Commodity seasonality: driving season, heating season, harvests and why patterns fail

Commodity seasonality is the tendency of some commodity prices, spreads or stock levels to follow an annual rhythm driven by the physical calendar: summer driving and winter heating for energy, planting and harvest for crops, and weather risk at particular times of year. The rhythm is real in the physical market. Whether it produces a reliable price pattern you can trade is a different and much weaker claim, because the market knows the calendar too and prices much of it in advance.

This guide explains where seasonality comes from in the main commodity groups, how it shows up in the futures curve rather than only in the spot chart, and why seasonal charts are less reliable than they look. It assumes you know the basics in how commodity futures work.

Where seasonality comes from

Commodities are physical goods with production and consumption cycles. Three forces create most of the seasonal patterns:

• Demand that depends on the weather or the calendar. People drive more in summer and heat their homes in winter.
• Supply that depends on the growing season. Crops are harvested once or twice a year, so supply arrives in a lump and has to be stored until it is used.
• Infrastructure cycles. Refineries schedule maintenance in quieter months; pipelines and ports have seasonal constraints; hurricanes and frosts strike at particular times of year.

Storage links these together. When supply and demand are out of step during the year, stocks absorb the difference, and the cost of storing a commodity from one season to the next shapes the prices of futures contracts for different months.

Energy

Petrol and the US driving season

US petrol (gasoline) demand tends to be highest in the summer, often described as running from Memorial Day at the end of May to Labor Day at the start of September. Refiners build petrol stocks in spring ahead of it. The US also requires a summer-grade fuel blend with lower volatility in the warmer months, and the switch between grades affects the pricing of petrol futures around spring.

The common seasonal story is that petrol and refining margins firm up in spring as the market prepares for summer. Sometimes that happens; sometimes high stocks, weak demand or a broader sell-off in crude swamp it.

Heating oil, diesel and winter

Distillates, which include heating oil and diesel, see extra demand in the Northern Hemisphere winter, especially in the US Northeast and Europe. Stocks are typically built in autumn. A cold snap with low distillate stocks can lift prices quickly; a mild winter with ample stocks can do the opposite.

Refinery maintenance

Refiners tend to schedule planned maintenance, known as turnarounds, in spring and autumn, between the peak demand seasons. During turnarounds refiners buy less crude, so crude stocks can build while product stocks draw. Those effects show up in the weekly figures discussed in the guide to EIA and API inventory reports, and they are one reason a crude build in April or October is often read calmly.

Natural gas

Natural gas has the clearest seasonal shape of the major energy markets. In the US, stocks are injected into storage roughly from April to October and withdrawn roughly from November to March. Winter weather forecasts drive much of the volatility. The natural gas guide covers this in detail, including why winter contracts usually trade at a premium to summer ones.

Hurricane season

The Atlantic hurricane season runs from 1 June to 30 November. Storms in the Gulf of Mexico can shut offshore production, refineries and export terminals along the US Gulf Coast. That makes late summer and early autumn a period when the energy market prices some weather risk, though any given season may pass with little disruption.

Agriculture

Grains and oilseeds

For Northern Hemisphere crops such as US corn and soybeans, the cycle runs roughly: planting in spring, a growing season through summer when weather risk is highest, and harvest in late summer and autumn. Winter wheat is planted in autumn and harvested the following summer.

Two textbook ideas come from this cycle:

• Weather premium. Uncertainty about the crop is greatest during the growing season, so prices can carry a premium that fades if the weather is benign.
• Harvest pressure. When a crop comes in, supply is at its largest and storage is filling, which can weigh on prices around harvest.

The Southern Hemisphere complicates the picture. Brazil and Argentina harvest large soybean and corn crops at other times of year, so the global supply calendar is more spread out than the US one alone. The guide to wheat, corn and grains goes further into crop reports and their timing.

Soft commodities

Coffee, cocoa and sugar have their own calendars:

• Coffee: Brazil's harvest falls broadly in the middle of the year, and the Brazilian winter brings frost risk to coffee areas. A severe frost in July 2021 is a well-known example of weather overriding everything else.
• Cocoa: in West Africa, the main crop season begins around October, with a smaller mid-crop later in the season. Weather and crop disease during the growing months drive supply expectations.
• Sugar: Brazil's centre-south harvest runs through the middle of the year, and mills there choose how much cane to turn into sugar and how much into ethanol.

The soft commodities guide covers these markets and how they behave as CFDs.

