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The closing-cross window (3:59-4:00 p.m. ET): dollar volume on witching day vs. a typical June 2026 session

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 2026-08-10, from What Is Triple Witching? Volume & Volatility.

as of scalar 1×5read in context →
witching close dollars b
56.1
other june median close dollars b
36.1
times median
1.56
witching close pct of day
4.9
other june median close pct of day
3.7
Rows × columns
1 × 5
Computed
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 The closing-cross window (3:59-4:00 p.m. ET): dollar volume on witching day vs. a typical June 2026 session, derived from the stored result.
ColumnTypeRangeNotes
witching_close_dollars_b number every row is 56.1 US dollars
other_june_median_close_dollars_b number every row is 36.1 US dollars
times_median number every row is 1.56
witching_close_pct_of_day number every row is 4.9 percent
other_june_median_close_pct_of_day number every row is 3.7 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.

the exact SQL behind every number
WITH sessions AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS day,
           sumIf(toFloat64(close) * toFloat64(volume),
                 toHour(toTimeZone(window_start, 'America/New_York')) = 16
                 OR (toHour(toTimeZone(window_start, 'America/New_York')) = 15
                     AND toMinute(toTimeZone(window_start, 'America/New_York')) = 59)) AS close_dollars,
           sum(toFloat64(close) * toFloat64(volume)) AS day_dollars
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE window_start >= '2026-06-01 04:00:00'
      AND window_start < '2026-07-01 04:00:00'
      AND toHour(toTimeZone(window_start, 'America/New_York')) >= 9
      AND toHour(toTimeZone(window_start, 'America/New_York')) <= 16
      AND NOT (toHour(toTimeZone(window_start, 'America/New_York')) = 9
               AND toMinute(toTimeZone(window_start, 'America/New_York')) < 30)
      AND NOT (toHour(toTimeZone(window_start, 'America/New_York')) = 16
               AND toMinute(toTimeZone(window_start, 'America/New_York')) > 0)
    GROUP BY day
)
SELECT round(anyIf(close_dollars, day = '2026-06-18') / 1e9, 1) AS witching_close_dollars_b,
       round(quantileDeterministicIf(0.5)(close_dollars, cityHash64(toString(day)), day != '2026-06-18') / 1e9, 1) AS other_june_median_close_dollars_b,
       round(anyIf(close_dollars, day = '2026-06-18')
             / quantileDeterministicIf(0.5)(close_dollars, cityHash64(toString(day)), day != '2026-06-18'), 2) AS times_median,
       round(anyIf(close_dollars / day_dollars, day = '2026-06-18') * 100, 1) AS witching_close_pct_of_day,
       round(quantileDeterministicIf(0.5)(close_dollars / day_dollars, cityHash64(toString(day)), day != '2026-06-18') * 100, 1) AS other_june_median_close_pct_of_day
FROM sessions
HAVING countIf(day != '2026-06-18') > 0

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