How Professional Traders Differ From Amateurs

Two weeks ago, we discussed How Experts Differ from Novices (in the ways people learn).

Along very similar lines, Brett Steenbarger looks at the differences between how How Professional Traders Differ From Amateurs .

This is quite instructive along several lines of thought:  1) the development of expertise (which I refered to in the "Zen of Trading"), as well as the decision making process investors and traders engage in.

Here are the 5 keys according to Dr. Steenbarger: 

1) Resources – These professionals had a wealth of analytic resources
at their fingertips–and they used these resources. They had a keen eye
for how their market should be priced and took advantage of occasions
when it moved from that benchmark.

2) Information Networks – The
pros knew other pros and constantly talked with them to find out what
was going on in the marketplace. This network was an important edge for
many of the traders.

3) Strategy – Every trader I talked with
could enunciate his or her specific edge in the marketplace and, in
some fashion, could quantify that. I could not find a pure gut trader
in the bunch.

4) Adaptation – Each of the pros knew details of
his or her P/L, but also detailed trading statistics such as Sharpe
ratios. When the stats veered off course, they were quick to make

5) Complexity – The professional traders employed
complex trading strategies that relied on trading different instruments
and timeframes, all to exploit a single idea. Many of these strategies
involved hedges that managed risk, even as they aggressively pursued
their ideas. The idea of buying/selling a single thing and exiting it
never arose in my conversations with them.

For those of you interested in Behavioral Economics, or the related psychology behind trading, check out  Dr. Steenbarger’s site TraderFeed.

Fascinating stuff, Doc. Thanks!

UPDATE:  May 15, 2006  6:57am

Freakonomics authors Stephan Dubner and Steven Levitt look at expertise; It turns out that experts are not born, they are made.


How Professional Traders Differ From Amateurs
Brett Steenbarger
TraderFeed, Saturday, April 29, 2006

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What's been said:

Discussions found on the web:
  1. trader75 commented on May 14

    One thing I am coming to believe more and more is that passion is a distinct edge in itself.

    When you are truly passionate about something, the concept of ‘work hours’ becomes irrelevant. The best traders are fascinated by the markets; they absolutely love what they do. When you are fully immersed in your craft, you are working even when you’re not working. Great ideas can pop up when you’re brushing your teeth or walking in the park or hitting golf balls on the driving range. Setbacks are opportunities to learn more, not reason to quit. The thought of quitting is like the thought of cutting off your right arm. All your subconscious ducks are lined up and pointed towards the goal. Because you love it, you live it.

    It’s the same with average guys and sports scores. How many guys say they hate math and see any type of figuring as a chore, yet they can memorize Kobe’s free throw percentage or Barry Bonds’ on base percentage with zero effort. Love makes the work easy–not like work at all. Which of course lets you work three times as hard as the guy who is force feeding himself… and finish the day with more energy to boot.

    Point being, anyone seeking to play a competitive game without passion is at a clear and compelling disadvantage vis a vis those who have it in spades. Everything else can be learned except that.

    Just my .02

  2. Robert Cote commented on May 14

    You overanalyze. Pros do one thing different, they modify their models based on results. Amateurs arrive with conclusions and seek out supporting data. Pros test their models and either modify or discard them. Disintermediation and technology have made information, resources and complexity available to all. This leaves adaptation.

  3. mentalmodel commented on May 14

    With the rise of the Wall St. quant, I’m surprised to see that institutions and professionals still use technical analysis when all sorts of more formally developed and tested methods exist (statistical arbitrage and market microstructure analysis).

    Is this a situation where politics wins out over performance? That is, fund managers and traders with no hope of ever understanding this stuff will ignore it in favor of Elliott Wave and the like.

    I’m not knocking technical analysis — I actually believe most securities markets are nowhere near efficient. It’s just that recent developments in applied math and computing make it look like gambling.

  4. trader75 commented on May 14

    Now THERE is an interesting debate topic. Quantitative models and rocket-scientist worship: worthy of the hype, or just another high falutin’ fad that will flame out when LTCM part deux comes to town?

    Seems to me that most quants toil under the stewardship of grizzled veterans, who know when to use the math and when to throw it out. There’s a reason it’s not the other way around.

    But then I ain’t no rocket scientist, so maybe I’m just talking my book…

  5. RW commented on May 14

    Everyone talks their book Trader75, the quants certainly included; it could not be otherwise. The problem with quants or other widely accepted truthiness is that, like LTCM, they tend to cluster in the ‘proven’ zones – the ones where the models say most profit is to be made – the problem there being that that reduces variance and variability is the stuff evolution works on; when there is too little of it the most likely result when the system changes (as it always does eventually) is extinction. Today’s predator is tomorrow’s prey; always. Humility is a good survival trait.

  6. John Navin commented on May 15

    I never think about my Sharpe ratio. Do you think Babe Ruth considered his batting average every time he stepped up to the plate? I don’t think so. I think the Babe was thinking about last night’s dame, last night’s cigar and last night’s shot of scotch.

  7. trader75 commented on May 17

    Check out this fascinating piece from Seed Magazine:

    Another interesting factor in terms of trading and investing success: Stress, or the lack thereof. Those who enjoy their work, and naturally traverse a rich array of topics in the course of their work, are more likely to encourage neurogenesis. Those who handle stress badly, or don’t like their work, or have a very narrow focus, or all three, are falling behind on the cognitive front. Them that has gets, and doing what you love can actually make you smarter (as long as it doesn’t stress you out too much). Zazen anyone?

