How Is Market Seasonality Calculated?

Introduction

Seasonality is one of the most widely used concepts in commodity and financial market analysis. Traders frequently encounter seasonal charts showing recurring patterns in markets such as corn, wheat, natural gas, gold, crude oil or stock indices.

However, one important question is often overlooked:

How is seasonality actually calculated?

The answer is more complex than it may first appear. There is no single universally accepted method for calculating market seasonality. Different analysts, research platforms and software vendors may use different approaches depending on the objective of the analysis.

As a result, two seasonal charts for the same market can look different even if they are based on broadly similar historical data.

This article explains the most common calculation methods, their strengths and limitations, and the transparent research approach I use within the COT-Trader framework.

What Is Market Seasonality?

Market seasonality describes recurring price tendencies that appear during specific periods of the year. These tendencies may be linked to production cycles, demand patterns, weather, storage, tax effects, institutional flows or broader calendar effects.

Examples include:

• corn, soybean and wheat markets reacting to planting and harvest cycles,
• natural gas responding to seasonal heating and cooling demand,
• precious metals showing recurring strength or weakness during certain calendar periods,
• equity markets displaying well-known calendar effects such as year-end strength.

Seasonality does not predict the future. It describes how a market has behaved historically during similar calendar periods.

The objective is to identify tendencies, not certainties.

Why Different Seasonal Charts Can Look Different

Many traders assume that seasonality is calculated in the same way everywhere. In reality, every seasonal chart is the result of several methodological decisions.

Important questions include:

• Which historical period is used?
• Are prices or percentage returns analyzed?
• Is the data normalized?
• Are averages or medians used?
• How are outliers handled?
• Are seasonal curves or specific trading windows evaluated?
• Are transaction costs, roll effects or drawdowns included?

Different answers to these questions can produce different results. This does not necessarily mean that one chart is right and another is wrong. More often, the charts are simply answering different research questions.

Method 1: Average Historical Returns

The simplest approach calculates the average return for a specific period.

The average return would be:

(+4.0 + 2.0 - 1.0 + 3.0) / 4 = +2.0%

Advantages

• easy to calculate,
• easy to understand,
• useful for quick comparisons.

Limitations

• highly sensitive to outliers,
• exceptional years can distort the result,
• provides limited information about risk, drawdown or tradability.

Method 2: Cumulative Seasonal Curves

This is one of the most common approaches used for visual seasonal charts.

The process typically works as follows:

1. Calculate daily or weekly returns for each historical year.
2. Calculate the average return for each calendar day or week.
3. Cumulate those average returns over the calendar year.

The result is a continuous seasonal curve showing the average historical path of the market throughout the year.

Advantages

• easy to visualize,
• useful for identifying recurring tendencies,
• helpful for education and market context.

Limitations

• can hide significant year-to-year variation,
• strong individual years may influence the curve,
• does not automatically identify tradable opportunities.

A visually attractive seasonal chart is not automatically a profitable trading strategy.

Method 3: Normalized Historical Years

Many seasonal studies normalize historical data before averaging. The goal is to make different years comparable regardless of their absolute price levels.

For example, corn trading at 2.00 USD in one decade and 6.00 USD in another decade should not be averaged directly as raw prices if the objective is to compare seasonal behavior.

A common solution is to convert each year to a common starting value, such as 100.

This allows historical years to be compared on a percentage basis rather than on an absolute price basis.

Advantages

• improves comparability across different price regimes,
• reduces distortions caused by changing price levels,
• especially useful for long historical datasets.

Limitations

• slightly more complex than simple averages,
• still depends on the chosen averaging method,
• does not solve all issues related to outliers or regime shifts.

Method 4: Median-Based Seasonality

Instead of using the average, some analysts use the median historical path or median return. The median is less sensitive to extreme outliers than the arithmetic average.

This can be useful when a small number of exceptional years would otherwise dominate the seasonal curve.

Advantages

• more robust against extreme years,
• can provide a cleaner view of typical behavior,
• useful when historical data contains major shocks.

Limitations

• may understate the impact of large but relevant market moves,
• less intuitive for some traders than average returns,
• still does not directly measure trade risk.

Method 5: Seasonal Window Analysis

Seasonal curves answer the question:

How does the market typically behave during the calendar year?

Seasonal window analysis asks a different question:

Which specific historical periods have produced measurable trading results?

Instead of only looking at the full annual curve, this method evaluates defined trading windows, such as March 15 to June 20.

This approach can include additional metrics such as:

• win rate,
• average gain,
• average loss,
• profit factor,
• maximum drawdown,
• sample size,
• risk-reward characteristics.

Advantages

• more directly applicable to trading,
• provides measurable statistics,
• can be used for systematic strategy development.

Limitations

• requires robust historical testing,
• results can vary depending on the selected period,
• over-optimization is a real risk if too many windows are tested.

There Is No Single Correct Method

A common misconception is that one calculation method is universally superior.

In reality, each method serves a different purpose.

The best method depends on the question being asked.

The COT-Trader Seasonal Research Approach

Transparency is a core principle behind COT-Trader.

As a private trader and market researcher, I do not want to treat seasonality as a black box. My goal is to make the assumptions behind seasonal analysis visible and to evaluate seasonal patterns in a structured, measurable way.

The COT-Trader framework does not rely on a single visual curve or isolated seasonal pattern. Instead, I use a structured research process that combines seasonal tendencies with positioning data, fundamental context and risk metrics.

Step 1 – Historical Data Collection

Daily market data is collected over a defined historical period. For futures markets, contract structure, roll effects and data continuity must be considered carefully.

Step 2 – Data Normalization

Historical years are normalized to improve comparability and reduce distortions caused by changing price levels. This allows each historical year to contribute on a comparable percentage basis.

Step 3 – Seasonal Pattern Identification

Seasonal tendencies are analyzed using historical performance data. The objective is to identify recurring periods of strength or weakness, not to assume that history will repeat exactly.

Step 4 – Seasonal Window Evaluation

Potential trading windows are evaluated using measurable statistics such as:

• win rate,
• average return,
• sample size,
• drawdown characteristics,
• risk-reward profile.

Step 5 – Additional Market Confirmation

Seasonality is not used in isolation. Whenever possible, seasonal tendencies are evaluated alongside additional market information such as:

• Commitment of Traders (COT) positioning,
• open interest developments,
• WASDE reports and agricultural fundamentals,
• broader commodity market context,
• volatility and risk conditions.

The objective is not to predict markets with certainty. The objective is to identify historically recurring opportunities that are supported by multiple independent factors.

Key Takeaway

Seasonality is a valuable research tool, but it is often misunderstood.

Different calculation methods produce different results because they answer different questions. Rather than searching for a single correct seasonal chart, traders should focus on understanding:

• how the data was calculated,
• which assumptions were made,
• which limitations exist,
• whether the methodology fits the trading objective.

A seasonal chart is only as useful as the methodology behind it.

Transparency is the first step toward using seasonality effectively in market research and trading.

Sources and Further Reading

• TradingView Help Center – Seasonality charts
• CME Group – Agricultural futures and seasonal market factors
• SeasonalCharts – Market seasonality concepts
• Seasonax – Seasonality and seasonal analysis
• General time series analysis literature on seasonality and decomposition methods

Disclaimer

Past performance does not guarantee future results. Seasonal patterns represent historical tendencies and should not be interpreted as investment advice or guarantees of future market behavior. Trading futures, CFDs and other leveraged products involves substantial risk.