In Chapter Two, we discussed the flow that can be experienced when we own the zone. We examined the nature of an edge and defined it as a collection of synergistic advantages.
In this chapter, I will do a case study of how one trader pulled it all together: me.
The Background
First, some reference material:
- 2007.05.31: 20-minute discussion with Tim Bourquin at TraderInterviews.com
- 2007.05.31: Evolution of a Trader and Portfolio
- March/April 2005: Excellent article about Mark Douglas’ Path to Consistency workshop, written by attendee Gary Stone
- 2007.05.23: Mauboussin on Strategy: Turtles in Omaha
- Peter L. Bernstein: The Failure of Invariance
After 20 years of discretionary trading, I found myself constrained by its limitations. Even with my experience and photograhic memory, I have but only two eyes, and it was simply not possible for me to watch and trade more than a handful of symbols.
Since 1996, my solution had been to trade stock index futures in the morning, but the bulk of my money was sitting there, doing nothing. OK, maybe nothing is a bit of an exaggeration since there is only one price for risk. Still, I felt there was more to life in the capital markets than just the futures/cash combo.
In addition to potentially increasing returns, there were other motivators such as:
- Eliminating unforced errors that come with being a human trader such as making trades on an impulse, taking profits too soon and taking larger losses than necessary;
- Spending time enjoying life rather than look at more charts outside market hours;
- Reducing stress by not having to get up at 5AM Pacific for the rest of my life;
- Occasional free lunches from diversification and rebalancing; and,
- Clients would be better served by a mechanical trading system.
The Mission
The decision was made to automate the trading and incorporate quantitative methods to build portfolios and manage risk.
The Signals
The first thing traders think about are signals: when to buy and when to sell. It was a good place for me to start since I knew a lot about this subject. But how would I go about coding it all up? It was time for a phone call.
The best problems, like the best toys, are hard to exhaust. You can approach them from a variety of different angles, each new angle making the problem fresh again, and bringing the opportunity to discover something new. Any idea, no matter how crazy seeming, might work and can be worth exploring. Indeed, the harder the problem, the more degrees of freedom one can allow in tackling it. Fischer relished hard problems because he relished that freedom, but in practice he did not try just anything. In his view, if a problem does not yield to known methods, that doesn’t mean we need more sophisticated methods, indeed probably just the opposite. Usually problems are hard not because our technique is deficient but because our understanding is deficient. — Fischer Black and the Revolutionary Idea of Finance
I explained my predicament to The Quant. After much discussion, we agreed on one thing: avoid making forecasts. That is what everyone else tries to do while little attention is paid to crafting a betting strategy or determining which game was most advantageous to play.
In quant language, I would use a probabalistic modelling approach rather than a deterministic one.
A little luck never hurts, but it’s rare for anyone to attribute their success to it, especially anyone on Wall Street, where appearance seems sometimes to matter as much as performance.
Then there’s James Simons, president and founder of Renaissance Technologies, the hedge fund with the best track record of the past decade (up an average 35.6 percent annually since 1989 and 64 percent so far this year). If anything, Simons can’t seem to say enough about luck. That’s in part a reflection of his unusual background. A brilliant mathematician, Simons won the Veblen Prize for geometry in 1976 before tiring of academia and taking up trading full time. Luck, for him, is a matter of statistical probabilities; trading success comes from scrutinizing data to create models that narrow the range of outcomes. — Institutional Investor, Dailyii.com (by subscription)
Our thinking was more in line with Douglas’ Five Fundamental Truths, that my goals would be better served if I put my efforts into identifying a point in time when, in all probability, the prevailing price move was over. Chalk one up for the (Quant) mathematician.
Initially, I thought mechanical trading would present unacceptable tradeoffs because I perceived discretionary trading to be more precise; however, research revealed and proved that proper handling of trade logistics — stuff that is difficult for humans to calculate and manage on-the-fly in real-time — is the real secret of success.
It turns out that pinpoint accuracy is required only for highly leveraged intraday scalping such as the volatile first hour of trading stock index futures. In addition, I confirmed for myself that many practices that lead to profitable trading and investing are counterintuitive.
Anyone with average intelligence can learn to trade. This is not rocket science. However, it’s much easier to learn what you should do in trading than to do it. Good systems tend to violate normal human tendencies. Of the people who can learn the basics, only a small percentage will be successful traders. …
Decision theorists have performed experiments in which people are given various choices between sure things (amounts of money) and simple lotteries in order to see if the subjects’ preferences are rationally ordered. They find that people will generally choose a sure gain over a lottery with a higher expected gain but that they will shun a sure loss in favor of an even worse lottery (as long as the lottery gives them a chance of coming out ahead). These evidently instinctive human tendencies spell doom for the trader - take your profits, but play with your losses. — William Eckhardt interviewed by Jack Schwager, The New Market Wizards: Conversations with America’s Top Traders
System Design
To repeat, the goal was to identify reversal points.
Many systematic traders spend the majority of their time searching for good places to initiate. It just seems to be part of human nature to focus on the most hopeful point of the trading cycle. Our research indicated that liquidations are vastly more important than initiations. If you initiate purely randomly, you do surprisingly well with a good liquidation criterion. In contrast, random liquidations will kill the best system. At ETC we expend a lot of our research effort on liquidations.
Most standard statistical techniques are inappropriate for analyzing trading. Statisticians have developed many delicate techniques that squeeze information from minimal data, but these give false results in this business. I tell traders that if the results don’t sock you in the eye, they’re probably not real. Accordingly, we use only the most robust and assumption free statistical tests, we have an aversion to summary statistics that obliterate important structural elements. For assessing systems, we use a technique called bootstrapping so that the complete distribution of past outcomes can make itself felt in decisions; the distribution is not simply viewed in terms of its mean and variance which can give a distorted picture.
Our aversion to summary statistics that obliterate structure extends to the trading systems themselves. For instance, we avoid moving averages of price in making trades. Such moving averages are popular mostly because they’re mathematically tractable, but they smooth away all the structural information inherent in the price data. — William Eckhardt, 1996
In theory, if we know the mechanism behind price changes, we can predict them. But we do not, at least not me. Therefore, no attempt was made to forecast the magnitude and duration of any directional move.
As an aside, we can contrast this to pattern-based technical trading. My view is that price patterns are manifestations of the investor sentiment cycle, most eloquently summed up by Justin Mamis in his book, The Nature of Risk.
In order to trade profitably with this discretionary approach, one must be a keen observer, someone who can relate and compare current market conditions to historical accounts. In order to be an impartial judge, one must maintain independence of thought and possess a certain cynicism about human nature.
One cannot be a permabull or a permabear because this job entails reviewing evidence while simultaneously holding two opposing ideas in one’s head at all times. We must be able to judge where we stand in the sentiment cycle at any given moment in time.
While rule-based trading of chart patterns not difficult — see The Ultimate Trading Course — quantifying them for mechanical trading is another matter.
Back to engineering. Once the algorithm for the reversal points was in place, it was time to make the system intuitive to use and esthetically pleasing to the eye.
How it works:
- Price bars are colored blue when the system is long. The pink dots below mark the price where the system will generate a sell signal.
- A CLOSE BELOW the pink dot triggers a sell signal as a downswing begins.
- Price bars are colored pink when the system is short. The blue dots above mark the price where the system will generate a buy signal.
- A CLOSE ABOVE the blue dot triggers a buy signal as an upswing begins.
With the basic signals in place, the real fun begins. In Chapter Four, we will examine the role of trade logistics, the oft-ignored, niggly stuff that can bankrupt traders faster than a market crash: position size (leverage, stop loss, pyramiding), trend-filtering and shorting policy.