dst_gap_2025
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 2026-10-01, from us-premarket-and-after-hours-uk-time.
| session_date | session_label | london_clock_at_us_open | hours_ahead_of_new_york |
|---|---|---|---|
| 2025-10-17 | Fri 17 Oct | 14:30 | 5 |
| 2025-10-20 | Mon 20 Oct | 14:30 | 5 |
| 2025-10-21 | Tue 21 Oct | 14:30 | 5 |
| 2025-10-22 | Wed 22 Oct | 14:30 | 5 |
| 2025-10-23 | Thu 23 Oct | 14:30 | 5 |
| 2025-10-24 | Fri 24 Oct | 14:30 | 5 |
| 2025-10-27 | Mon 27 Oct | 13:30 | 4 |
| 2025-10-28 | Tue 28 Oct | 13:30 | 4 |
| 2025-10-29 | Wed 29 Oct | 13:30 | 4 |
| 2025-10-30 | Thu 30 Oct | 13:30 | 4 |
| 2025-10-31 | Fri 31 Oct | 13:30 | 4 |
| 2025-11-03 | Mon 3 Nov | 14:30 | 5 |
| 2025-11-04 | Tue 4 Nov | 14:30 | 5 |
| 2025-11-05 | Wed 5 Nov | 14:30 | 5 |
| 2025-11-06 | Thu 6 Nov | 14:30 | 5 |
| 2025-11-07 | Fri 7 Nov | 14:30 | 5 |
- Rows × columns
- 16 × 4
- 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 | 2025-10-17 to 2025-11-07 | |
session_label |
text | 16 distinct values (Fri 17 Oct, Fri 24 Oct, Fri 31 Oct…) | |
london_clock_at_us_open |
text | 2 distinct values (13:30, 14:30) | |
hours_ahead_of_new_york |
number | 4 to 5 |
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(toDate(toTimeZone(window_start, 'America/New_York'))) AS session_date,
any(formatDateTime(toTimeZone(window_start, 'America/New_York'), '%a %e %b')) AS session_label,
formatDateTime(any(toTimeZone(window_start, 'Europe/London')), '%H:%i') AS london_clock_at_us_open,
toUInt8((toHour(any(toTimeZone(window_start, 'Europe/London')))
- toHour(any(toTimeZone(window_start, 'America/New_York'))) + 24) % 24) AS hours_ahead_of_new_york
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND window_start >= '2025-10-17 00:00:00'
AND window_start < '2025-11-08 00:00:00'
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) = 570
GROUP BY session_date
ORDER BY session_date
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.