak".
> > >
> > > He suggests that Correlation between Equity Curve and Perfect Profit
> > > (CECPP) "is an excellent sole evaluation measure" (i.e. fitness
> > > function).
> > >
> > > Independant of fitness function, he further suggests that model
> > > efficiency can be measured by expressing Net Profit as a ratio of
> > > Perfect Profit; (Net Profit / Perfect Profit) * 100 with a value of 5
> > > or greater being very good.
> > >
> > > What you describe in your note sounds very much like his model
> > > efficiency measure.
> > >
> > > If you simplify the idea a bit, using minimum price movements, you end
> > > up with Zig instead of Perfect Profit.
> >
> > Yes, but that doesn't include compounding. So that seems to be a little
> > bit too much simplification ;-)
> >
> > Greetings,
> >
> > Thomas
> >
> > > Mike
> > >
> > > --- In amibroker@xxxxxxxxxxxxxxx, "Steve Dugas" <sjdugas@> wrote:
> > > > Hi - For years I have been calculating stats for the reg line
> > > > alongside stats for the equity curve. But lately I have been
> > > > wondering about the value of the regression line because, in real
> > > > life there are volatile periods where the market has more to give,
> > > > and less volatile periods where there just isn't as much available.
> > > > Would it be better to shoot for capturing a consistent % of what is
> > > > available rather than just a consistent %? I was thinking about how
> > > > to account for this, the best I have come up with is to replace the
> > > > reg line with a "perfect system eq line", i.e. one that looks ahead
> > > > to the next day and pre-positions itself so that it wins every day.
> > > > Then scale it down to see how the system eq line fits against the
> > > > "perfect shape". Anyone have any thoughts whether this might be a
> > > > better approach? Thanks!
> > > >
> > > > Steve
> > > >
> > > >
> > > > ----- Original Message -----
> > > > From: "aarbee60" <rajbakshi@>
> > > > To: <amibroker@xxxxxxxxxxxxxxx>
> > > > Sent: Monday, October 19, 2009 8:02 PM
> > > > Subject: [amibroker] Re: Is the Walk forward study useful?
> > > >
> > > > > In terms of getting significantly different results by shifting IS
> > > > > and OOS windows, could it be the result of using ObjFun like
> > > > > CAR/MDD where the result would be different if the CAR and/or MDD
> > > > > changes significantly at the beginning or end of the IS window. In
> > > > > this case, as the IS or OOS window is shifted the ObjFun changes
> > > > > dramatically giving rise to the behaviour noted in the earlier
> > > > > posts of this thread.
> > > > >
> > > > > Curtis Faith in his "Way of the Turtle" refers to this behaviour in
> > > > > the context of designing more robust metrics. He has devised a
> > > > > couple of metrics that make a lot of sense. RAR% (Regressed Annual
> > > > > Return percentage) instead of CAR uses the beginning and ending
> > > > > values of linear regression line instead of those of the equity
> > > > > curve.
> > > > >
> > > > > For example if at the beginning of the IS or OOS window, the equity
> > > > > curve had a downturn and a significant upturn before the end of
> > > > > that window the CAR would be significantly higher. In the same case
> > > > > if the Linear Regression line was used, the CAR value would be
> > > > > less. In general, the RAR% value is less sensitive to equity
> > > > > changes at the beginning and end of the test. This is why Curtis
> > > > > Faith refers to it as a more Robust measure of performance.
> > > > >
> > > > > Anyone has any views on above. Would be very interested to hear.
> > > > >
> > > > > Cheers
> > > > > Raj Bakshi
> > > > >
> > > > > --- In amibroker@xxxxxxxxxxxxxxx, "Mike" <sfclimbers@> wrote:
> > > > >> Hi Ton,
> > > > >>
> > > > >> I agree that the rule of thumb is subjective. So far, I've been
> > > > >> willing to live with it.
> > > > >>
> > > > >> It appears that you and I have different expectations of IS/OOS
> > > > >> window sizes. I treat the calculation of walk forward window sizes
> > > > >> as a second pass optimization, similar to a simple moving average
> > > > >> (SMA) crossover system.
> > > > >>
> > > > >> - There are two variables (e.g. IS length/OOS length vs. fast
> > > > >> SMA/slow SMA)
> > > > >> - An optimal combination is desired
> > > > >> - We use a fitness function to measure optimal (e.g. OOS:IS ratio
> > > > >> vs. CAR/MDD)
> > > > >>
> > > > >> This is how I try to satisfy your Aronson quote "Each strategy
> > > > >> will have its own best values for IS/OOS periods".
