- 1998: Volume 7, No. 2 Computed Contracts: Their Meaning, Purpose and Application

By Bob Pelletier

It is an unending study of an ever-changing subject. It is a quest that takes commodity traders and technicians deep into the history of the markets, brings them rushing back to the present and hurls them pensively into the future. Technical analysis is indeed an exciting, sometimes grueling, business; one which leads its practitioners to tackle large quantities of historical data for individual commodities. Speculators demand a workable way to view the markets that simulates the perils, profits and pitfalls of actual trading. Those in the know are finding that the most meaningful results can be found in the study of "computed" contracts, which are derived from, but do not exactly mirror actual market activity. This is a discussion of the various types of computed contracts available to CSI data resource subscribers.

Let's start with the basic fact that futures contracts are relatively short lived. They are created on some date by traders on some exchange floor and eventually die when their delivery dates are reached. This birth-death process for commodity and futures contracts is an inherent characteristic that cannot be ignored. Some commodity contracts have longer lives than others. Grain contracts, for example, trade for a year or two, while financial markets may be traded six to 10 years into the future. In all markets, nearby contracts (those about to expire) enjoy much heavier volume and open interest than contracts with later expiration dates. Technical traders are wary of entering illiquid markets, where order execution slippage can take a significant toll on both actual profits and efficient order execution. Liquidity factors relating to open interest and volume, life span and distance from expiration are all important considerations.

Nearest Future Contracts

Traders of the 1950s and before were comfortable viewing a concatenation of contracts of the same commodity over time. These were created by manually splicing together the nearest portion of successive delivery months into a series covering 10, 20 or even 50 years. They could then simulate the practice of trading and viewing the more active (and most liquid) period of each successive contract to obtain a feel for trends, volatility and opportunity for profit. Many traders still prefer viewing the markets from a nearest-contract perspective. An advantage to this approach is that the most heavily traded portion of every contract viewed in the concatenated series is a representation of the actual market prices. A major disadvantage is that significant price jumps or drops (discontinuities) occur from one contract to the next which help to discredit, distort and diminish results.

Gann Contracts

Gann enthusiasts represent another trading group that is interested in simulating markets over a wider spectrum of contract history. This group views the markets similarly to the nearest future proponents, with the exception that like contracts (those with the same delivery month classification) of a commodity are concatenated. For example, a Gann time series might hold the final year of the June 1987 contract, followed by the final year of the June 1988 contract, followed by successive June contracts up to and including the most current June contract that lies within 12 months of its expiration date.

The Gann approach may be better than the nearest-future variety because there are fewer discontinuities. On the other hand, the one-year segments of a "Gann file" may be too long to yield meaningful information. What may have been learned from the distant (early) portion of each one-year segment of the time series may not readily apply to the more volatile later portion of each successive one-year series. As a contract approaches maturity, its characteristics such as volatility and trading volume gradually increase until a maximum level is reached near the end of each delivery month's contribution to the overall series. Unfortunately, the later period of each contract is likely, in a statistical sense, to show no resemblance to the relatively tame earlier period. This phenomenon suggests a lack of stationarity, a statistical property explained in the Perpetual Contract® data discussion below.

Perpetual Contract Data

In 1970, when the computer became more popular for analysis, CSI unveiled its trademarked Perpetual Contract data. This computed contract represented a time-weighted average price of the two active contracts that lie earlier and later than a fixed number of days and months ahead of the then-current date. This method of calculation remains popular because it provides an accurate view of the market's characteristic wave form over time that is "perpetual" in nature. It is similar to the forward contracts offered by the London Metal Exchange. The major drawbacks of the Perpetual Contract data approach is that the contracts cannot be traded directly, and can only be used as a guide for overall market direction. They are used to assist in examining long-term analysis alternatives. They should not be heavily relied upon in examining agricultural markets where different supply-and-demand conditions may affect the distinct old and new crops. An alternative to the standard Perpetual Contract data is the open interest-weighted Perpetual Contract which has a near-contract view that results from all contract prices being weighted by their respective open interest.

