--- In
amibroker@xxxxxxxxxxxxxxx, "Mike" <sfclimbers@xxx> wrote:
>
> Bing,
>
> In this example, the t-test is calculated to give us a level of confidence that the average of the sample is different than zero.
>
> If a trade strategy had no predictive power, then its results would be purely random, producing a net gain (over the long run) of zero with an equal number of winners and losers.
>
> Actually, it would be a net gain of zero *over the prevailing trend*, where the trend itself might be greater than zero, as per Aronson. But, that is another conversation.
>
> The more trades taken, the more likely the true average would show. For example; Flip a coin 4 times. You might get 3 heads, 1 tail for an average of 0.75 heads. Flip a coin 1000 times and the average number of heads will be much much closer to 0.5.
>
> Going back to your trade example, if we are getting a non zero average after thousands of trades, then we are more and more confident that in fact the average is not zero. Thus, the larger t-test score is justified, and is in fact built into the equation.
>
> In other words, you don't have to worry about getting a SQN score of 7 after 5000 trades, because you will likely never find a trade strategy that is capable of producing an expectancy of 0.1 after that many trades!
>
> Mike
>
> --- In
amibroker@xxxxxxxxxxxxxxx, "bingk66" <bing.kwok@> wrote:
> >
> > Hi Howard,
> >
> > If there are no means to limit the number of transactions in the calcs, then one seriously runs the risk of challenging the mystical t-test score of 7 that you spoke about previously.
> >
> > As an example, if the OOS test was run over a 5 year period with 5000 transactions (a mere 1000 transaction/year, which is not excessive, especially for very short term trades), sqrt(5000) alone would yield in excess of 70 for the multiplier. This would leave expectancy/StdDev of R with just a target of 0.1, to reach the 7 t-tests score.
> >
> > Now, if you had 1,000,000 tranasctions in your OOS test....
> >
> > The concept of limiting the trade count does make sense to me. Maybe 100 is too low, and should be set higher. There does come a point whereby the sqrt(N) part of the equation will render the rest of the equation irrelevant once N gets too large.
> >
> > $0.02
> >
> > Bing
> >
> >
> >
> > --- In
amibroker@xxxxxxxxxxxxxxx, Howard B <howardbandy@> wrote:
> > >
> > > Hi Zozu --
> > >
> > > I must disagree with Van Tharp on this.
> > >
> > > If the runs are truly out-of-sample, then each and every one contributes to
> > > the computation. It makes no sense to limit the count to 100. It is poor
> > > procedure to limit the count. It is bad science to limit the count. Do not
> > > limit the count.
> > >
> > > If the runs are in-sample, then the test has no meaning anyway. Computing
> > > the t-test statistic using any N will be misleading. Do not even do the
> > > computation. If a decision to trade a system is made after computing the
> > > t-test statistic on trades that came solely from in-sample results, there is
> > > an extremely high probability that a Type I error will be committed. That
> > > is, the trader will believe that his system is better than random, when it
> > > is in fact not better than random. Type I errors result in loss of money.
> > >
> > > Thanks,
> > > Howard
> > >
> > >
> > > On Tue, Oct 13, 2009 at 10:54 AM, zozuzoza <zozuka@> wrote:
> > >
> > > >
> > > >
> > > > Hi Howard,
> > > >
> > > > Limiting the number of N doesn't mean that you are not using all trades for
> > > > the calculation of SQN. Only the sqrt(N) part of the formula is limited in
> > > > order not to distort the results if there are many trades. It makes sense.
> > > > The other part of the formula does count on all the trades.
> > > >
> > > > Zozu
> > > >
> > > >
> > > >
> > >
> >
>