STRASMORE/EXPLORE 2,170 QUERIES

The same hypothetical on every session: expiration mornings against the rest of the tape

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 2026-08-03, from AM vs PM Settled Index Options Explained.

as of table 3×5read in context →
The same hypothetical on every session: expiration mornings against the rest of the tape — 3 rows by 5 columns, computed from US exchange, SIP and OPRA data.
bucketgroup_sizemedian_gap_abs_pctmedian_settlement_spread_usdmedian_open_minute_volume_m
triple witching Friday90.4900.95
other monthly expiration200.230.80.75
ordinary session6180.280.950.62
Rows × columns
3 × 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 same hypothetical on every session: expiration mornings against the rest of the tape, derived from the stored result.
ColumnTypeRangeNotes
bucket text 3 distinct values
group_size number 9 to 618
median_gap_abs_pct number 0.23 to 0.49 percent
median_settlement_spread_usd number 0 to 0.95 US dollars
median_open_minute_volume_m number 0.62 to 0.95 US dollars

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 session_date,
           toFloat64(argMin(open, window_start)) AS first_print,
           toFloat64(argMax(close, window_start)) AS last_print,
           toFloat64(sumIf(volume, (toHour(toTimeZone(window_start, 'America/New_York')) * 60
                + toMinute(toTimeZone(window_start, 'America/New_York'))) = 570)) AS open_minute_shares
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND toDate(toTimeZone(window_start, 'America/New_York')) BETWEEN toDate('2023-12-01') AND toDate('2026-07-31')
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY session_date
),
sequenced AS (
    SELECT session_date,
           first_print,
           last_print,
           open_minute_shares,
           any(last_print) OVER (ORDER BY session_date ASC
                                 ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prior_close
    FROM sessions
),
labelled AS (
    SELECT session_date,
           multiIf(toDayOfWeek(session_date) = 5
                       AND toDayOfMonth(session_date) BETWEEN 15 AND 21
                       AND toMonth(session_date) IN (3, 6, 9, 12), 'triple witching Friday',
                   toDayOfWeek(session_date) = 5
                       AND toDayOfMonth(session_date) BETWEEN 15 AND 21, 'other monthly expiration',
                   'ordinary session') AS bucket,
           abs(first_print / prior_close - 1) * 100 AS gap_abs_raw,
           abs(greatest(last_print - round(prior_close, 0), 0)
               - greatest(first_print - round(prior_close, 0), 0)) AS spread_raw,
           open_minute_shares / 1000000 AS open_minute_millions
    FROM sequenced
    WHERE prior_close > 0
      AND session_date >= toDate('2024-01-01')
)
SELECT bucket,
       count() AS group_size,
       round(quantileDeterministic(0.5)(gap_abs_raw, cityHash64(session_date)), 2) AS median_gap_abs_pct,
       round(quantileDeterministic(0.5)(spread_raw, cityHash64(session_date)), 2) AS median_settlement_spread_usd,
       round(quantileDeterministic(0.5)(open_minute_millions, cityHash64(session_date)), 2) AS median_open_minute_volume_m
FROM labelled
GROUP BY bucket
ORDER BY group_size ASC

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