If ever there was a valid application for the cliche that change
is the only constant, it's algorithmic trading. It is a technology
that, by its nature, exists on shifting ground. The financial
institutions and system developers who drive algorithmic
innovations--a crowded field that is accustomed to competition as a
permanent Wall Street state--have had to pick up their pace and
raise their game ever higher. New products are readily mimicked if
not surpassed, and that happens in not much more time than it takes
to run a strategy that is built on trading decisions that machines
make and carry out in subsecond timeframes.
So how does anybody keep up with all those market demands for
more and faster electronic trading products? And how can we, using
the traditional medium of words on paper (or on a Web page),
capture what's happening in a business that changes by the day,
hour or even minute? What we can do--and what's probably necessary
amid the frenzy of moment-by-moment activity--is step back and put
these developments in perspective. For all the talk of millisecond
and microsecond executions, algorithmic and programmatic trading
systems are the result of months and years of research and
development. As Securities Industry News has covered the
leaders in algo trading--ranging from Credit Suisse to agency
brokerages such as Instinet and Investment Technology Group to
Goldman Sachs & Co.--one of the common denominators, or
constants or recurring themes, has to do with consistency and
commitment over a long period of time. The execution of trades may
occur in under a second, but the execution of algorithmic trading
as a business strategy is anything but short-term or instantaneous.
This isn't for dabblers, and that's why we find so much that is
newsworthy to delve into and cover in our weekly and daily media.
One of those new trends, interestingly, is that algorithmic systems
are becoming consumers of news, and Dow Jones & Co. and Reuters
Group, among others, have developed
"machine-readable news" products to feed the analytical maw
alongside conventional market data. |
