STRASMORE/EXPLORE 3,256 QUERIES 22Y EQUITIES · 12Y OPTIONS

3,256 answered market questions

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Does OPRA Have Depth of Book? Feed Explained
Options trade prints by exchange count, five household names (Sep 16, 2026)ranking · 2026-10-08 · 5×3Preview: 5 ranked values, largest first. Traded volume across strikes, one AAPL expiry (Sep 16, 2026)table · 2026-10-08 · 55×3 Distinct AAPL option contracts trading, by hour of the sessionranking · 2026-10-08 · 8×2Preview: 8 ranked values, largest first. One contract, one minute: displayed size at each exchange's best quoteranking · 2026-10-08 · 8×4Preview: 8 ranked values, largest first. Contracts in the chain versus contracts that traded (Sep 16, 2026)ranking · 2026-10-08 · 12×3Preview: 12 ranked values, largest first.
What Is a Liquidity Heatmap? How to Read One
AAPL top of book by half hour (ET), latest session: NBBO updates and displayed size at the best bid and ask as a share of the peak bucketseries · 2026-10-04 · 32×4Preview: a 16-point series, ending higher.
Level 1 vs Level 2 vs Level 3 Market Data
Executed volume by price level: the traded ladder (KO, 30 minutes)table · 2026-08-07 · 63×3 Level 1 top of book: quoted spread across a full session (AAPL, June 17 2026)series · 2026-08-07 · 32×3Preview: a 16-point series, ending lower. Top-of-book message rate, minute by minute (NVDA, one hour)series · 2026-08-07 · 60×3Preview: a 16-point series, roughly flat. Top-of-book spread and quote message count by name (10:00 to 11:00 a.m. ET)ranking · 2026-08-07 · 5×3Preview: 5 ranked values, smallest first.
Options trade prints by exchange count, five household names (Sep 16, 2026)

Options trade prints by exchange count, five household names (Sep 16, 2026)

most recentas of ranking 5×3read in context →
Options trade prints by exchange count, five household names (Sep 16, 2026) — 5 rows by 3 columns, computed from US exchange, SIP and OPRA data.
symbolvenues_printingbusiest_venue_share_pct
NVDA1813.7
AAPL1815.1
MSFT1816.9
SPY1817
KO1817.3
the exact SQL behind every number
WITH per_venue AS
(
    SELECT
        underlying_symbol AS symbol,
        exchange,
        count()           AS prints
    FROM global_markets.options_trades
    WHERE underlying_symbol IN ('AAPL', 'MSFT', 'NVDA', 'SPY', 'KO')
      AND sip_timestamp >= '2026-09-16 00:00:00'
      AND sip_timestamp <  '2026-09-17 00:00:00'
    GROUP BY symbol, exchange
)
SELECT
    symbol,
    count()                                   AS venues_printing,
    round(100 * max(prints) / sum(prints), 1) AS busiest_venue_share_pct
FROM per_venue
GROUP BY symbol
ORDER BY venues_printing DESC, busiest_venue_share_pct ASC
$