Share of the day's volume printed minute by minute into the US close, and the closing cross itself
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 2026-11-07, from KRX After-Hours Trading: Korea's Sessions.
| et_time | share_of_session_pct |
|---|---|
| 15:50 | 0.573 |
| 15:51 | 0.425 |
| 15:52 | 0.518 |
| 15:53 | 0.741 |
| 15:54 | 1.114 |
| 15:55 | 1.044 |
| 15:56 | 0.76 |
| 15:57 | 0.995 |
| 15:58 | 1.315 |
| 15:59 | 2.218 |
| 16:00 | 20.333 |
- Rows × columns
- 11 × 2
- 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 |
|---|---|---|---|
et_time |
text | 11 distinct values (15:50, 15:51, 15:52…) | |
share_of_session_pct |
number | 0.425 to 20.333 | percent |
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
et_time,
round(100 * sum(shares) / any(d.session_volume), 3) AS share_of_session_pct
FROM
(
SELECT
if(has(conditions, 8), '16:00',
formatDateTime(toTimeZone(sip_timestamp, 'America/New_York'), '%H:%i')) AS et_time,
toFloat64(size) AS shares
FROM global_markets.stocks_trades
WHERE ticker = 'AAPL'
AND sip_timestamp >= toDateTime('2026-09-15 15:50:00', 'America/New_York')
AND sip_timestamp < toDateTime('2026-09-15 16:10:00', 'America/New_York')
AND NOT hasAny(conditions, [15, 16, 38])
AND (has(conditions, 8)
OR sip_timestamp < toDateTime('2026-09-15 16:00:00', 'America/New_York'))
) AS m
CROSS JOIN
(
SELECT toFloat64(sum(size)) AS session_volume
FROM global_markets.stocks_trades
WHERE ticker = 'AAPL'
AND sip_timestamp >= toDateTime('2026-09-15 04:00:00', 'America/New_York')
AND sip_timestamp < toDateTime('2026-09-15 20:00:00', 'America/New_York')
AND NOT hasAny(conditions, [15, 16, 38])
) AS d
GROUP BY et_time
ORDER BY et_time
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.