Every 121-day window around a Coca-Cola ex-dividend date
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 2026-09-20, from Qualified Dividend Holding Period: 61 Days.
| ex_date | ex_date_label | window_opens | window_closes | calendar_days_in_window | trading_sessions_in_window |
|---|---|---|---|---|---|
| 2023-03-16 | Mar 16, 2023 | Jan 15 | May 15 | 121 | 83 |
| 2023-06-15 | Jun 15, 2023 | Apr 16 | Aug 14 | 121 | 83 |
| 2023-09-14 | Sep 14, 2023 | Jul 16 | Nov 13 | 121 | 85 |
| 2023-11-30 | Nov 30, 2023 | Oct 1 | Jan 29 | 121 | 82 |
| 2024-03-14 | Mar 14, 2024 | Jan 14 | May 13 | 121 | 83 |
| 2024-06-14 | Jun 14, 2024 | Apr 15 | Aug 13 | 121 | 84 |
| 2024-09-13 | Sep 13, 2024 | Jul 15 | Nov 12 | 121 | 86 |
| 2024-11-29 | Nov 29, 2024 | Sep 30 | Jan 28 | 121 | 82 |
| 2025-03-14 | Mar 14, 2025 | Jan 13 | May 13 | 121 | 84 |
| 2025-06-13 | Jun 13, 2025 | Apr 14 | Aug 12 | 121 | 83 |
| 2025-09-15 | Sep 15, 2025 | Jul 17 | Nov 14 | 121 | 86 |
| 2025-12-01 | Dec 1, 2025 | Oct 2 | Jan 30 | 121 | 83 |
| 2026-03-13 | Mar 13, 2026 | Jan 12 | May 12 | 121 | 84 |
| 2026-06-15 | Jun 15, 2026 | Apr 16 | Aug 14 | 121 | 84 |
- Rows × columns
- 14 × 6
- Period covered
- to
- 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 |
|---|---|---|---|
ex_date |
date | 2023-03-16 to 2026-06-15 | |
ex_date_label |
text | 14 distinct values (Dec 1, 2025, Jun 13, 2025, Jun 14, 2024…) | |
window_opens |
text | 13 distinct values (Apr 14, Apr 15, Apr 16…) | |
window_closes |
text | 12 distinct values (Aug 12, Aug 13, Aug 14…) | |
calendar_days_in_window |
number | every row is 121 | |
trading_sessions_in_window |
number | 82 to 86 |
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.
SELECT
toString(x.ex_date) AS ex_date,
formatDateTime(x.ex_date, '%b %e, %Y') AS ex_date_label,
formatDateTime(x.ex_date - 60, '%b %e') AS window_opens,
formatDateTime(x.ex_date + 60, '%b %e') AS window_closes,
121 AS calendar_days_in_window,
countDistinct(s.date) AS trading_sessions_in_window
FROM
(
SELECT DISTINCT ex_dividend_date AS ex_date
FROM global_markets.stocks_dividends
WHERE ticker = 'KO'
AND cash_amount > 0
AND ex_dividend_date >= '2023-01-01'
AND ex_dividend_date <= today() - 61
) AS x
CROSS JOIN
(
SELECT date
FROM global_markets.stocks_daily_aggs
WHERE ticker = 'SPY'
AND date >= '2022-10-01'
AND date <= today()
) AS s
WHERE s.date >= x.ex_date - 60
AND s.date <= x.ex_date + 60
GROUP BY x.ex_date
ORDER BY x.ex_date
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.