The seasonal gap by era: average half-year returns and the spread between them
Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-08-03, from Does Sell in May and Go Away Work?.
| era | season_count | avg_may_oct_pct | avg_nov_apr_pct | winter_minus_summer_pct | summers_positive |
|---|---|---|---|---|---|
| All seasons 2005-2025 | 21 | 3.88 | 6.06 | 2.18 | 16 |
| First half 2005-2014 | 10 | 1.22 | 6.06 | 4.84 | 7 |
| Second half 2015-2025 | 11 | 6.3 | 6.06 | -0.24 | 9 |
- Rows × columns
- 3 × 6
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
era |
text | 3 distinct values | |
season_count |
number | 10 to 21 | count |
avg_may_oct_pct |
number | 1.22 to 6.3 | percent |
avg_nov_apr_pct |
number | every row is 6.06 | percent |
winter_minus_summer_pct |
number | -0.24 to 4.84 | percent |
summers_positive |
number | 7 to 16 |
Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.
Run it yourself
This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.
WITH monthly AS (
SELECT toStartOfMonth(toDate(toTimeZone(window_start, 'America/New_York'))) AS month_start,
argMax(close, window_start) AS month_close
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2005-04-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-04-30')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY month_start
),
pairs AS (
SELECT toYear(apr_m.month_start) AS season_year,
toFloat64(oct_m.month_close) / toFloat64(apr_m.month_close) - 1 AS may_oct,
toFloat64(apr_next.month_close) / toFloat64(oct_m.month_close) - 1 AS nov_apr
FROM monthly AS apr_m
INNER JOIN monthly AS oct_m ON oct_m.month_start = addMonths(apr_m.month_start, 6)
INNER JOIN monthly AS apr_next ON apr_next.month_start = addMonths(apr_m.month_start, 12)
WHERE toMonth(apr_m.month_start) = 4
),
tagged AS (
SELECT may_oct,
nov_apr,
arrayJoin(['All seasons 2005-2025',
if(season_year <= 2014, 'First half 2005-2014', 'Second half 2015-2025')]) AS era
FROM pairs
)
SELECT era,
count() AS season_count,
round(100 * avg(may_oct), 2) AS avg_may_oct_pct,
round(100 * avg(nov_apr), 2) AS avg_nov_apr_pct,
round(100 * (avg(nov_apr) - avg(may_oct)), 2) AS winter_minus_summer_pct,
countIf(may_oct > 0) AS summers_positive
FROM tagged
GROUP BY era
ORDER BY era
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.