> > > > >>
> > > > >> Upon finding an optimal CAR/MDD using fast SMA/slow SMA, we should
> > > > >> theoretically be able to trade that same optimal combination of
> > > > >> fast SMA/slow SMA over different time periods and expect to get a
> > > > >> somewhat stable CAR/MDD (subject to changing market conditions).
> > > > >>
> > > > >> I would not expect combinations of fast SMA/slow SMA to be stable
> > > > >> relative to each other. Looking at a 3-D graph for this crossover
> > > > >> system will reveal peaks and valleys. Taking a single slice of
> > > > >> that graph (i.e. holding slow SMA constant and varying only fast
> > > > >> SMA) will reveal a rising and falling wave.
> > > > >>
> > > > >> So, I would expect exactly the same in the IS/OOS experiment you
> > > > >> describe. You are simply taking a slice of the 2 variable
> > > > >> optimization graph (holding IS constant and varying OOS). I would
> > > > >> expect a rising and falling wave representing the peaks and
> > > > >> valleys that would appear on the full 3-D graph.
> > > > >>
> > > > >> If I optimize the ratio of OOS:IS using IS length/OOS length, then
> > > > >> I expect to get a somewhat consistent OOS:IS ratio (subject to
> > > > >> market changes) when using that same optimal IS length/OOS length
> > > > >> over different data ranges. I don't expect to get a stable OOS:IS
> > > > >> ratio using a fixed IS length and variable OOS length.
> > > > >>
> > > > >> Mike
> > > > >>
> > > > >> --- In amibroker@xxxxxxxxxxxxxxx, "Ton Sieverding"
> > > > >> <ton.sieverding@>
> > > > >>
> > > > >> wrote:
> > > > >> > Thanks for your patience Mike -)
> > > > >> >
> > > > >> > 1. I know Pardo disagrees with Aronson. And yes I am also using
> > > > >> > Pardo's rule of thumb. But a rule of thumb without a scientific
> > > > >> > explanation is still a rule of thumb and therefore subjective.
> > > > >> > The result of this is when taking 1/8 in stead of 1/3, I am
> > > > >> > getting a completely different results. That's what Aronson
> > > > >> > tells me. So I do not understand why Pardo disagrees with
> > > > >> > Aronson ... Of course I should ask him. And I will ...
> > > > >> >
> > > > >> > 2. Here you are telling me what Aronson says : "Each strategy
> > > > >> > will have its own best values for IS/OOS periods". But trying to
> > > > >> > find the best values is empirical and therefore without having a
> > > > >> > 'good theory' why your are getting these values is highly
> > > > >> > subjective. Pardo is not giving me this good theory and Aronson
> > > > >> > tells me this good theory does not exist ...
> > > > >> >
> > > > >> > 3. With regard to our topic, it's not so important which
> > > > >> > objective function you are using for the WalkFoward. In general
> > > > >> > I use the CAR/MDD. But whatever OF gives you the same random
> > > > >> > WalkForward results. Where of course by definition you should
> > > > >> > use a return/risk related OF ...
> > > > >> >
> > > > >> > 4. The way I am analyzing the WalkForward result is simple. I am
> > > > >> > calculating the differences between the IS and OOS results in
> > > > >> > percentages from OOS. Then I am taking the average and standard
> > > > >> > deviation of all these percentages. This gives me an idea about
> > > > >> > the average IS/OOS error as well as the spread around this
> > > > >> > average. For the same AFL using the same Symbol you should do
> > > > >> > the WalkFoward in the way I mentioned in my previous email and
> > > > >> > calculate the above average/stdev relation. In order to get a
> > > > >> > stable WalkForward result being independent of the IS/OOS ratio,
> > > > >> > the average/stdev relation should be more or less stable. It's
> > > > >> > not. It's highly dependent on the IS/OOS ratio you are using ...
> > > > >> >
> > > > >> > BTW ... To get things straight, I am not throwing WalkFoward out
> > > > >> > of the window. I am just trying to believe in what I am using.
> > > > >> > And it's getting more and more difficult for me ...
> > > > >> >
> > > > >> > Regards, Ton.
> > > > >> >
> > > > >> >
> > > > >> >
> > > > >> >
> > > > >> > ----- Original Message -----
> > > > >> > From: Mike
> > > > >> > To: amibroker@xxxxxxxxxxxxxxx
> > > > >> > Sent: Monday, October 05, 2009 11:09 AM
> > > > >> > Subject: [amibroker] Re: Is the Walk forward study useful?
