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.

Equipment that 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 away efficiency.

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.

I’ve often 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.

Acquiring and 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.

The learning 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 trader.

Let’s look at one key area that makes trading very different from doctoring or engineering:  the search for certainty or “What happens when you do everything right and it still turns out wrong?”

Doctors, 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.

Trading is 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) related…

• The rules 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 in control)

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.

So what?

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.

A useful 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 tweaking.

Each trade 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.

Reviewing only large data sets creates a discipline that serves several purposes:

• It reduces stress by telling our mind that no single trade matters very much, as long as we follow our rules.
• It reduces the variability of results over time because we’re only adjusting our system or strategy after an appropriate interval of time.
• It greatly 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.

Great Trading,

D. R.