Jack Schwager Market Wizards Lecture

market_wizards_by_jack_schwager I just watched a video lecture by Jack Swager, author of trading classics Market Wizards and The New Market Wizards.   If you haven’t heard of them, in each book Schwager interviews top traders and picks their brains about trading, the markets, and what made them successful.

The reasons these works are revered as classics is not because he gets the Market Wizards to reveal their “magic” strategies.  In fact not one says explicitly how to profit trading and they all have different methods.   What we do get is insight into what makes them tick.  See below for a partial list of traders mentioned in the video.  Its a very accomplished group.

In the lecture, Schwager pulls together the common traits of these elite traders and distills them into critical success factors.  All are important ingredients for success.  The one I want to highlight as critical is Schwager saying that none of the wizards would do something like “la-de-da today looks good to buy bonds”.   They all had some sort of pre-planned strategy, that strategy gave them an edge in the market, and they knew what to do with it.  Schwager also pointed out that by entering the market without a plan, the amateur trader can do worse than chance.

Schwager touches upon the paradox that trading seems easy yet requires a tremendous amount of work to master – I can definitely relate!

 The video (and the books) are somewhat dated.  I doubt the traders Schwager mentions are today getting chart books delivered to their homes on the weekends.   These days, the web and services like Market Club offer charts on about every market that moves so we can all pour over thousands of charts like the masters.   Or, we can program our computers to scan for us.   Schwager’s comments on computerized trading is another area that is outdated.

Even so, many of the traits and behavioral patterns that made these traders great can offer us timeless lessons towards success.    Here’s who I heard Schwager cite as Market Wizards: Jim Rogers, William O’Neil, Ed Seykota, Michael Marcus, Marty Schwatrz, Paul Tudor Jones, Monroe Trout, Linda Raschke, Van Tharp, William Eckhardt, Stanley Druckenmiller (worked with George Soros).

Click here to watch this complimentary video  

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Expectancy Definition

Expectancy measures a trading strategy’s profit potential.   It considers both the reliability or win rate as well as the amount gained by each win.    That way, it can compare trading strategies that often win small gains with strategies that rarely win but win big when they do. 

Expectancy = (win_rate * avg_win) – (loss_rate * avg_loss)

Van Tharp defines expectancy in terms of risk here, as the average of the R-multiples returned by trading or backtesting the system.

Extra Insight:

Over a large number of trades, the expectancy is the expected gain of the trading strategy.  Higher expectancy is generally better.  Always avoid trading strategies with negative expectancy.

Scaling the expectancy by risk is indeed useful, especially when it comes time to compare different systems.  I use the R-multiples as suggested by Van Tharp for ease of calculation.

Expectancy is also known as the Kelly Criterion for the Bell Labs researcher who proved the equation as an upper bound on the amount to risk.     A common language way to say it is to risk an amount proportional to the expected gain.   So if the expectancy is 45%, Kelly advocated risking 45% of the account value.   This may be mathematically optimal over a large number of trades but it can have a very vicious drawdown!   Imagine trading a high expectancy system, say 80% and the first trade is a loss.  For a $100k account, that would leave only $20k in the account and a long road to make a 4x gain to break even.

Expectancy is not the be-all and end-all of a trading system.   The standard deviation or variance of the results is important.  The win rate is too.  Both give insight into how psychologically difficult it is to stick to the trading strategy.

Updated: 11/12/08.

Results Distribution Definition

The Results Distribution is a graphical way to show the performance of a trading strategy.   The graph above shows the results of backtesting a trading strategy across several thousand stocks over a 3-year period.

Extra Insight:

Let’s take apart a simpler example:

Each trade is assigned to a bin depending on its profit/loss results.   Different people use different measure of results: dollar gain, percent gain, etc.  I use Van Tharp’s R-Multiple which is the gain divided by the amount risked.   I label the bins with the mid-point of the range.  

For example, a trade that gains twice what was risked goes into the “1.5” bin along with all the other trades that returned between 1 and 2 times the risk amount (R-Multiple).    

The horizontal axis shows each bin and the vertical shows the number of trades in the bin.   The red line is the zero point which separates winning and losing trades.  In some graphs, profitable bins are green and losers red.  

In our example above, we see that 196 trades returned a profit that was less than the amount risked (less than 1 R-Mult) because the bar for the “0.5” bin is 196 trades high.   Unfortunately for this strategy, even more trades lost money, which we can tell at a glance from this graph.

In general, better strategies have more action to the right of zero on the chart – more profitable trades either in quantity or quality or both!

The Puppetmaster’s article on Redistribution is an excellent illustration comparing the results distributions for two different trading strategies.

Updated 11/12/08.