1. Define the exact spread
Specify both delivery months, contract years and calculation order. Avoid vague or generic continuous-contract labels.
2. Set the window before evaluating it
Choose the entry and exit dates you want to test. If dates are repeatedly changed after seeing the result, the study becomes vulnerable to hindsight bias.
3. Align equivalent windows across history
Apply the same seasonal dates to prior years using the corresponding dated-contract relationship.
Measure the pattern instead of assuming it
Choose the exact spread, define the dates and compare multiple history windows with the individual seasons behind the average.
Analyze Seasonal Behavior4. Measure more than average return
Track win rate, average move, median move, best and worst years, drawdown and adverse excursion.
5. Compare several lookbacks
A pattern that survives 5-, 10-, 15- and 20-year samples is more stable than one that appears only in a single hand-picked window.
6. Inspect every trade
A table of individual historical seasons is critical. It reveals clustering, outliers and whether one unusual year dominates the result.
7. Add current-market context
Use the forward curve and current contract pricing to understand whether today's market resembles the historical environment.
Build the backtest before looking at the answer
Write down both contract months, calculation order, entry date and exit date first. Only then calculate historical results. This reduces the temptation to change the rules until the backtest looks attractive.
What the backtest should output
| Output | Purpose |
|---|---|
| Year-by-year trades | Shows the actual distribution |
| Average and median | Shows central tendency and outlier sensitivity |
| Win rate | Frequency of profitable seasons |
| Worst move | Historical downside example |
| Cumulative P&L | Shows path dependency |
Test robustness, not just profitability
Shift entry and exit dates slightly and compare several lookbacks. If performance collapses immediately, the result may be overfit. Also ask what happens if the strongest historical year is removed from the sample.
Research is not execution
Historical spread movement does not include every real-world execution effect. Slippage, commissions, margin and timing can affect realized outcomes. Use backtest P&L as research evidence, not a guarantee.
Common backtest failure modes
A seasonal backtest can look impressive while still being methodologically weak. Common problems include using inconsistent contract rolls, choosing dates after seeing the result, excluding difficult years, mixing price conventions or reporting only the strongest lookback.
Another problem is treating overlapping historical windows as independent evidence when they are driven by the same underlying market regime. A robust backtest should make the sample construction transparent and should preserve all years that meet the rules.
Finally, distinguish statistical attractiveness from practical tradability. A spread with beautiful historical behavior may still be difficult to execute if one leg is illiquid or the contract specifications create a large dollar exposure.
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Frequently asked questions
What is futures seasonality?
Futures seasonality is the study of recurring historical behavior at similar times of year. It describes past patterns and does not guarantee future performance.
Why use dated contracts for seasonal spreads?
Calendar spreads depend on exact delivery months. Using dated contracts preserves the specific relationship being tested instead of blending different expirations.
Is the longest lookback always best?
No. Longer history adds sample size, while shorter windows may better reflect recent structural changes. Comparing several windows is usually more informative.
Should I trust the seasonal average?
Only after checking the individual years, losing seasons, dispersion and whether a few outliers dominate the average.
Research seasonal spreads with transparent history
Compare exact delivery months, recurring windows and every historical season behind the pattern.
Open Futures Spread AnalyzerFutures trading involves substantial risk. Historical seasonality is descriptive and does not guarantee future results. This material is for education and research only and is not investment advice.