wat_clock
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.
| wat_clock | et_clock | avg_volume_millions |
|---|---|---|
| 09:00 | 04:00 | 0.1 |
| 10:00 | 05:00 | 0.03 |
| 11:00 | 06:00 | 0.05 |
| 12:00 | 07:00 | 0.11 |
| 13:00 | 08:00 | 0.15 |
| 14:00 | 09:00 | 5.6 |
| 15:00 | 10:00 | 5.15 |
| 16:00 | 11:00 | 4.06 |
| 17:00 | 12:00 | 3.02 |
| 18:00 | 13:00 | 2.95 |
| 19:00 | 14:00 | 3.36 |
| 20:00 | 15:00 | 6.05 |
| 21:00 | 16:00 | 1.31 |
| 22:00 | 17:00 | 0.08 |
| 23:00 | 18:00 | 0.04 |
| 00:00 | 19:00 | 0.03 |
- Rows × columns
- 16 × 3
- 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 |
|---|---|---|---|
wat_clock |
text | 16 distinct values (00:00, 09:00, 10:00…) | |
et_clock |
text | 16 distinct values (04:00, 05:00, 06:00…) | |
avg_volume_millions |
number | 0.03 to 6.05 | count |
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
formatDateTime(toTimeZone(window_start, 'Africa/Lagos'), '%H:00') AS wat_clock,
formatDateTime(toTimeZone(window_start, 'America/New_York'), '%H:00') AS et_clock,
round(toFloat64(sum(volume))
/ countDistinct(toDate(toTimeZone(window_start, 'America/New_York')))
/ 1e6, 2) AS avg_volume_millions
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'AAPL'
AND window_start >= toDateTime('2026-09-08 08:00:00')
AND window_start < toDateTime('2026-10-04 00:00:00')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) >= 240
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) < 1200
GROUP BY wat_clock, et_clock
ORDER BY et_clock
Use dis data for your AI assistant
E go open ready to query, with dis page data. Free, no account.