Advocates of Perpetual Contract data series point out that these series are more likely to exhibit statistical stationarity than, say, a Gann contract. This is particularly true when there is a long enough period from birth to death to change the contract's volatility over time. The concept of "stationarity" is simple to understand. For a serially correlated time series to be stationary (and most time series are serially correlated), the mean and variance of the series must remain statistically constant. Another significant advantage of Perpetual Contract data is that it offers flexibility to focus on near or far contracts as a single independent series for analysis purposes. For example, an analyst could pair off far-forward future hogs against nearby corn (the raw material needed to produce the hogs) to study the dependent impact of these two commodities on each other.

Back-and Forward-Adjusted Contracts

More recently, traders have shown an interest in back-and forward-adjusted contracts. Back-adjusted contracts use the actual prices of the most recent contract with a backward correction of price discontinuities for successive earlier active delivery months. In a forward-adjusted contract, the prices of the current contract are changed to eliminate the gap between the current and recently expired contract. An important aspect to remember about forward-adjusted contracts is that current prices do not represent actual values for today's markets. Because of the removal of contract-to-contract price jumps and drops in both back- and forward-adjusted contracts, they appear as smooth, blended, homogeneous price histories representing a sorted and concatenated compilation of successive contracts over time.

This method of joining contracts in a series over a period of years or decades permits the analyst to focus on the period when one might prefer to trade the markets in actual practice.

Negative Values in Back-and Forward-Adjusted Series

An advantage of the back-adjusted approach to long-term market synthesis and simulation is that the data observed is precisely the same as the exchange's representation of the final contract in the concatenated series. A flaw in back-adjusting is the strong chance that an inflation-sensitive market could produce negative price quantities into the past. The same logic allows forward-adjusted contracts in a deflationary environment to produce negative current prices for today. The suggestion that prices can be negative in actuality is clearly flawed and could discredit the accuracy of such a methodology for longer term analysis. No one would really pay you to take 50 bars of gold away or pay you to take thousands of pounds of cotton. This flaw demonstrates that a bias is introduced through the removal of contract-to-contract price discontinuities.

When early contract prices in a concatenated set are significantly less than their real contract counterparts, they tend to produce a bias that in simulated trading would heavily favor the act of buying over selling. In addition, even if the early contract prices are not significantly different from their current-contract counterparts, inflation could play a role in influencing buying over selling when such a long series is introduced as representative of current pricing norms. This phenomenon should tell you that your results may be invalid and that applying in the present what you have learned by simulating the past can distort your trading algorithm. Fortunately, there is a way this bias can be removed without compromising the validity of your simulation.

Deciding Which Computed Approach to Follow

There are many considerations in choosing computed contracts for analysis and, eventually, for impacting investment decisions. Each category has some unique value. Both the nearest futures contract and Perpetual Contract data can view the markets from early and distant delivery perspectives by focusing upon contracts that expire either early or late with respect to any given current date. The Perpetual Contract is the only viable approach that can focus upon a fixed period forward in time and therefore achieve a substantial level of statistical stationarity.

From an astrological perspective, perhaps only the Gann computation is valid. It seems to have the advantage of offering a predictably long period of time to view a market on an annualized basis and may have some longevity benefits not possible with nearest-future contracts. Nearest-future contracts have the advantage of focusing upon the most liquid period of a contract's life, but the disadvantage of offering very brief periods of individual contract data.

The back-adjusted contract offers the most flexibility for the user. Current data can be supplied as it was actually traded in exchange-released form and past data can be expressed in adjusted and detrended form. The mechanical effects of back-adjusting and price inflation can be removed, making the detrended series an excellent source of information for seasonal analysis. A minor disadvantage to the back- or forward-adjusted contract is the heavy computing requirements necessary to produce the resulting series. Total computing time is measured in seconds rather than microseconds, making it necessary to wait for results.

This message is presented to guide you in your study of the commodity markets and to help you understand the ever-changing subject at hand. It is not only for those who are contemplating building trading systems based on computed contract series, but also for those whose trading systems have been derived from such approaches. Each type of computed contract discussed here can add some visibility to market analysis. It is important to consider both the strengths and possible weaknesses inherent in these methods to maximize profits and preserve capital in actual trading.

Bob Pelletier is the president of CSI, a data retrieval service offering daily updates and end-of-day historical data on commodities, stocks, options, indexes and mutual funds. CSI also provides software to assist traders with data retrieval and maintenance, graphic analysis, accounting and trading system evaluation. For information on CSI products and services, call (561) 392-8663 or e-mail marketing@csidata.com.