dst_switch
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 2026-10-08, from us-premarket-and-after-hours-lagos-time.
| session_date | session_label | et_open | wat_open | wat_open_decimal |
|---|---|---|---|---|
| 2025-10-27 | 27 Oct | 09:30 | 14:30 | 14.5 |
| 2025-10-28 | 28 Oct | 09:30 | 14:30 | 14.5 |
| 2025-10-29 | 29 Oct | 09:30 | 14:30 | 14.5 |
| 2025-10-30 | 30 Oct | 09:30 | 14:30 | 14.5 |
| 2025-10-31 | 31 Oct | 09:30 | 14:30 | 14.5 |
| 2025-11-03 | 3 Nov | 09:30 | 15:30 | 15.5 |
| 2025-11-04 | 4 Nov | 09:30 | 15:30 | 15.5 |
| 2025-11-05 | 5 Nov | 09:30 | 15:30 | 15.5 |
| 2025-11-06 | 6 Nov | 09:30 | 15:30 | 15.5 |
| 2025-11-07 | 7 Nov | 09:30 | 15:30 | 15.5 |
- Rows × columns
- 10 × 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 | 2025-10-27 to 2025-11-07 | |
session_label |
text | 10 distinct values (27 Oct, 28 Oct, 29 Oct…) | |
et_open |
text | 1 distinct value (09:30) | |
wat_open |
text | 2 distinct values (14:30, 15:30) | |
wat_open_decimal |
number | 14.5 to 15.5 | US dollars |
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,
formatDateTime(toDate(toTimeZone(window_start, 'America/New_York')), '%e %b') AS session_label,
formatDateTime(toTimeZone(min(window_start), 'America/New_York'), '%H:%i') AS et_open,
formatDateTime(toTimeZone(min(window_start), 'Africa/Lagos'), '%H:%i') AS wat_open,
round(toHour(toTimeZone(min(window_start), 'Africa/Lagos'))
+ toMinute(toTimeZone(min(window_start), 'Africa/Lagos')) / 60, 2) AS wat_open_decimal
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'SPY'
AND window_start >= toDateTime('2025-10-27 00:00:00')
AND window_start < toDateTime('2025-11-08 00:00:00')
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_date, session_label
ORDER BY session_date
Use dis data for your AI assistant
E go open ready to query, with dis page data. Free, no account.