From: scc26mf@centauri.unm.edu (Mark Fleharty)
Newsgroups: misc.invest
Subject: Patter Recognition
Date: 26 Jan 1993 00:18:25 GMT
Organization: University of New Mexico, Albuquerque
Distribution: world
NNTP-Posting-Host: pioneer.unm.edu


Here is an article that a friend of mine wrote.
I did not post this article to promote the buisness of the company he
works for 'Hanseatic', I posted it here to increase the flow of information
and to promote education.  I have permision from the author and ofcorce
All disclaimers apply. 

__________________________________________________________

PATTERN RECOGNITION:  A NEW DIRECTION IN TECHNICAL ANALYSIS


INTRODUCTION

Over the past 10 years, trading system designers at Hanseatic 
have forged a new computer-
based pattern recognition approach to technical 
analysis of markets.  But before 
proceeding to a description of the 
kinds of patterns we have developed, it should be helpful 
to briefly set forth our philosophy about 
technical analysis and price behavior in markets. 


A CRITICAL VIEW OF TECHNICAL ANALYSIS

Over the years, the basic premise that has 
guided our market research is that price 
movement is sometimes random, but at other 
times manifests trends that are quite non-random
so we believe that price movement does 
derive from some underlying price 
structure that presupposes an inherent
order to markets.  For us, this has 
precluded any 
efforts dedicated to methodologies such 
as wave theory and Gann techniques, to say 
nothing of astrology.  Nor do we think there 
is any systematic cyclic component in price 
data.  We also do not believe there is any 
evidence of an existing method that is very good 
at market forecasting; we are much more 
interested in following markets well than in 
predicting price levels.  Much of our work is 
concerned with identifying better than normal 
risk-reward market relationships in one 
time dimension (e.g. weekly), and then recognizing 
low risk entry points in an adjacent time 
dimension (e.g. daily).

One final philosophical point has to do with computers.
Computers give us the advantage 
of being able to analyze and organize 
tremendous quantities of data and explore the 
relationships among different categories of 
data.  That is the good news.  The bad news is 
that without a strong sense of philosophy 
about how markets behave, and what system 
objectives one is aiming to achieve, would-be 
system designers are left largely with 
unproductive chases through the vast 
labyrinth of technical analysis techniques detailed in 
literally hundreds of books, periodicals and software.
Unfortunately, in markets, even 
poorly conceived systems work occasionally.


THE HANSEATIC APPROACH

Price is the only input to Hanseatic models.  
Sentiment measures and other exogenous 
variables may be useful in the U.S. markets
where objective measures can be formulated, 
but they are simply not practical in the 
analysis of cash currencies and other international 
debt and equity markets that are the focus 
of much of the work at Hanseatic.  In order to 
accommodate a range of investment objectives 
and horizons, we apply our models in four 
time dimensions: monthly, weekly, daily and hourly.

The platform upon which we have developed 
our pattern recognition approach to market 
analysis combines an exponential moving 
average of price with two momentum-derived 
oscillators.  The primary oscillator is 
relatively more reactive to price movements; the 
secondary oscillator is less reactive but more stable.
An important aspect of this platform 
construction is that it is not optimized 
in any way.  The degree of smoothing in the monthly 
Dollar - D-Mark, the weekly S&P500, the 
daily Nikkei-225 and hourly-IBM, for instance, is 
exactly the same.  Similarly, the 
construction of the oscillators is the uniform across time 
dimensions and markets.

The patterns are defined by either specific 
relationships among time, price and the 
oscillator components of Hanseatic's 
models or a sequence of these relationships.  In either 
case, the objective is to identify a 
specific reward-risk relationship which has above-average 
probabilities of generating profit in 
either the short or longer term.  To demonstrate the 
viability of each pattern, we have used a 
so-called "12-stock q^t" which consists of four years 
of daily data on the following stocks:

		AMGEN				MERCK
		COMPAQ COMPUTER		PHILLIP MORRIS
		APPLE COMPUTER			U.S. SURGICAL
		MICROSOFT				FANNIE MAE
		AMR					DISNEY
		INTEL 				DIGITAL COMPUTER

The patterns are in two general categories.  
The first category are transition patterns which 
basically try to identify a change in trend 
from one direction to anry of patterns 
relate to the identification of entry/re-entry points into 
trends.  These patterns inherently have 
higher than normal probabilities because of the 
persistence of trends in many markets 
and indi3 months, for example, 
there may be several daily entry and 
re-entry patterns.  These patterns have high batting 
averages, are of relatively short duration, 
and have returns that are good but necessarily 
constrained by the length of the trade.

The second type of trade pattern is designed 
to recognize and enter a trend that has the 
probability of continuing for some length of 
time, and then remain in the trade until it is 
recognized as having ended.  These patterns 
also have good batting averages, but last much 
longer on average and offer the potential 
of very large returns.

A word of caution about probabilities of 
achieving a positive return, or more simply, 
batting averages.  Most high success-rate 
systems are poor performers.  There is even 
evidence to suggest that there is an inverse 
correlation between high batting averages and 
return.  So for those patterns that do have high 
batting averages, our objective is to have 
the profit factor greater than 2.0 before 
transaction costs.  (Profit factor is a performance 
measure defined as the ratio of the total net 
profits from profitable trades divided by the 
total net losses from losing trades:  a 
break-even system has a profit factor of 1.)

An important aspect of our work is integrating 
the parameters of adjoining time 
dimensions to improve the risk-reward relationship 
associated with a particular pattern.  
For instance, favorable trend or momentum c
haracteristics in the weekly time dimension 
will improve the performance of a daily trend 
pattern.  Used in the opposite way, weekly 
trend and momentum measures can be used as 
filters to enhance the performance of a 
particular pattern.  Lower time dimensions 
parameters are generally used to improve the 
entry and exit points for next-higher time 
dimension patterns.

CONCLUSION

The observation and analysis of price patterns 
is the quintessential art form of technical 
analysis.  We, however, have never seen 
evidence that any of the traditional patterns hold 
up to any sort of vigorous statistical 
investigation.  Hanseatic has constructed algorithms 
which describe several transition and 
trend patterns, each of which has been successfully 
computer-tested over a large data sample 
with a minimum of optimization.







 
Ed Meihaus is Vice President and Chief Investment 
Officer for Hanseatic Group.  The 
Hanseatic Group is a New Mexico-based trading 
advisor/funds manager with fifteen years 
of experience in diversification-based 
management of currencies and both financial and 
commodity futures.  It has subsidiaries which 
are registered with the CFTC as a commodity 
trading advisor and the SEC as an investment 
advisor.  A third subsidiary participates in 
securities lending.  Hanseatic advises 75 
international banks on currency and interest rates 
and transmits the advice on a 24 hour basis.  
Hanseatic manages almost US$ 200 million in 
trading accounts for institutions and acts 
as a trading advisor to an offshore fund managed 
by Cragnotti & Partners, an equity investor in Hanseatic.

------------------------------------------------------

I had to put the above because Ed Meihaus wanted that to be there
other wise I would not have been able to post this article.

If you have any questions.  Feel free to E-Mail me or post
a message to misc.invest.
				Mark Fleharty,
				scc26mf@centauri.unm.edu
'Risk not thy whole wad.'