At our dinner with Peter Brandt a few weeks ago, there was a point where Peter pulled out his iPad. He wanted to show us something created by a trader in his small group. (This same trader is known, in certain circles, for making the single most profitable trade of all time, in absolute dollar terms, for one of the top investment banks on Wall Street.)
What Peter showed us was a series of equity curve charts. These charts looked dramatically different in character. If you imagined them as stock charts, the “stock” behind each chart had fairly extreme personality variations.
Some of these charts marched upward and to the right in a pleasingly smooth angle. Other charts swung their elbows wildly. Still others looked like manic depressives: Plunging downward early on… flat-lining at the depths… and then rising up like a Phoenix, taking off like a rocket ship in the final stretch.
But here is the kicker: They were all randomly generated outcomes for the exact same system. These charts were simply the result of plugging the SAME stats into a Monte Carlo results simulator, and then seeing the variations of what came out.
Imagine trading system X. Imagine that this system has long-run historical results something like this:
- Average win rate 45%
- Average win size 2.0R
- Average loss size 1.2R
- Average win size variation Y
- Average loss size variation Z
- etc. etc.
The point is that you can come up with a hypothetical win/loss profile (including variations of win and loss size) for any trading system — or any trader for that matter, based on historical data — and plug those variables into a hypothetical Monte Carlo generator to create multiple possible output paths, and the variation in these paths will likely be extreme… so extreme most people would have no idea.
Wild-looking differences in equity curve charts, then, are just hypothetical “counterfactuals” of what a statistically analyzed track record might look like after thousands of trades, assuming you knew beforehand how the stats would pan out.
Same system, different landscape, different short-term outcomes… all converging on the same long-term path, but with quite different means of getting there.
This has huge implications.
Imagine a trader builds and executes system X for twelve months. The results are in line with expectations. Better still, the equity curve is smooth and attractive.
He shares the system with another trading friend. This friend executes system X perfectly, but in a different calendar year (for a different twelve-month period).
This time the results seem volatile and terrible. The first eight months in particular are teeth-grinding to the extreme. By month nine the second trader is thinking the system is broken or markets went bad and he should probably dump it.
Where did the smooth gains go? What happened?
Possibly nothing at all. It might be the exact same system… distributing an alternate variation of outcomes that are still 100% “valid” and “normal” in terms of probability distributions as to how the system could be expected to perform over long periods of time.
Lots of other implications. Some traders will be forced to wonder if they are fools or idiots (probably not). Others will be tricked into thinking they are gods (definitely not). Just variation in the cluster of probabilistic pathways as they deviate from the mean.
This makes perfect sense when you think about it. Given a set of statistical parameters, the number of probabilistic pathways that result in moving from point A to point Z are practically infinite. There are more legal games of chess, leading to either checkmate or stalemate, than there are particles in the known universe. So why wouldn’t there be a wide distribution of possible paths in getting from A to Z?
To give a more concrete example: Imagine two software entrepreneurs with equal talent, passion and dedication. Imagine they both start their careers at the same time, and both reach a $100 million net worth by age 45. What does that tell you about the similarity of the paths they took? Nothing at all.
One might have started half a dozen companies… failed three times… had two “almost” hits with small scores… and then hit paydirt on the sixth try. The other might have become an early stage “unicorn” employee… saw his net worth peak at $200 million right out of the gate… and then lost half his fortune after holding on through a disappointing IPO and crash.
There are as many ways to “arrive” at a destination as there are trip routes from New York to Los Angeles! (With some of the routes far more aesthetically pleasing than others…)
The implications are larger here still in respect to psychological effects, money management impacts (especially given many investors have no natural sense of probability whatsoever, or differences between skill and luck for that matter), and the potential fallout of outlier losses.
I have a theory crystallizing as to why probabilistic variations in system outcomes both reinforce and support the “90/10″ profit distribution outcome so common among successful traders. And a couple more “Winning Trader Series” entries to develop too…
Disclosure: This content is general info only, not to be taken as investment advice. Click here for disclaimer