Seasonality is a seasonal fluctuation or cycle forming a progression or trend. Nearly all markets are affected by various fundamental forces, many of which are seasonal in nature.  Such factors as weather, fiscal calendars, crop cycles, and specific characteristics of futures contracts themselves (such as delivery and expiration) tend to recur and influence, to one degree or another, certain markets every year.  As any market or spread relationship responds to a series of annually recurring factors, seasonal price patterns tend to evolve.

Many time series display seasonality. By seasonality, we mean periodic fluctuations. For example, retail sales tend to peak for the Christmas season and then decline after the holidays. So time series of retail sales will typically show increasing sales from September through December and declining sales in January and February. Seasonality is quite common in economic time series and less common in engineering and scientific data. 

Why Seasonals Work: The seasonal approach to markets is designed to anticipate future price movement rather than constantly react to an endless stream of often contradictory news. Although numerous factors affect the markets, certain conditions and events recur at annual intervals. Perhaps the most obvious is the annual cycle of weather from warm to cold and back to warm. However, the calendar also marks the annual passing of important events, such as the due date for U.S. income taxes every April 15th. Such annual events create yearly cycles in supply and demand. Enormous supplies of grain at harvest dwindle throughout the year. Demand for heating oil typically rises as cold weather approaches but subsides as inventory is filled. Monetary liquidity may decline as taxes are paid but rise as the Federal Reserve recirculates funds.  

Natural Market Rhythms: These annual cycles in supply and demand give rise to seasonal price phenomena - to greater or lesser degree and in more or less timely manner. An annual pattern of changing conditions, then, may cause a more or less well-defined annual pattern of price responses. Thus, seasonality may be defined as a market's natural rhythm, an established tendency for prices to move in the same direction at a similar time every year. As such, it becomes a valid principle subject to objective analysis in any market. 

In a market strongly influenced by annual cycles, seasonal price movement may become more than just an effect of seasonal cause. It can become so ingrained as to become nearly a fundamental condition in its own right --- almost as if the market had a memory of its own. Why? Once consumer and producers fall into a pattern, they tend to rely on it, almost to the point of becoming dependent on it. Vested interests then maintain it. "Pattern" implies a degree of predictability. Future prices move when anticipating change and adjust when that change is realized. When those changes are annual in nature, a recurring cycle of anticipation/realization evolves. This recurring phenomenon is intrinsic to the seasonal approach in trading, for it is designed to anticipate, enter, and capture recurrent trends as they emerge and exit as they are realized.

The first step, of course, is to find a market's seasonal price pattern. In the past, weekly or monthly high and low prices were used to construct relatively crude studies. Such analysis might suggest, for instance, that cattle prices in April were higher than in March 67% of the time and higher than in May 80% of the time. Computers, however, can now derive a daily seasonal pattern of price behavior from a composite of daily price activity over several years. Properly constructed, such a pattern provides historical perspective on the market's annual price cycle.

Of course, market behavior does not always coincide with its seasonal pattern; markets are dynamic, patterns an evolutionary composite. Macro-economic forces ensure bull and bear trends, which may offer the greatest trading opportunities but which exhibit behavior diametrically opposed one to the other. Therefore, dissecting seasonal behavior into two even more distinct patterns, one bullish and one bearish, provides yet another perspective from which to discover emerging trends and to understand what behavior to anticipate - and when

Further behavioral study can examine both bullish and bearish market patterns. To be included in either composite, a contract year must meet a strict mathematical definition: Using linear regression the absolute value of the slope of the line best describing its closing-price scatterplot must exceed a predetermined level. Years with a neutral bias are reflected in a third series, the purpose being to isolate bullish and bearish patterns of behavior. The nature of these patterns themselves may then be further examined for such things as specific time periods critical to a breakout or trend definition. The goal of Bull/Bear patterns are to provide potential insight into when bull years may be strongest and bear years may be weakest, Additionally, potential periods where bull years are weak, or bear years are strong may also come to light.

