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 data >>> * in 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 size. >>>> In financial research, the data set size is usually limited not by the data >>>> availability but by the data stationarity. To create valid sample sizes >>>> from >>>> small data, a technique called "leave-one-out" or "bootstrapping" or >>>> "jackknifing" is used. In those techniques the model is developed on the >>>> entire data except for one "holdout" point, then tested on this point. Then >>>> a different point is selected and the process is repeated. The validation >>>> results are obtained by combining the results of holdout points. >>>> Walk-forward optimization is an example of this technique and actually >>>> reduces standard error in the more important "out-of-sample" test. >>>> >>>> >better model would be the one which not only >>>> >accounts for the supply/demand, but also for its changing elasticity >>>> >over time >>>> >>>> That is definitely so and is often driven by seasonality as well >>>> as regime shifts. For futures, such as ES, the elasticity could drift in >>>> response to the proximity of the expiration date or as a result of changing >>>> market sentiment or increased trading in spot or in "dark pools", >>>> which impacts demand but is not reflected in bid/ask quotes. >>>> >>>> >the manner in which its parameters change overtime is not intuitive at >>>> >all >>>> >>>> If the value of the parameters themselves is not intuitive, then its >>>> change over time is very likely not to be intuitive as well and vice versa. >>>> Most non-intuitive parameter changes happen when the optimization surface >>>> is >>>> very flat or has many local maxima. Then a minor change in the data can put >>>> you into a very different local maxima and cause very unsettling parameter >>>> jumps. That is why restricting the optimization region to the vicinity of >>>> the most recent parameter values allows for parameters to only drift >>>> gradually. Then trends in parameter changes can be spotted and understood >>>> intuitively. >>>> >>>> ------------------------------ >>>> *From:* nonlinear5 <[email protected]> >>>> *To:* JBookTrader <[email protected]> >>>> *Sent:* Sat, December 4, 2010 11:34:20 PM >>>> *Subject:* [JBookTrader] Re: Dynamic Parameter Optimization >>>> >>>> > Eugene, your comment goes to the need to have sufficiently large >>>> backtest >>>> > database relative to the number of adjustable parameters, so that the >>>> results >>>> > are statistically significant. How does that relate to potential >>>> > non-stationarity of parameters? >>>> >>>> The non-stationarity of parameters is a problem, indeed. However, some >>>> things are more or less absolute. Think of the supply/demand >>>> relationship. If you can capture its essence in the strategy, that >>>> should work today, tomorrow, and 10 years in the future. Now, I do >>>> acknowledge that a better model would be the one which not only >>>> accounts for the supply/demand, but also for its changing elasticity >>>> over time. However, such model would be more complex, more difficult >>>> to understand, and more time-consuming to test. Perhaps more >>>> importantly, while the supply/demand law by itself is quite intuitive, >>>> the manner in which its parameters change overtime is not intuitive at >>>> all. The best we can hope for in our walk-forward optimization is that >>>> whatever parameters were the "optimal" in a recent period would still >>>> be the optimal in the next period. For the sake of this hope, we would >>>> be required to significantly shorten our optimization periods, thus >>>> incurring a penalty of standard error in our confidence bands. >>>> >>>> -- >>>> 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 jbooktrader+ >>>> [email protected]. >>>> For more options, visit this group at >>>> http://groups.google.com/group/jbooktrader?hl=en. >>>> >>>> >>>> -- >>>> 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]<jbooktrader%[email protected]> >>>> . >>>> For more options, visit this group at >>>> http://groups.google.com/group/jbooktrader?hl=en. >>>> >>> >>> -- >>> 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]<jbooktrader%[email protected]> >>> . >>> For more options, visit this group at >>> http://groups.google.com/group/jbooktrader?hl=en. >>> >>> -- >>> 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]<jbooktrader%[email protected]> >>> . >>> For more options, visit this group at >>> http://groups.google.com/group/jbooktrader?hl=en. >>> >> >> -- >> 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]<jbooktrader%[email protected]> >> . >> For more options, visit this group at >> http://groups.google.com/group/jbooktrader?hl=en. >> >> -- >> 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]<jbooktrader%[email protected]> >> . >> For more options, visit this group at >> http://groups.google.com/group/jbooktrader?hl=en. >> > > -- > 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]<jbooktrader%[email protected]> > . > For more options, visit this group at > http://groups.google.com/group/jbooktrader?hl=en. > > -- > 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]<jbooktrader%[email protected]> > . > For more options, visit this group at > http://groups.google.com/group/jbooktrader?hl=en. > -- 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.
