STRASMORE/EXPLORE 2,985 QUERIES

auction_share_by_day

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 2026-10-03, from closing-auction-share-of-daily-volume.

as of ranking 4×3read in context →
auction_share_by_day — 4 rows by 3 columns, computed from US exchange, SIP and OPRA data.
labelnyse_listed_pctnasdaq_listed_pct
quiet Tuesday, Aug 1119.1717.39
monthly opex Friday, Aug 2110.3512.62
quarterly rebalance Friday, Sep 1826.635.5
ordinary Wednesday, Sep 2314.5221.6
Rows × columns
4 × 3
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 auction_share_by_day, derived from the stored result.
ColumnTypeRangeNotes
label text 4 distinct values
nyse_listed_pct number 10.35 to 26.6 percent
nasdaq_listed_pct number 12.62 to 35.5 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.

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
    auction AS
    (
        SELECT
            toDate(toTimeZone(sip_timestamp, 'America/New_York')) AS session,
            ticker,
            max(size)                                             AS auction_shares
        FROM global_markets.stocks_trades
        WHERE ticker IN ('KO', 'JNJ', 'XOM', 'WMT', 'AAPL', 'MSFT', 'COST', 'CSCO')
          AND toDate(sip_timestamp) IN ('2026-08-11', '2026-08-21', '2026-09-18', '2026-09-23')
          AND toHour(toTimeZone(sip_timestamp, 'America/New_York')) = 16
          AND toMinute(toTimeZone(sip_timestamp, 'America/New_York')) = 0
        GROUP BY session, ticker
    ),
    day_volume AS
    (
        SELECT
            date        AS session,
            ticker,
            max(volume) AS day_shares
        FROM global_markets.stocks_daily_aggs
        WHERE ticker IN ('KO', 'JNJ', 'XOM', 'WMT', 'AAPL', 'MSFT', 'COST', 'CSCO')
          AND date IN ('2026-08-11', '2026-08-21', '2026-09-18', '2026-09-23')
        GROUP BY session, ticker
    )
SELECT
    multiIf(
        session = '2026-08-11', 'quiet Tuesday, Aug 11',
        session = '2026-08-21', 'monthly opex Friday, Aug 21',
        session = '2026-09-18', 'quarterly rebalance Friday, Sep 18',
                                'ordinary Wednesday, Sep 23')                 AS label,
    round(100 * sumIf(toFloat64(auction_shares), ticker IN ('KO', 'JNJ', 'XOM', 'WMT'))
              / sumIf(toFloat64(day_shares),     ticker IN ('KO', 'JNJ', 'XOM', 'WMT')), 2)             AS nyse_listed_pct,
    round(100 * sumIf(toFloat64(auction_shares), ticker IN ('AAPL', 'MSFT', 'COST', 'CSCO'))
              / sumIf(toFloat64(day_shares),     ticker IN ('AAPL', 'MSFT', 'COST', 'CSCO')), 2)        AS nasdaq_listed_pct
FROM auction
INNER JOIN day_volume USING (session, ticker)
GROUP BY session
ORDER BY session
⌘/Ctrl + Enter

Work with this data in your AI assistant

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

More from this analysisclosing-auction-share-of-daily-volume
biggest_prints ranking 8×4 → auction_share_by_session series 42×3 → final_minutes_trace series 11×3 → close_conditions table 7×4 → Top 25 weekly-options underlyings by distinct contracts traded, with expiration weekdays ranking 25×4 → Annualized volatility vs total return, 25 large caps, calmest to wildest (~2 years) ranking 25×3 → See all 2,985 queries →