Basic Pattern Dynamics: Consider the following seasonal pattern that evolved for January Heating Oil. Demand, and therefore prices, are typically low during July --- often the hottest month of the year. As the industry begins anticipating cooler weather, the market finds increasing demand for future inventory --- exerting upward pressure on prices. Finally, the rally in prices tends to climax even before the onset of the coldest weather as anticipated demand is realized, refineries gear up to meet that demand, and the market begins to focus instead on inventory liquidation.

The other primary petroleum product encounters a different, albeit still weather-driven, cycle of demand as exhibited in the seasonal pattern for August Gasoline. Prices tend to be lower during the poorer driving conditions of winter. However, as the industry begins to anticipate the summer driving season, demand for future inventory increases and exerts upward pressure on prices. By the official opening of the driving season (Memorial Day) refineries then have enough incentive to meet that demand.

Seasonal "Pegs": Seasonal patterns derived from daily prices rarely appear as perfect cycles. Even in patterns with distinct seasonal highs and lows, seasonal trends in between are sometimes subject to various, even conflicting forces before they are fully realized. A seasonal decline may typically be punctuated by brief rallies. For example, even though cattle prices have usually declined from March/April into June/July, they have exhibited a strong tendency to rally in early May as retail grocery outlets inventory beef for Memorial Day barbecues. Soybean prices tend to decline from June/July into October's harvest, but by Labor Day the market has usually anticipated a frost scare.

Conversely, a seasonal rally may typically be punctuated by brief dips. For example, uptrends are regularly interrupted by bouts of artificial selling pressure associated with First Notice Day for nearby contracts. Such liquidation to avoid delivery can offer opportunities to take profits and/or to enter or reestablish positions.

Therefore, a seasonal pattern constructed from daily prices can depict not only the four major components of seasonal price movement, but also especially reliable segments of larger seasonal trends. Recognizing fundamental events that coincide with these punctuations can provide even greater confidence in the pattern.

Inherent Strengths / Weaknesses: Such trading patterns do not repeat without fail, of course. The seasonal methodology, as does any other, has it own inherent limitations. Of immediate practical concern to traders may be issues of timing and contraseasonal price movement. Fundamentals, both daily and longer term, inevitably ebb and flow. For instance, some summers are hotter and dryer - and at more critical times - than others. Even trends of exceptional seasonal consistency are best traded with common sense, a simple technical indicator, and/or a basic familiarity with current fundamentals to enhance selectivity and timing of entry/exit. How large must a valid statistical sample be? Generally, more is better. For some uses, however, "modern" history may be more practical. For example, Brazil's ascent as a major soybean producer in 1980 was a major factor in the nearly 180-degree reversal in that market's trading pattern from the 1970s. Conversely, relying solely on disinflationary patterns prevalent in 1981-1999 could be detrimental in a new inflationary environment.

During such historic transitions in underlying fundamentals, trading patterns will evolve. Analyzing cash markets can perhaps help neutralize such effects, but certain patterns specific to futures (such as those that are delivery- or expiration-driven) can get lost in translation. Thus, both sample size and the sample itself must be appropriate for its intended use. These may be determined arbitrarily, but best by a user fully cognizant of the consequences of that choice. 

Related issues involve projecting into the future with statistics, which confirm the past but do not predict in and of themselves. The Super-Bowl winner/stock-market direction "phenomenon" is an example of statistical coincidence because there exists no cause-and-effect relationship. However, it does raise a valid issue: When computers mechanically sift only raw data, what discoveries are relevant? Does the simple, isolated fact that a pattern has repeated in 14 out of the last 15 years make it necessarily valid?

Certainly, patterns driven by known fundamentals inspire more confidence; but to know all relevant fundamentals in every market is impractical. Properly constructed seasonal patterns may typically help one find trends that have recurred in the same direction during the same period of time most years with a high degree of past reliability. Finding a "cluster" of such historically reliable trends, with similar entry and/or exit dates, not only reduces the odds of statistical aberration but also implies recurring fundamental conditions that presumably will exist again in the future and affect the market to one degree or another in a more or less timely manner.

A seasonal pattern merely depicts the well-worn path a market itself has tended to follow. It is a market's own consistency which provides the foundation for why seasonals work (Credits: &