Now consider the period after the system was published. (Unfortunately, the original series of utility stocks was discontinued. We replaced it with the Philadelphia Electrical utility average.) This period — Dec. 31, 1997 to April 11, 2013 — is shown in “Rougher waters” (below). The curve is not as flat and wide as the in-sample optimization period, but we still have a broad area of profitable performance. Parameters from four to 14 for T-bonds and 14 to 22 for the utility stocks produce between $100,000 and $150,000 over this period. The area of $50,000 to $100,000 (or more) profit represents a large part of this curve.
Based on this study, we decide to take a closer look at a system that uses an eight-period moving average for T-bonds and an 18-period moving average for the utility stock index. Deducting $75 for slippage and commission, we make $79,275 on 57% profitable trades from Sept. 22, 1987 through Dec. 31, 1997. Our average winning trade is $2,017, and our average losing trade is $554. Our profit factor was 1.92. Out-of-sample results are represented by the period following when the system was published. From Dec. 31, 1997 to April 11, 2013, the system made $122,331 on 60% winning trades. The average winner was $1,913, while the average loser was $1,882. The profit factor over this period was 1.56.
Now that you have seen that this system has done well over time, you should ask about the premise. Utility stock prices are based on perceived future borrowing interest costs. Normally, this logic is sound, but during deregulation of California power, this relationship had problems. This system’s only two consecutive losing years were 1999 and 2000, during the California energy crisis.
Intermarket analysis is a powerful tool, but a deep understanding of the markets is required to ensure that you accurately can spot what will and will not work. This becomes especially critical when correlations are broken by other forces. Auto-detection of these times is a key area of research for intermarket-based trading systems.
It’s not easy answering these two simple questions. Is a system reliable? Can I trade it and make money? Often, these two questions lead to more questions than answers. In any case, a key for both system traders and discretionary traders is to know yourself. If you don’t understand your own risk tolerances and your hunger for profit, you don’t know if you’re capable of following any given system. Understand yourself, understand the premises of your trading system and use the data analysis tools covered in this article, and you’ll be well on your way to long-term profitability in the markets.
Murray A. Ruggiero Jr. is the author of “Cybernetic Trading Strategies” (Wiley). E-mail him at firstname.lastname@example.org.