Metals

Industrial and precious metals have weaker seasonal patterns because they are not grown and can be stored for decades. Some analysts point to seasonal physical demand for gold around Indian festivals and the wedding season, or around Chinese New Year, and to construction seasons for industrial metals in China. These effects are small relative to the forces that usually drive metals: interest rates, the dollar, growth expectations and investment flows. The guides to gold and silver and copper cover those drivers.

Seasonality lives in the futures curve

This is the most important practical point, and the one most seasonal charts leave out.

If everyone knows heating oil demand rises in winter, the price of a futures contract for January delivery already reflects that expectation. The market does not wait for January to arrive. So the seasonal pattern shows up mainly as differences between contract months on the futures curve, not as a predictable rise in the price of any one contract.

For natural gas, winter contracts usually trade above summer ones. For petrol, spring and summer contracts have tended to price differently from winter ones. The glossary entry on contango explains how upward-sloping curves work.

What this means for CFD traders

Many commodity CFDs are based on a futures contract and roll to the next contract before expiry. When the next contract is priced differently, the provider adjusts your position or the price to account for the gap. If you are long and the next contract is more expensive, you effectively pay that difference over time, even though the chart of the rolled price may look smooth.

Worked example: a seasonal roll

Illustrative numbers only. You are long a natural gas CFD that tracks the October futures contract at 3.00. The provider rolls to the November contract, which is trading at 3.30 because it covers the start of the heating season.

• At the roll the CFD's reference price moves from 3.00 to 3.30.
• The provider applies an adjustment so that your open profit or loss is unchanged by the switch: in effect your entry level is raised by 0.30 for a long position, or a cash adjustment is booked to your account.
• If the November contract then drifts down towards 3.10 by the time it becomes the front month, a long holder loses 0.20 from 3.30, even though the spot-like chart may show winter prices higher than October's.

So a trader who buys in October because a seasonal chart shows prices higher in winter can be right about the pattern on the chart and still lose money, because the curve had already priced the winter premium. The rules differ between providers; the guide to CFD rollover and expiry explains the mechanics.

Continuous charts that splice together different contracts can also create seasonal-looking patterns that are partly artefacts of the splicing. Always check how a seasonal chart was built.

Why seasonal patterns are unreliable

Small samples

A seasonal chart that averages twenty years of data has twenty observations for each month. That is not much evidence. Consider a pattern that has been positive in 14 of the last 20 years.

Illustrative calculation: if each year were a pure coin toss with a 50% chance of rising, the probability of seeing 14 or more up years out of 20 is about 5.8%. Unusual, but not rare.

Now suppose an analyst scans 10 commodities across 12 calendar months, which is 120 separate tests. Even if no seasonal effect existed anywhere, you would expect about 120 × 5.8%, or roughly 7, of those month-and-commodity combinations to look as strong as 14 out of 20 purely by chance. A seasonal chart that someone found by searching is far less impressive than one they predicted before looking. This is the same overfitting problem described in how to backtest a strategy.

Averages hide the spread

A seasonal average can be dominated by one or two extreme years. An average rise of 4% in a given month could come from many small gains, or from a single year with a 30% spike and a string of small losses. Before taking any seasonal statistic seriously, look at the year-by-year results: the median, the worst year and the number of losing years.

Illustrative example: average versus reality

Suppose a seasonal study of a commodity over ten years shows these returns for one month, as illustrative figures: +22%, +3%, +2%, +1%, −1%, −2%, −3%, −4%, +6%, +11%.

• Average: (22 + 3 + 2 + 1 − 1 − 2 − 3 − 4 + 6 + 11) ÷ 10 = 35 ÷ 10 = 3.5%.
• Up years: 6 out of 10.
• Median: about +1.5% (the middle two values, +1% and +2%).
• Without the single +22% year, the average of the other nine is 13 ÷ 9, or about 1.4%.

The headline of 3.5% a year looks like an edge. The year-by-year detail shows a pattern that depends on one outlier and loses in four years out of ten.

The market prices what it knows

Seasonal patterns that are widely known tend to be traded in advance, which spreads the effect out or moves it earlier, until it becomes hard to see. That is what market efficiency predicts for public, simple information, and it is why the physical calendar shows up in the curve rather than as a free pattern in spot prices.

Structural change

Seasonal patterns are built on the market as it used to be. Things change:

• The growth of US shale oil changed US production and trade flows.
• US exports of liquefied natural gas linked US gas more closely to world demand.
• Biofuel mandates tied corn and sugar more closely to energy prices.
• Climate patterns are shifting, and growing regions change over time.

