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SPY intraday volume curve vs a flat TWAP schedule, June to August 2026

most recentas of 2026-09-28 series 13×4read in context →

SPY intraday volume curve vs a flat TWAP schedule, June to August 2026 — 13 rows by 4 columns, computed 2026-09-28 from US exchange, SIP and OPRA data.
et_time	avg_volume_millions	vwap_share_pct	twap_share_pct
09:30	4.61	11.01	7.69
10:00	3.33	7.97	7.69
10:30	2.79	6.66	7.69
11:00	2.47	5.89	7.69
11:30	2.48	5.93	7.69
12:00	2.2	5.25	7.69
12:30	1.97	4.7	7.69
13:00	2.04	4.88	7.69
13:30	1.87	4.47	7.69
14:00	2.28	5.45	7.69
14:30	2.46	5.88	7.69
15:00	3.14	7.52	7.69
15:30	10.21	24.39	7.69

the exact SQL behind every number

WITH bars AS
(
    SELECT
        toDate(toTimeZone(window_start, 'America/New_York'))           AS et_date,
        toHour(toTimeZone(window_start, 'America/New_York')) * 60
            + toMinute(toTimeZone(window_start, 'America/New_York'))   AS minute_of_day,
        toFloat64(volume)                                              AS shares
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= toDateTime('2026-06-01 00:00:00', 'UTC')
      AND window_start <  toDateTime('2026-09-01 00:00:00', 'UTC')
    UNION ALL
    SELECT
        toDate(toTimeZone(sip_timestamp, 'America/New_York'))          AS et_date,
        959                                                            AS minute_of_day,
        toFloat64(maxIf(size, has(conditions, 8)))                     AS shares
    FROM global_markets.stocks_trades
    WHERE ticker = 'SPY'
      AND sip_timestamp >= toDateTime('2026-06-01 00:00:00', 'UTC')
      AND sip_timestamp <  toDateTime('2026-09-01 00:00:00', 'UTC')
      AND toHour(sip_timestamp, 'America/New_York') IN (13, 16)
      AND toMinute(sip_timestamp, 'America/New_York') < 10
    GROUP BY et_date
    HAVING countIf(has(conditions, 8)) > 0
),
per_bucket AS
(
    SELECT
        formatDateTime(toDateTime(intDiv(minute_of_day, 30) * 30 * 60, 'UTC'), '%H:%i') AS et_time,
        sum(shares)                                                                 AS bucket_shares,
        countDistinct(et_date)                                                      AS sessions
    FROM bars
    WHERE minute_of_day >= 570
      AND minute_of_day <  960
    GROUP BY et_time
)
SELECT
    b.et_time                                          AS et_time,
    round(b.bucket_shares / b.sessions / 1e6, 2)       AS avg_volume_millions,
    round(100 * b.bucket_shares / t.window_shares, 2)  AS vwap_share_pct,
    round(100 / t.bucket_count, 2)                     AS twap_share_pct
FROM per_bucket AS b
CROSS JOIN
(
    SELECT
        sum(bucket_shares) AS window_shares,
        count()            AS bucket_count
    FROM per_bucket
) AS t
ORDER BY et_time

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