Hi, I just checked, it is there: go to files; sort them by date of upload and you find: JBookTrader- my.zip close to the top. This is the recent one.
alternatively you can find patch.txt this is an older version. Klaus PS alternatively you can sort by name of uploader and then you see it if you look for Klaus On 11 Dez., 03:21, Alexana <[email protected]> wrote: > Klaus, I tried to look at the file you had posted, but seems the page > is not there. Can you repost? > > On Dec 10, 9:22 am, Klaus <[email protected]> wrote: > > > > > > > > > it is online in the group file repository. > > > Klaus > > > On 10 Dez., 14:37, Astor <[email protected]> wrote:> Thanks Klaus! I > > look forward to seeing it. > > > > ________________________________ > > > From: Klaus <[email protected]> > > > To: JBookTrader <[email protected]> > > > Sent: Fri, December 10, 2010 3:55:37 AM > > > Subject: [JBookTrader] Re: Dynamic Parameter Optimization > > > > Dear Astor, > > > > I posted it earlier for a previous JBT version. > > > I will upload a version for the current JBT in a few moments, This is > > > an excerpt of my production code, but note that I modified it somewhat > > > to > > > ignore certain error conditions (missing volume values and wrongly > > > ordered dates). > > > > Cheers > > > Klaus > > > > On 8 Dez., 21:53, Astor <[email protected]> wrote: > > > > > Thanks Klaus. Exellent description. Can you post your JBT extension for > > > > backtests? I would love to incorporate that into my program. > > > > > ________________________________ > > > > From: Klaus <[email protected]> > > > > To: JBookTrader <[email protected]> > > > > Sent: Wed, December 8, 2010 2:48:13 PM > > > > Subject: [JBookTrader] Re: Dynamic Parameter Optimization > > > > > use of-out-of sample data is a must in all machine learning approaches > > > > (and this is actually what we do here). > > > > (So, yes i also take the approach, this is actually the reason why I > > > > built JBT extension for batches of backtests.) > > > > > Perhaps it can be better understood if looks at the danger of > > > > presenting all the data. > > > > What can happen is that the strategy (and actually JBT does not > > > > support learning of parameters, but only of parameter settings) > > > > that is generated is sort of memorizing the presented data.. and then > > > > provides good results there, but not beyond. > > > > The result does not generalize to further data. > > > > > It is like with people, if you really want someone to understand > > > > something, you will teach him (presenting examples > > > > is one approach for teaching). But at the end you want to know whether > > > > he really understood (i.e., got the principles > > > > and is able to use them to solve knew problems) or whether he just > > > > memorized. The only way to find this out is to show him s.th. he has > > > > not seen before... > > > > That is what use of out-of-sample means. Simulated forward-Trading is > > > > another way to achieve the same result, but then you are doing it in > > > > real time (i.e., need weeks), but if you have more data, you can do > > > > this simply in minutes with testing.. > > > > > On 8 Dez., 15:15, Astor <[email protected]> wrote: > > > > > > >people do a lot of silly things. A lot of them agree on these silly > > > things > > > > > > > > > > True enough. This thing, though, has been the subject of so much > > > > > academic > > > > > research that it is probably not one of them. Of course, just like > > > > > you, everybody wants to use as much data as possible for model > > > > > optimization, > > > > so > > > > > a technique called "bootstrapping" is used, which is similar to > > > > > walk-forward > > > > > optimization that I was proposing. > > > > > > ________________________________ > > > > > From: ShaggsTheStud <[email protected]> > > > > > To: [email protected] > > > > > Sent: Tue, December 7, 2010 11:13:26 PM > > > > > Subject: Re: [JBookTrader] Re: Dynamic Parameter Optimization > > > > > > I dunno, people do a lot of silly things. A lot of them agree on > > > > > these > > > silly > > > > > things. > > > > > > If you had extra data, why would you not use it to see the > > > > > sensitivity of > > > >your > > > > > parameters? > > > > > > On Tue, Dec 7, 2010 at 8:34 PM, Astor <[email protected]> wrote: > > > > > > Shaggs, I wish I could claim credit for this approach but it is not my > > > > >approach. > > > > > It is a standard statistical methodology used by every professional > > > > > Quant > > > > shop, > > > > > without exceptions. In institutional settings, you could never get any > > > > strategy > > > > > past the Investment Committee without presenting strong out-of-sample > > > >results. > > > > > > >This is not to say that sensitivity to parameter changes, robustness > > > checks, > > > > > >etc need not be done. They still need to be done on in-sample data. > > > > > > ________________________________ > > > > > From: ShaggsTheStud <[email protected]> > > > > > > >To: [email protected] > > > > > >Sent: Tue, December 7, 2010 8:53:49 PM > > > > > > >Subject: Re: [JBookTrader] Re: Dynamic Parameter Optimization > > > > > > >So the difference between our approaches is that in your approach > > > > > >you look > > > >at > > > > > >the 1 "optimal" case, and then you try it on some other set of data > > > > > >to > > > >verify > > > > > >it. Ok, that is interesting. > > > > > > >I much prefer to have one set of data, and look at the optimization > > > > > >map, > > > and > > > > > >view the sensitivities