Reason I’m asking, is that I don’t want to waste time duplicating work. I’ve googled a bit, but nothing I can find is terribly clear, that I can see.

For anyone unfamiliar: imagine that any waveform (such as a price chart) is a sum of component frequencies, each with various phases and amplitudes which can be described with a function. For instance, if the market were a pure sinusoid, or maybe a sum of two sinusoids of fixed magnitude, the market would be flawlessly predictable and everyone would be rich (or, break even, as no one would lose money ever again).

Of course we aren’t dealing with anything nearly so perfect, but over short timeframes and with some attention paid to the periodicity of one day and lower, any ‘edge’ to be had will stick out like a sore thumb, such as a huge dip or ‘spike’ at certain frequencies, when compared to others. And yes, I know that markets are supposed to be fractal and so forth, but as many of us know: asian session often doesn’t have the range that London does, &c &c. True fractals don’t have such limitations, thus, there are some basic statistical variations out there. Maybe nothing big, but I’d like to have a look at them anyway. A 51%/49% bias is more than enough, if it’s robust.

There are some websites with rudimentary analysis regarding seasonable variability (one particularly long, singular frequency component of 52 calendar weeks); I am sure there are some hard stats with regard to weekly, daily as well. I’m wondering if anyone’s compiled this sort of thing anywhere, or, perhaps just in private? If I had 20 or 30 years of tick data in the right format, and say, a friend with a Mathematica licence, it would be fairly easy to have a look… but I don’t quite have that. But I’m sure *someone* must have done it. Something a bit more graphically and completely presented than “here are the daily ranges for typical Monday through Friday, and the weekly pivot points.”

Eremarket has mentioned a statistically significant bias toward S1/R1 turning points, and ICT has mentioned that maximums or minimums often happen in the first four hours of trading… perhaps we can get a graph of all this sort of thing combined?

That’s what I’m looking for with Fourier analysis, though it would have to be ‘back translated’ into useful statistics from there, I’m sure. The idea being, one could also see how robust such stats were with regard to each other ~ loose correlations, strong correlations or what. For instance, if Statistic A implies price may go up and Statistic B implies it may go down… which to weigh more?

Sounds a bit abstract, and yes it won’t be able to process individual events like ‘Bernanke started talking pessimistically and price did X’ but over the long haul, may expose some advantages.

Insights very welcome. This is for my longer term forex education. And of course if anyone happens to have price data and say Mathematica or MatLab I’d love to see some results…