information frequency vs tradeability

[rudolf stricker <lists@xxxxxxxxxxx>]

List,

I would like to get comments on some ideas on the subject above:

>From Fourier's theorem we know, that at least 4 points are needed to
identify a "wave form" in a series of data vs time (like price data).
Because this is valid independently from the kind of problem or the
analysis method used, its also valid for "trading" and for any "TA
technique". So based on eg day-to-day data, we can identify / predict
only waveforms of at least (3 to) 4 days (to make some profit from
them).

If we look at the price data of our favorite security from this point
of view, we can construct probability distributions for price changes
for eg 1, 2, 4, 8 days (when working with day-to-day data) and compare
eg the probability to loose / win more than xx% for these time
intervals. To make it more simple, we can also calculate the standard
deviation for these price changes (, assuming normal probability
distributions).

If we read from these data, that e.g. the price changes for 1 and/or 2
days are much higher than for 4 days, my thesis is, that we cannot be
consistently successful with applying TA techniques to trade these
securities. Or otherwise: The size of 1 and 2 day price changes
divided by the 4 day price changes is a measure for the "general risk"
or "non-tradeability"  of this security. - Improvement of the
"tradeability" can be achieved (only ?) by an increase of the
information frequency from this point of view.

So my questions are:

What do you think about these ideas? Is there anything fundamentally
wrong with it? 
Are there around any approaches of similar or other kind to measure
the "tradeability" of a security? 
How does your favorite security look like in this light, eg in terms
of "standard deviations"? What "general risk" rates do you trade
successfully using TA techniques?

Any comments and suggestions are welcome.
mfg rudolf stricker
| Disclaimer: The views of this user are strictly his own.