Learning to Trade:
The Psychology of Expertise
Brett N. Steenbarger, Ph.D.
When people hear that I am an active trader and a professional psychologist, they
naturally want to hear about techniques for mastering emotions in trading.
That is an
important topic to be sure, and later in this article I will even have a few things to say
But there is much more to psychology and trading than “trading psychology”,
and that is the ground I hope to cover here.
Specifically, I would like to address a
surprisingly neglected issue:
How does one gain expertise as a trader?
It turns out that there are two broad answers to this question, focusing upon quantitative
and qualitative insights into the markets.
We can dub these
These perspectives are much more than
academic, theoretical issues.
How we view knowledge and learning in the markets will
shape the strategies we employ and—quite likely—the results we will obtain.
article, I will summarize these two positions and then offer a third, unique perspective
that draws upon recent research in the psychology of learning.
I believe this third
perspective, based on
, has important, practical implications for our
development as traders.
Developing Expertise Through Research
The research answer to our question says that we gain trading expertise by performing
We collect a database of market behavior and then we research
variables (or combinations of variables) that are significantly associated with future price
This is the way of
mechanical trading systems
, as in the trading strategies
developed with TradeStation and the systems featured on the FuturesTruth.com site.
become expert, the mechanical system trader would argue, by building a better mousetrap
—finding the system with the lowest drawdown, least risk, greatest profit, etc.
A variation of the research answer can be seen in traders who rely on
The data-miner questions whether there can be a single system appropriate for
all markets or appropriate for all time frames.
To use a phrase popularized by Victor
Niederhoffer, the market embodies “ever-changing cycles”.
The combination of
predictors that worked in the bull market of 2000 may be disastrous a year later.
data-miner, therefore, engages in continuous research: modeling and remodeling the
markets to capture the changing cycles.
Tools for data mining can be found at
There are hybrid strategies of research, in which an array of prefabricated mechanical
systems are defined and then applied, data-mining style, to individual stocks to see which
ones have predictive value
This is the approach of “scanning” software, such
as Nirvana Systems’ OmniTrader.
By scanning a universe of stocks and indices across
an array of systems, it is possible to determine which systems are working best for which