to changes in the parameters. A robust > > > > > >strategy > > > will > > > > >not > > > > > >be a picking out small "local minimums", it will have a wide plateau > > > > > >of > > > > > >profitibility, and have good distribution on the trades graph where > > > > > >there > > > >are > > > > > >not large periods of drawdown. > > > > > > >I think my method is more robust, and would yield better real world > > > > >performance > > > > > >than your method, but I can't prove it. > > > > > > >On Tue, Dec 7, 2010 at 5:52 PM, Astor <[email protected]> wrote: > > > > > > >>No, you do not do the same thing on both sets. You optimize and test > > > > >different > > > > > >>models on in-sample set only. You can do it as much as is necessary > > > > > >>to get > > > > >good > > > > > >>results. You test only the final model on your out-of-sample and > > > > > >>you can > > > >not > > > > > >>change the model or re-optimize parameters and re-test on > > > > > >>out-of-sample. Out-of-sample is like virginity, - once used it is > > > > > >>gone. > > > > > > >>Results from out-of-sample is what you expect to get in real > > > > > >>trading. > > > > > > ________________________________ > > > > > From: ShaggsTheStud <[email protected]> > > > > > > >>To: [email protected] > > > > > >>Sent: Tue, December 7, 2010 7:01:03 PM > > > > > > >>Subject: Re: [JBookTrader] Re: Dynamic Parameter Optimization > > > > > > >>Doing the same thing on two different sets of data seems identical > > > > > >>to > > > doing > > > > >it > > > > > >>on one combined set of data. How is it different? > > > > > > >>On Tue, Dec 7, 2010 at 4:14 AM, Astor <[email protected]> wrote: > > > > > > >>The "in-sample" set is where you develop your model and optimize > > > > > >>your > > > > > >>parameters. Because optimization searches through a very large > > > > > >>number of > > > > > >>possible parameter values, it finds those values which best fit the > > > > > >>datain > > > > >this > > > > > >>set. In a different data set, such as the one that may occur in real > > > > trading, > > > > > >>these parameters may prove perfectly useless. In Quant research, > > > > > >>such > > > > >situation > > > > > >>is (derogatively) referred to as "datamining" or overfitting. With > > > > > >>enough > > > > >model > > > > > >>parameters and extensive optimization, I can get perfect accuracy > > > > > >>predicting "in-sample" lottery winners. Of course that model will > > > > > >>not work > > > > to > > > > > >>predict next, "out-of-sample", lottery winner. > > > > > > >>>The "out-of-sample" set is a way to verify that the found model > > > > > >>>and its > > > > > >>>parameters are general instead of unique to the "in-sample" > > > > > >>>development > > > > set. > > > > > >>>Combining the two sets into a single set defeats that purpose. > > > > > > ________________________________ > > > > > From: ShaggsTheStud <[email protected]> > > > > > > >>>To: [email protected] > > > > > >>>Sent: Mon, December 6, 2010 10:21:59 PM > > > > > >>>Subject: Re: [JBookTrader] Re: Dynamic Parameter Optimization > > > > > > >>>That whole "in sample" and "out of sample" data thing strikes me > > > > > >>>very as > > > > >very > > > > > >>>odd. If it works on the in-sample and not the out-sample, its > > > > > >>>going to > > > >have > > > > >a > > > > > >>>bad distribution as a single set, so why not just combine it? > > > > > > >>>On Sun, Dec 5, 2010 at 5:56 AM, Astor <[email protected]> wrote: > > > > > > >>>> we would > > > > > >>>>>be required to significantly shorten our optimization periods, > > > > > >>>>>thus > > > > > >>>>>incurring a penalty of standard error in our confidence bands. > > > > > > >>>>I understand your concern Eugene. However, it is important to > > > > > >>>>recognize > > > > >that > > > > > >>>>in strategy development and validation there are two sets of data > > > > > >>>>and > > > two > > > > >sets > > > > > >>>>of confidence bands. First set is used for strategy development > > > > > >>>>and > > > > >parameter > > > > > >>>>optimization and is often called "in-sample". The second set is > > > > > >>>>used > > > only > > > > >to > > > > > >>>>validate the strategy performance and is called "out-of-sample". > > > > > > >>>>If the confidence interval is very broad (standard error is > > > > > >>>>large) in > > > the > > > > > >>>>"in-sample" data, your strategy is not reliable and should not be > > > > > >>>>used. > > > > > > >>>>If the "in-sample" results are good and have acceptable confidence > > > > >intervals, > > > > > >>>>the next step is validation of the strategy on "out-of-sample" > > > > > >>>>data. > > > > > >>>>Because "out-of-sample" data has not been used for parameter > > > >optimization, > > > > >the > > > > > >>>>results obtained on this data are far more important than those > > > > > >>>>from > > > > > >>>>"in-sample". If the "out-of-sample confidence interval is too > > > > > >>>>broad, the > > > > > >>>>validation results are not reliable and the strategy should not > > > > > >>>>be used. > > > > > > >>>>It is extremely common that the available data set is too small to > > > > >partition the > > > > > >>>>data into in- and out- of sample sets of adequate > > ... > > Erfahren Sie mehr » -- You received this message because you are subscribed to the Google Groups "JBookTrader" group. To post to this group, send email to [email protected]. To unsubscribe from this group, send email to [email protected]. For more options, visit this group at http://groups.google.com/group/jbooktrader?hl=en.
