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> { An N^2 day test, with a perfect random walk, is expected to have
> > > a standard deviation N times larger than a 1-day test.
> > > So the "Expected" column is N, and the "Actual" column is
> > > N^2-day_StdDev / 1-day_StdDev. }
The above is from the comments to Gary's trendiness indicator code.
Are these comments correct? Shouldn't the first sentence be: "An n^2 day
test, with a perfect random walk, is expected to have a standard deviation
N times larger than a 1-day test, ASSUMING THAT THE 1-DAY TEST IS ALSO A
RANDOM WALK."
Since it's not, I'm confused as to why we are willing to base the whole
indicator on the 1-day SD. Isn't that arbitrary? Why not base it,
arbitrarily, on a 16-day SD, and extrapolate the 16-day to all the other
time frames, including back to the 1-day? Wouldn't that skew the results
of all the time frames in a completely different way, sending some of the
former "anti-trending timeframes" onto the "trending timeframes" list, and
vice-versa?
As far as I can see, this indicator could be useful for ranking markets, by
trendiness, in a given time frame only. But the trendiness vs
anti-trendiness line-in-the-sand idea, strikes me as meaningless because it
isn't independent of scale.
I'm not a heavy stats or math guy, so please feel free to go ahead and tell
me what's wrong with my thinking.
Paul
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