> > > > >> >
> > > > >> >
> > > > >> > Ton,
> > > > >> >
> > > > >> > 1. Pardo disagrees with Aronson (and Bandy). Pardo suggests
> > > > >> > that a OOS to IS ration of 25% - 35% is best, but that a good
> > > > >> > rule of thumb for empirical testing is 1/8 to 1/3.
> > > > >> >
> > > > >> > 2. Yes, I suspect that each strategy will have its own best
> > > > >> > values for IS/OOS and that other values will appear as useless.
> > > > >> > It is up to us to try and find the best values.
> > > > >> >
> > > > >> > With respect to your comment: "I am getting results that show
> > > > >> > a random pattern", my question remains; What are you measuring?
> > > > >> > In other words, what values appear random - your fitness value?
> > > > >> > CAR? Something else?
> > > > >> >
> > > > >> > 3. I have done very much as you ask, except that I also varied
> > > > >> > my IS period. I mostly kept my ratios within Pardo's suggested
> > > > >> > 1/8 to 1/3, but went as low as 1/12 and as high as 1/2 just to
> > > > >> > be sure.
> > > > >> >
> > > > >> > For example IS=1 year, IS=2 years, IS=3 years giving
> > > > >> >
> > > > >> > IS1yr+OOS6mth, IS1yr+OOS3mth, IS1yr+OOS1mth
> > > > >> > IS2yr+OOS12mth, IS2yr+OOS6mth, IS2yr+OOS3mth
> > > > >> > IS3yr+OOS18mth, IS3yr+OOS12mth, IS3yr+OOS6mth
> > > > >> >
> > > > >> > IS2yr+OOS6mth produced the most consistent CAR, even though a
> > > > >> > weighted UPI was used as the fitness function for the actual
> > > > >> > walk forward.
> > > > >> >
> > > > >> > I do not have a strong opinion as to whether or not there
> > > > >> > really is a relationship between IS and OOS sizes. I found that
> > > > >> > Pardo's rule of thumb was as good a starting place as any. I was
> > > > >> > happy that my values (25%) coincided with what he advised. But,
> > > > >> > had my studies suggested a ratio outside of Pardo's range, I
> > > > >> > would have still gone with what my results suggested, despite
> > > > >> > Pardo's advice.
> > > > >> >
> > > > >> > Mike
> > > > >> >
> > > > >> > --- In amibroker@xxxxxxxxxxxxxxx, "Ton Sieverding"
> > > > >> > <ton.sieverding@>
> > > > >> >
> > > > >> > wrote:
> > > > >> > > Hi Mike,
> > > > >> > >
> > > > >> > > What I am saying is :
> > > > >> > >
> > > > >> > > 1. That according to David Aronson "There is no theory that
> > > > >> >
> > > > >> > suggests what fraction of the data should be assigned to
> > > > >> > training ( IS ) and testing ( OOS )." and that "Results can be
> > > > >> > very sensitive to these choices ... ". I assume that he knows
> > > > >> > where he is talking about ...
> > > > >> >
> > > > >> > > 2. That when I am doing WalkFoward tests following the
> > > > >> > > advice of
> > > > >> >
> > > > >> > Howard Bandy, Robert Pardo AND Van Tharp, I am getting results
> > > > >> > that show a random patron when changing the OOS en IS periods.
> > > > >> > So my conclusion is that WalkFoward is a subjective test ...
> > > > >> >
> > > > >> > > Therefore I have serious problems using WalkFoward tests. If
> > > > >> > > you
> > > > >> >
> > > > >> > can help me to get things done in an objective way then I will
> > > > >> > be delighted to know how you want to do that. But for sure Van
> > > > >> > Tharp did not help me ...
> > > > >> >
> > > > >> > > Please do a simple WF test with OOS=1year and
> > > > >> > > IS=1month...12months.
> > > > >> >
> > > > >> > So creating WF results for OOS1y+IS1m, OOS1y+IS2m etc. And see
> > > > >> > what you are getting. This is purely random. The result says
> > > > >> > nothing to me ...
> > > > >> >
> > > > >> > > Regards, Ton.
> > > > >> > >
> > > > >> > >
> > > > >> > >
> > > > >> > > ----- Original Message -----
> > > > >> > > From: Mike
> > > > >> > > To: amibroker@xxxxxxxxxxxxxxx
> > > > >> > > Sent: Monday, October 05, 2009 9:29 AM
> > > > >> > > Subject: [amibroker] Re: Is the Walk forward study useful?
> > > > >> > >
> > > > >> > >
> > > > >> > > 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@> 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@>
> > > > >> > > > 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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