[From Norman Hovda (990810.12:15 MST]
(I'm willing to carry on with the above convention, but I'm also curious as
to why since the header of every e-mail msg contains similar info?)
On to help with modeling... assuming this is appropriate to this forum...
if not just send me on my way.
In examining PCT web info and demos I found a model that may
sufficiently correspond with my observations so as to be used to
simulate and/or monitor in real time organized exchanges in free
markets, i.e., the Dow Jones Industrial Averages (DJIA) and "An
experiment with a simple method for controlling an inverted pendulum"
by Bill Powers, "Journal on Perceptual Control theory, Vol 1, No 1,
1999, or at the very least a place to begin.
As best I call tell, given my rudimentary understanding of PCT, certain
modifications to Bill's model will be required but I'm in way over my head
as regarding the mathematical / engineering details described in the 10
page Journal article. Therefore I will rely on my mathematician /
programer to follow any specific suggestions any of you may offer.
OTOH, for this layman, I will appreciate efforts to KISS. <g>
Here's my situation as a PCT newbee. I'm looking to see how PCT may
assist with stock market analysis, i.e., the DJIA. I observe DJIA pattern
regularity that suggests a PC system controlling for balance over time.
In the above Inverted Pendulum demo the user/mouse controls the
reference signal (bob) and "... the cart immediately moves under the bob
and the bob quickly comes into balance." IMO the DJIA data, when
arranged according to specific parameters, implies a bob type reference
signal (price 10745 shown in table illustration at following URL) which I
am able to define and track. Note the Mode of the letters distribution at
table's far right column.
http://www.cbot.com/mplex/quotes/ldbbs/ldbs-dj1.htm
The data offer information regarding price as a messenger about a
perceived cart or value area range. Obviously price location above or
below the fair *value area* offers defined trading opportunities.
(The "value area" = Bill's "cart" and is represented in the gifs mentioned
below by a solid vertical line adjacent to the left side of each price
distribution. The "bob" is the mode of the price distribution)
I've developed sftwr producing a continuous stream of actual measured
bobs and carts data, minute by minute, derived from variety of timeframe
sample sizes of DJIA tick data. All timeframe bobs determine respective
timeframe cart location by design but I observe as well (perhaps similar
to the Counter Control mentioned 990805 & 6 by Rick and Bruce) that,
at times, larger timeframe carts' balance is controlled for a shorter
timeframe bob and likewise there are occasions when shorter timeframe
carts' balance is controlled for a larger timeframe bob. However, the
complex interplay of the various timeframe bobs make it difficult to
determine *which* timeframe bob is dominating, i.e., for which bob is
the market gaining and/or maintaining the most effective control.
http://www.primenet.com/~nth/DDD1.gif
The above gif shows all three timeframe carts and bobs lined up.
http://www.primenet.com/~nth/DDD2.gif
This second gif shows the same three timeframes as DDD1.gif, but later
in the day, where the left and smallest timeframe cart and bob are
dominating, or if PCT can assist with this analysis, *possibly*
demonstrating the most effective control.
Now the question needing answer is, is it more likely that the smaller
cart and bob timeframe maintain control so that the larger timeframe will
reorganize / balance at lower prices or will the larger timeframe cart and
bod regain control thus eventually bringing the smaller timeframe cart
and bob into alignment with its larger self?
If the above is seen to be at all in the PCT ball park, what modeling
suggestions would anyone care to offer which
1. may help determine which bob, as a reference signals from a variety
of timeframe sample sizes, is the most effective real time controller?
and/or
2. to create a real time index that would represent an implied reference
signal bob based upon the aggregate of actual bobs and carts data from
a variety of timeframe sample sizes.
TIA,
nth
