would soon be an opportunity to take the other side. “The market doesn't reward idiots,” Maxwell once explained. By fading the fearful herd, he knew he could make a good living.
When I had the opportunity to watch Maxwell trade live, I realized how he was so consistent in his performance. He sat and sat and sat for good amounts of time, waiting for the market to “set up.” During this period, he would observe the flow of volume at various price levels. Watching a depth-of-market screen, he could tell when buyers were coming in or exiting at a particular level. If buyers couldn't take out a prior high or sellers a previous low, he would quickly take the other side. To no small degree, his trading was predicated on figuring out when traders were wrong before they figured it out.
Maxwell loved busy markets: The greater the order flow, the greater the opportunity to find those occasions when bulls and bears were caught in bad positions. Most of his sitting occurred during slow market periods. “There's no one in there,” he would shrug. An important part of his edge was not trading when he perceived no advantage in the marketplace.
The bull market ground on, VIX moved steadily lower, and the average daily ranges shrunk. Maxwell found himself with fewer opportunities. To make matters worse, individual traders were becoming less dominant in the market, as daytrading lost its appeal to the public in the aftermath of 2000 and proprietary trading began its descent in the wake of automated market making. With fewer idiots trading, Maxwell's profitability began a slow, steady decline. Gradually he began to wonder if he was the idiot. With larger trading firms increasingly relying on execution algorithms to get best price, prices moved differently than in the past. More than once, he lamented that the old ways of gauging buy and sell levels no longer worked.
Maxwell's risk management was good, so he wasn't losing much money. He also wasn't making much, however.
Not many peaks.
Not many valleys.
The passion ebbing.
Just like Chris and Gina – committed to what worked in the past, unable to find a bridge to the future.
You don't just wake up one morning and discover your edge is gone. Rather, as with those Kuhnian paradigms, negative evidence accumulates gradually until it is no longer possible to ignore. The restaurant owner who sold to Emil did not suddenly come to work and find no customers. The erosion occurred over time, during which he did everything possible to boost traffic: Change menu items more frequently, lower prices, run specials, and so on. All of these were changes within the same paradigm, however: They were rearrangements of deck seating on a sinking ship. Incremental changes don't work when qualitative change is necessary. For the restaurant owner, qualitative change was a bridge too far and he sold to Emil.
How many years did Gina and Chris plod forward in a marriage losing its compass before the contradictions accumulated and it was no longer possible to ignore the absence of closeness in the close home they were committed to building? An object in motion, not acted on by any outside force, remains in motion – and people are no different. We are wired to conserve energy: Constant change would be disruptive, exhausting, and inefficient.
It makes sense from a purely evolutionary perspective that what has enabled us to survive would become our default mode, our status quo. Maxwell rationalized the decline in his profitability in many ways: as the result of stress, burnout, high-speed algorithms, and just pure bad luck. Each rationalization helped sustain the status quo. “Don't fix what isn't broken” is common advice. As long as we convince ourselves that we're not broken, we don't have to seek fixes.
Key Takeaway
Routine is necessary for efficiency; breaking routine is necessary for adaptation.
There is another reason, however, why traders don't quickly embrace change in the face of changing markets and opportunities. Even when we possess a distinct and consistent edge in markets, the paths of our profitability can be highly variable. Over the long haul – 100 or more trades – an edge tends to be apparent, particularly if one is not engaging in high hit rate/high blowup strategies, such as the naked selling of options. Over the course of any series of, say, 10 or 20 trades, there are random series of winning and losing bets that can play havoc with our psyches.
Last year I wrote a blog post based on the P/L Forecaster that Henry Carstens posted to his Vertical Solutions site. In researching that post, I explored three profitability curves: one with no edge whatsoever (50 percent win rate; average win size equal to average loss size); one with a negative edge (50 percent win rate; average win size 90 percent of average loss size); and one with a positive edge (50 percent win rate; average win size 110 percent of average loss size). Over the course of 100 trades, we could see the edge – or lack of edge – play out. Along the way, however, were surprising ups and downs that were purely random. By running Henry's Forecaster many times, we can see how many ways it's possible to have a constant edge and end up at a relatively constant end point, but with extremely different paths along the way.
Traders very often overinterpret these random ups and downs in the P/L curve. When they have strings of winning trades, they convince themselves that they are seeing markets well and increase their risk taking. When they encounter a series of losing trades, they become concerned about slumps and reduce their risk taking. Those adjustments ultimately cost the trader money. Imagine a baseball player who gauged his performance every 20 or so at-bats. When he gets a large number of hits, he considers himself to have a hot hand and swings even harder at pitches. When he strikes out a number of times, he talks himself into changing his swing to get out of his slump. Both adjustments take the batter out of his game. Ignoring short-term outcomes and focusing on the consistency of the swing is a far more promising approach to batting performance.
So it is for traders. Someone like Maxwell is wise to not overinterpret daily, weekly, or monthly P/L. Rather, he should assess the elements of his trading process, from his generation of trade ideas to his trade execution, and seek to make incremental improvements. When the paradigm is working, the most constructive course of action is to steadily refine the paradigm.
The problem is that, once in a while, 20 trades turn to 40 turn to 60, 80, and 100, and evidence accumulates that the trading paradigm is no longer viable. Even a small edge is apparent after enough instances: That's why it makes sense to keep betting in Vegas when the odds are with you. If you go all in on any single bet, you court risk of ruin: That randomness of the path can take you out of the game. But if you bet moderately with a constant edge, more bets allow the edge to overcome randomness. When randomness overwhelms an edge not just over a dozen or so trades, but over a great number of them, then we have evidence that something has changed. Still, a trader like Maxwell can convince himself that this, too, shall pass.
Tracking your edge is relatively easy when you place several trades per day. What about less active investors and portfolio managers who might limit themselves to several trades per week or month? If trade frequency is low, an entire year of diminished performance could go by and represent nothing more than random bad luck in performance. Imagine, then, the trading firm that allocates more capital to the portfolio manager who has a good year and not to the one who underperforms. Those adjustments, no less than the hot hand/slump-inspired adjustments of individual traders, can drain performance over time.
It's a genuine challenge to track edge and randomness over small sample sizes of trades. If a strategy can be backtested objectively without overfitting historical data, it is possible to generate a reasonable set of performance expectations in the absence of recent, real-time trading. For a purely discretionary strategy, however, the sobering truth is that, over the course of a limited number of trades, we cannot really know whether performance is due to luck or skill. Michael Mauboussin writes convincingly about this challenge in his book, The Success Equation (2012), pointing out that our failure