A twenty-year seasonal average can mix several quite different market structures.

Weather and shocks override the calendar

A mild winter, a hurricane, a war, a pandemic or a policy decision by OPEC+ can overwhelm any seasonal tendency in a given year. The 2022 European gas crisis and the 2020 collapse in oil demand had nothing to do with the seasonal calendar. A seasonal tendency describes an average year, and you never trade an average year.

Using seasonality sensibly

Seasonality is useful, but mainly as context rather than as a signal. Some sensible uses:

• Knowing what the market is watching. In June, US weather forecasts for the corn belt matter; in December, heating-degree days and gas storage matter. Knowing the season tells you which reports and forecasts are likely to move prices. The economic calendar guide applies to commodity reports too.
• Reading inventories properly. A stock draw is only meaningful relative to what normally happens in that week of the year, which is why analysts compare stocks with a five-year seasonal range.
• Understanding the curve. Seasonality explains why some contract months are priced above others and what a roll may cost.
• Anticipating volatility. Weather-sensitive months tend to be more volatile in weather-sensitive markets, which matters for position size and stop distance.

What it should not do is replace a plan with a defined risk. If you take a trade partly for seasonal reasons, size it the same way as any other trade, with the instruments position size calculator, and accept that the seasonal story might simply not apply this year.

Worked example: sizing a seasonally motivated trade

Illustrative numbers. Account 8,000 euros, risk 1% per trade, so 80 euros. A heating oil CFD has a point value of 4 euros per 0.01 move per contract (that is, 400 euros per 1.00), minimum size 0.1 contracts. Winter months are more volatile, so you use a wider stop of 0.15 rather than 0.10.

• Risk per contract: 0.15 × 400 = 60 euros.
• Size: 80 ÷ 60 = 1.33, rounded down to 1.3 contracts.
• With a 0.10 stop the size would have been 80 ÷ 40 = 2 contracts.

Wider stops for more volatile seasons mean smaller positions for the same cash risk. The seasonal argument changes nothing about how much you can afford to lose. Point values vary widely between providers, so check the contract specification before you trade.

Common mistakes

• Treating a seasonal chart as a forecast. It summarises the past, often with very few observations.
• Ignoring the curve. Buying a CFD because winter prices are higher on a seasonal chart, without checking how much the winter contract already costs.
• Trusting averages without the year-by-year data. One outlier can create a pattern that does not exist in most years.
• Data mining. Scanning many markets and months will always produce some impressive-looking patterns by chance.
• Forgetting structural change. Patterns from a different market era may not apply now.
• Overriding risk rules for a seasonal idea. Seasonality does not reduce risk; weather-driven months often increase it.

Most retail CFD accounts lose money, and commodity CFDs carry the extra risks of gaps, rolls and limited trading hours. The risk warning has more.

Frequently asked questions

What is commodity seasonality?

It is the tendency of some commodity prices, spreads or inventories to follow an annual rhythm linked to the physical calendar, such as summer driving demand for petrol, winter heating demand for natural gas and heating oil, and harvest times for crops. It is a description of past tendencies, not a rule about future prices.

Is there a seasonal pattern in oil prices?

There are clear seasonal patterns in the physical oil market: refinery maintenance in spring and autumn, petrol demand in the US summer, distillate demand in winter and hurricane risk from June to November. Crude oil prices themselves have a much weaker and less reliable seasonal pattern, because crude is affected by many larger forces and the curve prices in known seasonal effects.

Why do winter natural gas contracts cost more than summer ones?

Because demand for gas is highest in winter and storage is limited, gas delivered in winter is more valuable than gas delivered in summer. The futures curve reflects that difference in advance. Holders of rolling gas CFDs can pay for this difference at each roll.

Are seasonal trading strategies reliable?

Not on their own. Seasonal statistics usually rest on few observations, can be dominated by one or two extreme years, and are easy to find by chance when many markets and months are scanned. Widely known patterns also tend to be priced in. Many traders use seasonality as background rather than as a signal.

How does the harvest affect grain prices?

Around harvest, new supply arrives and storage fills, which can weigh on prices, while the growing season before it carries the most weather uncertainty. That is the textbook pattern, but large Southern Hemisphere crops, export demand, stocks carried over from previous years and the weather itself often matter more in a particular year.