Shortened sessions on the SPY tape since January 2024
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 2026-09-26, from Mutual Fund Order Cutoff Times by Broker.
| session_date | session_label | weekday | last_bar_et | session_minutes |
|---|---|---|---|---|
| 2024-07-03 | Jul 3, 2024 | Wed | 13:00 | 211 |
| 2024-11-29 | Nov 29, 2024 | Fri | 13:00 | 211 |
| 2024-12-24 | Dec 24, 2024 | Tue | 13:00 | 211 |
| 2025-07-03 | Jul 3, 2025 | Thu | 13:00 | 211 |
| 2025-11-28 | Nov 28, 2025 | Fri | 13:00 | 211 |
| 2025-12-24 | Dec 24, 2025 | Wed | 13:00 | 211 |
- Rows × columns
- 6 × 5
- 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 |
|---|---|---|---|
session_date |
date | 2024-07-03 to 2025-12-24 | |
session_label |
text | 6 distinct values (Dec 24, 2024, Dec 24, 2025, Jul 3, 2024…) | |
weekday |
text | 4 distinct values (Fri, Thu, Tue…) | |
last_bar_et |
text | 1 distinct value (13:00) | |
session_minutes |
number | every row is 211 |
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(session_day) AS session_date,
session_label,
weekday,
last_bar_et,
session_minutes
FROM
(
SELECT
toDate(toTimeZone(window_start, 'America/New_York')) AS session_day,
formatDateTime(min(toTimeZone(window_start, 'America/New_York')), '%b %e, %Y') AS session_label,
formatDateTime(min(toTimeZone(window_start, 'America/New_York')), '%a') AS weekday,
formatDateTime(max(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS last_bar_et,
toUInt32(count()) AS session_minutes
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND window_start >= toDateTime('2024-01-01 00:00:00')
AND window_start < today() - 2
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) >= 570
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) < 960
GROUP BY session_day
HAVING session_minutes BETWEEN 60 AND 330
)
ORDER BY session_day
Use dis data for your AI assistant
E go open ready to query, with dis page data. Free, no account.