A seasonality backtest asks a focused question: how did a market behave during the same recurring period in many different years?
Instead of coding dozens of indicator rules, calendar-based research keeps the test centered on time: a recurring start date, end date and historical lookback.
The exact same calendar period is measured in each historical year.
Yearly observations show whether the effect was persistent or concentrated in a few periods.
The tested window can be compared with the broader full-year seasonal path.
Consistency across multiple lookbacks, a reasonable sample size, similar average and median returns, and a pattern that does not disappear when dates shift slightly are all useful signs of robustness.
No single statistic should be treated as proof. The goal is to reduce the chance that a visually attractive pattern is only noise.
You might test whether the S&P 500 historically performed differently from November through April, whether a specific stock tends to strengthen ahead of an annual event, or whether September weakness appears consistently across decades.
Each question should be defined before looking at the answer.
| Check | Why it matters |
|---|---|
| Exact start/end dates | Keeps the test reproducible |
| Multiple lookbacks | Checks historical stability |
| Win rate | Shows frequency of positive outcomes |
| Average + median | Highlights outlier dependence |
| Year-by-year returns | Shows the distribution behind the summary |
| Nearby windows | Tests sensitivity to exact dates |
Use the dashboard for recurring-window analysis on stocks and indices.