|
PureBytes Links
Trading Reference Links
|
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@xxx> 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@xxx>
> > 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!
> > >
> > > ------------------------------------
> > >
> > > **** IMPORTANT PLEASE READ ****
> > > This group is for the discussion between users only.
> > > This is *NOT* technical support channel.
> > >
> > > TO GET TECHNICAL SUPPORT send an e-mail directly to
> > > SUPPORT {at} amibroker.com
> > >
> > > TO SUBMIT SUGGESTIONS please use FEEDBACK CENTER at
> > > http://www.amibroker.com/feedback/
> > > (submissions sent via other channels won't be considered)
> > >
> > > For NEW RELEASE ANNOUNCEMENTS and other news always check DEVLOG:
> > > http://www.amibroker.com/devlog/
> > >
> > > Yahoo! Groups Links
>
> ------------------------------------
>
> **** IMPORTANT PLEASE READ ****
> This group is for the discussion between users only.
> This is *NOT* technical support channel.
>
> TO GET TECHNICAL SUPPORT send an e-mail directly to
> SUPPORT {at} amibroker.com
>
> TO SUBMIT SUGGESTIONS please use FEEDBACK CENTER at
> http://www.amibroker.com/feedback/
> (submissions sent via other channels won't be considered)
>
> For NEW RELEASE ANNOUNCEMENTS and other news always check DEVLOG:
> http://www.amibroker.com/devlog/
>
> Yahoo! Groups Links
>
>
>
------------------------------------
**** IMPORTANT PLEASE READ ****
This group is for the discussion between users only.
This is *NOT* technical support channel.
TO GET TECHNICAL SUPPORT send an e-mail directly to
SUPPORT {at} amibroker.com
TO SUBMIT SUGGESTIONS please use FEEDBACK CENTER at
http://www.amibroker.com/feedback/
(submissions sent via other channels won't be considered)
For NEW RELEASE ANNOUNCEMENTS and other news always check DEVLOG:
http://www.amibroker.com/devlog/
Yahoo! Groups Links
<*> To visit your group on the web, go to:
http://groups.yahoo.com/group/amibroker/
<*> Your email settings:
Individual Email | Traditional
<*> To change settings online go to:
http://groups.yahoo.com/group/amibroker/join
(Yahoo! ID required)
<*> To change settings via email:
mailto:amibroker-digest@xxxxxxxxxxxxxxx
mailto:amibroker-fullfeatured@xxxxxxxxxxxxxxx
<*> To unsubscribe from this group, send an email to:
amibroker-unsubscribe@xxxxxxxxxxxxxxx
<*> Your use of Yahoo! Groups is subject to:
http://docs.yahoo.com/info/terms/
|