How the session's volume piled up against the prior 10 sessions, checkpoint by checkpoint

Name: How the session's volume piled up against the prior 10 sessions, checkpoint by checkpoint
Creator: Strasmore
Published: 2026-09-16
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 2026-09-16, from Why Relative Volume Differs Between Platforms.

as of 2026-09-16 series 14×4read in context →

How the session's volume piled up against the prior 10 sessions, checkpoint by checkpoint — 14 rows by 4 columns, computed 2026-09-16 from US exchange, SIP and OPRA data.
et_time	session_cumulative_millions	typical_cumulative_millions	typical_share_of_session_pct
09:45	8.3	2.7	8.5
10:00	11.1	4.5	14.1
10:30	14.5	7.5	23.8
11:00	19	10.1	31.9
11:30	23.4	12.4	39.2
12:00	29.2	14.5	45.7
12:30	32.8	16.3	51.4
13:00	38.2	17.7	55.8
13:30	41.2	19.5	61.5
14:00	45.3	21	66.4
14:30	48	22.9	72.4
15:00	50.3	25.1	79.3
15:30	53.4	26.9	85.1
16:00	59.3	31.7	100

Rows × columns: 14 × 4
Computed: 2026-09-16
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 How the session's volume piled up against the prior 10 sessions, checkpoint by checkpoint, derived from the stored result.
Column	Type	Range	Notes
`et_time`	text	14 distinct values (09:45, 10:00, 10:30…)
`session_cumulative_millions`	number	8.3 to 59.3
`typical_cumulative_millions`	number	2.7 to 31.7
`typical_share_of_session_pct`	number	8.5 to 100	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 2026-09-16, and the query below is what returned them.

the exact SQL behind every number

WITH
    toDate('2026-09-10') AS session_day,
    [585, 600, 630, 660, 690, 720, 750, 780, 810, 840, 870, 900, 930, 960] AS checkpoints
SELECT
    formatDateTime(toDateTime(session_day, 'UTC') + checkpoints[i] * 60, '%H:%i', 'UTC') AS et_time,
    round(session_cums[i] / 1e6, 1)                                                        AS session_cumulative_millions,
    round(typical_cums[i] / 1e6, 1)                                                        AS typical_cumulative_millions,
    round(100 * typical_cums[i] / typical_cums[14], 1)                                     AS typical_share_of_session_pct
FROM
(
    SELECT
        arrayJoin(arrayEnumerate(checkpoints)) AS i,
        session_cums,
        arrayMap(k -> arrayAvg(x -> arrayElement(tupleElement(x, 2), k), arraySlice(prior, 1, 10)),
                 arrayEnumerate(checkpoints)) AS typical_cums
    FROM
    (
        SELECT
            anyIf(cums, d = session_day)                                                        AS session_cums,
            arrayReverseSort(x -> tupleElement(x, 1), groupArrayIf((d, cums), d < session_day)) AS prior
        FROM
        (
            SELECT
                d,
                arrayMap(cp -> arraySum(x -> if(tupleElement(x, 1) <= cp, tupleElement(x, 2), 0), mv),
                         checkpoints) AS cums
            FROM
            (
                SELECT d, groupArray((minute_of_day, vol)) AS mv
                FROM
                (
                    SELECT
                        toDate(toTimeZone(window_start, 'America/New_York'))      AS d,
                        toHour(toTimeZone(window_start, 'America/New_York')) * 60
                          + toMinute(toTimeZone(window_start, 'America/New_York')) AS minute_of_day,
                        max(toFloat64(volume))                                     AS vol
                    FROM global_markets.delayed_stocks_minute_aggs
                    WHERE ticker = 'AAPL'
                      AND window_start >= toDateTime(session_day - 20, 'America/New_York')
                      AND window_start <  toDateTime(session_day + 1, 'America/New_York')
                    GROUP BY d, minute_of_day
                    HAVING minute_of_day >= 570 AND minute_of_day <= 960
                )
                GROUP BY d
            )
        )
        HAVING length(session_cums) = 14 AND length(prior) >= 10
    )
)
ORDER BY i

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