  8. mentalmodel commented on May 17

    I can identify with trader75’s point of view about quants. William Poundstone documents the LTCM fiasco quite nicely in Fortune’s Formula. There you can also read about the investment philosophy of Claude Shannon (discoverer of information theory), which is closer to Buffett than Myron Scholes/Steven Ross.

    I read that there are two types of quants — those that have heard of Nassim Taleb, and those that haven’t. If quants were so great, there would be no need for traders. However I stand by my original assertion that most tools used by “chartists” are a holdover from the sixties when computers were either unavailable, or very slow (and nobody on Wall St had training in statistics). There are tons of tools available for offering predictions of price movements based on previous events — with p-values, which is something technical indicators will never give you. Some of the best are grounded in the various theories posited by behavioral finance.

    The quants are still pretty much wedded to the efficient market hypothesis, I think primarily for cultural reasons — Black Scholes and CAPM grew out of the Paul Samuelson school of finance (read about him also in Fortune’s Formula). Merton, Ross, and co regard Behavioral Finance as the enemy. Physics it ain’t.

  9. Justice Litle commented on May 18


    I agree with you that many of the popular uses of charts are a bit goofy. There is a whole lot of ‘mumbo jumbo’ in technical analysis, as Bruce Kovner has put it.

    With that said, I think charts still hold their own in certain unconventional ways–for example, as a compacted source of information and a sorting tool. It is possible to look at a significant number of charts in a compressed period of time and very quickly discern what is worth further examination. If one has an idea stable of, say, twenty stocks long and short in seven different industries, selected by various data-driven metrics and contemplations etc, then one can still use charts to ‘check in’ on the ideas in the stable relatively quickly each day / week etc.

    It is also possible to get a mental visual of market activity via chart… if you see a downside blowout followed by an immediate reversal on strong volume, that is a clue that a significant number of weak hands have been washed out and the composition of the longs has changed. The more layers of context you add, the more nuances you get… and the ability to distinguish subtleties and nuances in various situations, with an ability to ‘chunk’ the result into a single higher level action pattern, is a key differential between experts and novices. The skilled practitioner doesn’t calculate more quickly, he cuts off alternatives and comes to a recognized meta-level conclusion more quickly. Charts help with that.

    Last but not least, discretionary traders and investors are still playing a game that computers can’t beat in my opinion. Big Blue has taken on Garry Kasparov and supercomputers have beat out prop traders, but making money in longer timeframes is closer to the game of Go than the game of Chess in that raw computational ability still falls far short of the human mind’s creativity / combination capacities. Because this is true, a longer term trader / investor (i.e. someone who isn’t competing for ticks) has the task of keeping his mental landscape up to date with a consistent flow of useful information, as much of the valuable work is done in the ‘back room’ of the subconscious, and a very useful / practical element of that flow includes charts. Put fundamental information together with skilled intermarket analysis, add charts to assess multiple elements very quickly, and hey presto, you can have an actionable thesis develop that a computer could never replicate.

    Point being that a skilled practitioner can ignore the ‘mumbo jumbo’ and use charts to both assess a large volume of information quickly and visualize rapid shifts in investor composition / sentiment. This process has very little to do with the voodoo of elaborately named patterns or mystical Gann wheel type stuff; it is closer in spirit and application to the fundamental investor’s perusal of a balance sheet. The ‘expected value’ of a trade or an investment is cobbled together from a whole host of interoperating variables, and those variables can include the information presented by a chart.

  10. trader75 commented on May 18

    Oops, forgot to use my handle there. Oh well, not like I can’t be googled anyway…

  11. mentalmodel commented on May 18

    Thanks for the interest.

    “It is also possible to get a mental visual of market activity via chart… if you see a downside blowout followed by an immediate reversal on strong volume, that is a clue that a significant number of weak hands have been washed out and the composition of the longs has changed”

    Two points seem worth making. First, there is a sub-discipline in the field of statistics called Exploratory Data Analysis. Computer Science types have reinvented this for themselves as “data visualization”, and technical analysis has been slow to embrace it. Modelers tend to start there, and construct methods for data reduction or signal identification using pictures as a guide. In many areas, such as political science where statistics gets used extensively, these models usually don’t have very strong “physical” underpinnings. I think the case with financial markets and economics is more encouraging (hence the comfort physicists can find within it). Expected utility plays a role, but so does prospect theory, or something equivalent (call it behavioral bias, bounded rationality, herding, whatever you like). This leads to my second point. Terms like support, resistance, and weak hands appear almost everywhere you look, but almost never get mentioned together with fund flows, open interest in options, curvature in implied volatility surfaces, or sentiment measured by polls of retail and professional investors (answer yes or no, the market is overpriced but I continue to participate as it will go higher), even though they all seem to be measures of more or less the same thing — all things a good model should incorporate.

    My training in the dogma of science leads me to think that if I was handed other peoples’ money to invest, I’d probably choose to hire some experts in market microstructure analysis and behavioral finance to help identify trends as they develop, and make calculated gambles on the basis of this (all investing is gambling as I see it). The last thing I’d want to do is base my decisions entirely from a list of stock price charts coupled with a list of news articles (which seems like a pretty widespread practice on Wall St).

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