Tuesday, July 08, 2008

The Psychology of Profitability




Here are the equity curves for three traders over a 100 day period. Trader A (top chart) made money during the first half of the period, but gave it back in the second half. Trader B (middle chart) chopped around early, made money toward the middle, and gave some back late in the period. Trader C (bottom chart) lost a fair amount of money early on and recovered in the second half of the period.

Who is the better trader?

I strongly, strongly encourage you to check out the latest profit/loss (P/L) forecaster from Henry Carstens of Vertical Solutions. This is different from the forecaster I wrote about recently, which illustrated the impact of volatility on returns. (Both forecasters are linked here for comparison). This latest P/L forecaster predicts equity curves based upon the average win size per trade, the ratio of the average size of winning trades to the average size of losing ones, and one's winning percentage.

If you enter the data from your own trading, you will see forecasted paths of returns for your trading. If you use the same data and generate multiple forecasts, you'll catch the variation in your returns that might be expected by chance. That is eye-opening, because you'll see that the path to your returns can vary significantly simply due to chance.

So back to our three traders above. Who is best?

You may have guessed that they're all the same. All have an average win size of $100 per day. All have 50% winning trades, and all have average win sizes equal to average losers. In other words, none of these traders has a distinctive edge.

None has an edge, and yet the paths of their returns are quite different. If wins and losses amount to coin flips, we see more runs in a random series than we would expect. These runs show up as winning trading periods and losing ones.

During the winning periods, the trader is apt to think he has a hot hand and will increase his risk. During the losing periods, the trader will think he's gone cold and will cut his trading size back. Think about how such money management might affect the paths of the above curves.

How we interpret and respond to randomness helps explain why so many traders lose money even when their odds of winning and losing are even. Add in commissions and other overhead and you can appreciate how difficult trading can be.

RELATED POSTS:

Self-Inflicted Trading Problems

What Contributes to Profitability?
.

3 comments:

Eyal said...

You might also be interested in this:

http://www.hquotes.com/tradehard/simulator.html

Brandon Wilhite said...

This is a great post. These types of thought experiments are very helpful.

Brett Steenbarger, Ph.D. said...

Thanks for the link, Eyal. I do find the "what if" scenarios quite helpful in illustrating risk/reward concepts.

Brett