AAPL after the close: the heaviest post-close evening of Q3 2026, on both clocks

Name: AAPL after the close: the heaviest post-close evening of Q3 2026, on both clocks
Creator: Strasmore
Published: 2026-10-16
License: https://www.strasmore.com/terms/

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 2026-10-16, from US Earnings Release Times in Beijing Time.

as of 2026-10-16 table 16×5read in context →

AAPL after the close: the heaviest post-close evening of Q3 2026, on both clocks — 16 rows by 5 columns, computed 2026-10-16 from US exchange, SIP and OPRA data.
beijing_clock	et_clock	us_evening	shares_millions	trades_thousands
04:00	16:00	Jul 30	3.61	7.8
04:15	16:15	Jul 30	0.29	5.8
04:30	16:30	Jul 30	3.4	63.2
04:45	16:45	Jul 30	2.07	18.4
05:00	17:00	Jul 30	0.84	17.3
05:15	17:15	Jul 30	2.01	36
05:30	17:30	Jul 30	1.86	30.5
05:45	17:45	Jul 30	0.83	13.1
06:00	18:00	Jul 30	0.42	7.1
06:15	18:15	Jul 30	0.31	5.1
06:30	18:30	Jul 30	0.29	4.7
06:45	18:45	Jul 30	0.19	3.6
07:00	19:00	Jul 30	0.13	3
07:15	19:15	Jul 30	0.17	3
07:30	19:30	Jul 30	0.19	3.3
07:45	19:45	Jul 30	0.12	2.5

Rows × columns: 16 × 5
Computed: 2026-10-16
Completeness: No missing values
Source: US exchange, SIP and OPRA market data
Licence: Strasmore terms · free, no signup
Formats: JSON · CSV · the SQL below

What each column holds

Column definitions for AAPL after the close: the heaviest post-close evening of Q3 2026, on both clocks, derived from the stored result.
Column	Type	Range	Notes
`beijing_clock`	text	16 distinct values (04:00, 04:15, 04:30…)
`et_clock`	text	16 distinct values (16:00, 16:15, 16:30…)
`us_evening`	text	1 distinct value (Jul 30)
`shares_millions`	number	0.12 to 3.61	count
`trades_thousands`	number	2.5 to 63.2	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 2026-10-16, 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.

WITH busiest_evening AS
(
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'AAPL'
      AND window_start >= '2026-07-01 00:00:00'
      AND window_start <  '2026-10-01 00:00:00'
      AND toHour(toTimeZone(window_start, 'America/New_York')) BETWEEN 16 AND 19
    GROUP BY d
    ORDER BY sum(volume) DESC
    LIMIT 1
)
SELECT
    formatDateTime(toStartOfFifteenMinutes(toTimeZone(window_start, 'Asia/Shanghai')), '%H:%i')    AS beijing_clock,
    formatDateTime(toStartOfFifteenMinutes(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS et_clock,
    formatDateTime(toTimeZone(window_start, 'America/New_York'), '%b %e')                          AS us_evening,
    round(toFloat64(sum(volume)) / 1e6, 2)                                                         AS shares_millions,
    round(toFloat64(sum(transactions)) / 1e3, 1)                                                   AS trades_thousands
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'AAPL'
  AND window_start >= '2026-07-01 00:00:00'
  AND window_start <  '2026-10-01 00:00:00'
  AND toDate(toTimeZone(window_start, 'America/New_York')) = (SELECT d FROM busiest_evening)
  AND toHour(toTimeZone(window_start, 'America/New_York')) BETWEEN 16 AND 19
GROUP BY beijing_clock, et_clock, us_evening
ORDER BY et_clock

SQL to run

WITH busiest_evening AS
(
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'AAPL'
      AND window_start >= '2026-07-01 00:00:00'
      AND window_start <  '2026-10-01 00:00:00'
      AND toHour(toTimeZone(window_start, 'America/New_York')) BETWEEN 16 AND 19
    GROUP BY d
    ORDER BY sum(volume) DESC
    LIMIT 1
)
SELECT
    formatDateTime(toStartOfFifteenMinutes(toTimeZone(window_start, 'Asia/Shanghai')), '%H:%i')    AS beijing_clock,
    formatDateTime(toStartOfFifteenMinutes(toTimeZone(window_start, 'America/New_York')), '%H:%i') AS et_clock,
    formatDateTime(toTimeZone(window_start, 'America/New_York'), '%b %e')                          AS us_evening,
    round(toFloat64(sum(volume)) / 1e6, 2)                                                         AS shares_millions,
    round(toFloat64(sum(transactions)) / 1e3, 1)                                                   AS trades_thousands
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker = 'AAPL'
  AND window_start >= '2026-07-01 00:00:00'
  AND window_start <  '2026-10-01 00:00:00'
  AND toDate(toTimeZone(window_start, 'America/New_York')) = (SELECT d FROM busiest_evening)
  AND toHour(toTimeZone(window_start, 'America/New_York')) BETWEEN 16 AND 19
GROUP BY beijing_clock, et_clock, us_evening
ORDER BY et_clock

⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent

More from this analysisUS Earnings Release Times in Beijing Time

Earnings headlines by the hour on a Beijing clock, Q3 2026 ranking 24×4 → JPM before the open: the heaviest premarket morning of Q3 2026, on both clocks ranking 12×4 → Earnings headlines by US weekday, and the Beijing day a post-close print arrives on series 5×4 → SPY volume by New York hour, with the London clock beside it series 16×4 → AAPL median quoted spread by hour across one full extended day series 16×4 → The London clock time of the 9:30am ET open, late October 2025 series 16×4 → See all 3,293 queries →