Thinking Differently for Traders and Investors
When I was
training to be a chemical engineer, decision making was quite
“black and white”: Learn
the rules of the physical world and then apply them.
Learn how molecules combine and separate.
Learn how mass and energy get transferred from place to
place. Learn what is
economical and what is not. And
lastly, study hard and get good grades.
Once I was out
and practicing engineering in the real world, things weren’t
always so cut and dried. Outside
influences often complicated things.
The world of “black and white” became a world with many
shades of grey. In a
pristine lab environment (like back at school), molecules always
combined the same way. But
in the real world, contaminants could get in the system and reduce
yields or create new and undesirable products altogether.
was rated at “x” horsepower would always seem to run a little
less efficiently because some field condition (normal wear, extra
bends and turns, fluctuating temperature and humidity) would strip
In the end, the
best engineers in the real world of processing plants were those who
could deal most effectively with problems and conditions not found
in the text book. And it
will come as no surprise to most that raw intelligence was not
directly correlated to success on the floor of a chemical plant.
The best engineers had a certain savviness or what my Dad
would call “horse sense” about the best engineers.
And I have
found a very similar quality in the best traders and investors that
I’ve gotten to know. They
don’t have to be the most book-smart folks (though a few are), but
they have a certain grounded grasp of the big picture that allows
them to adapt, correct and continue with self-assured ease.
This group of
characteristics that turns the average thinker into someone with
good common sense or “street smarts” is somewhat difficult to
sum up in a few sentences. But
let’s look at some of these concepts or decision making loops that
may be most easily modeled.
Trading Is Not Engineering or Accounting
Before we jump
into some key decision making characteristics, let’s be clear on
the differences between the learning paths for trading and the path
for traditional knowledge-based professions like engineering,
accounting or medicine.
heard professional traders lament that those desiring to learn their
craft see a few mouse clicks and some fairly elementary math and
assume that they, too, can be consistently successful traders and
investors in matter of days or weeks.
Some pro traders will respond to this sentiment with a saying
like, “A highly paid doctor or lawyer had to study for years
before getting compensated handsomely.
Who would expect to get paid like me after studying just a
couple of weeks or months?”
And while part
of that thought process is correct (the fact that there are
knowledge based aspects to both trading and accounting), there is
also a major fallacy in the argument.
demonstrating minimal proficiency in the basic knowledge set needed
for engineering or accounting or law or medicine will lead to a
well-paid position for the vast majority of participants.
Not so for traders.
path for a trader is more like that of a professional poker player.
Demonstrating knowledge and proficiency in the basic skills
only gets you a seat at the table, it doesn’t assure you of an
income. While the
poker-trading analogy isn’t perfect, their paths of progression
are much more related than that of an doctor or an engineer and a
Let’s look at
one key area that makes trading very different from doctoring or
search for certainty or “What happens when you do everything right
and it still turns out wrong?”
engineers and indeed most professions live in a cause and effect
world. If you do A,
then a very high percentage of the time B will follow.
There are notable exceptions, when a treatment doesn’t work
or a product line gets contaminated, but by and large if you do the
correct action, you get the correct result.
quite different. A
trader can have the perfect set-up and entry, execute everything
perfectly and still have the trade result in a loss.
While traders lose money in this situation, that’s a
problem perhaps but probably not the biggest problem.
The largest problem for most people is the mental disconnect
between cause (doing everything right) and effect (losing
money). That result
conflicts with their classical education which does not equip them
with the tool set required for managing uncertain outcomes.
If we do things
exactly right and still only get the desired result 60% of the time
(or 50% or even 40% in long term trend following systems),
traditional cause-and-effect thinking
can easily make damaging conclusions.
Since cause and effect seem only causally (no pun intended)
really aren’t good and don’t serve me.
I no longer
need to follow my rules exactly. (or at all)
I can tweak
the rules so I am in better control (or at least feel like I’m
When a trade or
group of trades doesn’t come out well, our typical human reaction
to solve a problem kicks in. Almost
all traders and investors tweak their systems and strategies
prematurely, based on too little data (too small of a sample size).
So many people have been trained in an education system that
teaches us to solve a problem if we don’t get the desired outcome
with pure cause and effect thinking.
Dealing with highly complex systems with great levels of
uncertainty is just not in most people’s basic educational
background or experience.
The good news
is that cause-and-effect thinking works in most areas of our
personal and professional life.
And it is deeply rooted in our need to be right. It
does not, however, serve traders well.
solution to overcome our mental “cause and effect” disconnect is
simple to describe, but it’s very difficult to adopt for the long
term: broaden your view of trading results.
We must allow our trading and investing strategies to play
out long enough to reach their expected profitability.
Fretting and wringing your hands over the results of every
trade is not very useful and can lead to premature judgments and
should be evaluated ONLY in terms of whether or not we followed our
trading rules without regard for the dollars and cents results.
Reset your cause and effect decision process only after you
have a group of 30 or 50 trades (or an even higher number if you
trade more frequently). Then
you can evaluate cause and effect on a statistically valid data set,
not on any one trade or group of trades that have so many more
outside influences than one can ever hope to control.
large data sets creates a discipline that serves several purposes:
stress by telling our mind that no single trade matters very
much, as long as we follow our rules.
the variability of results over time because we’re only
adjusting our system or strategy after an appropriate interval
increases the chances of profitability because we do fewer of
the systematic things that cause losses.
No one trade is
important (as long as you always respect your stop loss)—it is
just a useful data point as part of the larger whole.
Allow yourself and your strategy the luxury of time.
And don’t be surprised if lower stress and greater
profitability follow close behind.
D.R. Barton, Jr.: A
passion for the systematic approach to the markets and
lifelong love of teaching and learning have propelled D.R.
Barton, Jr. to the top of the investment and trading
arena. He is a regularly featured guest on both Report
on Business TV, and
WTOP News Radio in Washington, D.C., and has been a guest
on Bloomberg Radio. His
articles have appeared on SmartMoney.com and Financial
Advisor magazine. You may contact D.R. at
"drbarton" at "iitm.com".