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Why a Limit Order Didn't Fill: 5 Causes
Where one session of AAPL prints actually happened, by venueranking · 2026-10-05 · 15×4Preview: 15 ranked values, largest first. Minutes a session spends near its own low, five household namesseries · 2026-10-05 · 5×4Preview: a 5-point series, roughly flat. Shares traded at KO's lowest price levels in one sessionranking · 2026-10-05 · 8×3Preview: 8 ranked values, smallest first. Odd lots versus round lots in one AAPL session: prints and sharesranking · 2026-10-05 · 5×4Preview: 5 ranked values, largest first.
What Is a Liquidity Sweep in Trading?
SPY 2025: the ten widest pokes above the prior day's high that closed back below itseries · 2026-09-28 · 10×7Preview: a 10-point series, ending lower. Inside the widest 2025 SPY sweep day: fifteen-minute high, low and volumeseries · 2026-09-28 · 26×4Preview: a 16-point series, ending lower. Volume in the fifteen minutes that printed the high, beside the session's median and busiest slicesranking · 2026-09-28 · 3×2Preview: 3 ranked values, smallest first. SPY: share of sessions that swept the prior day's high or low, by yearranking · 2026-09-28 · 5×3Preview: 5 ranked values, smallest first. How often a session pokes through the prior day's high or low and closes back inside, 2021 to 2025table · 2026-09-28 · 7×6
How to Read the US Equity Trade Tape
Two companies behind one symbol: TWTR on the daily barsseries · 2026-09-15 · 2×5Preview: a 2-point series, ending higher. The tape column on eight symbols, 2023-03-15ranking · 2026-09-15 · 8×3Preview: 8 ranked values, smallest first. Sessions and symbols on the tape, by yearseries · 2026-09-15 · 24×4Preview: a 16-point series, ending higher. Share of prints with a placeholder participant timestamp, one March session per year (IBM and MSFT)ranking · 2026-09-15 · 24×4Preview: 16 ranked values, smallest first. IBM's NYSE closing auction print: seconds after 4:00 p.m. ET, one March session per yearranking · 2026-09-15 · 17×4Preview: 16 ranked values, smallest first. Placeholder share by session, five large names, July to August 2015series · 2026-09-15 · 15×3Preview: a 15-point series, ending lower. The first session with a populated participant timestampscalar · 2026-09-15 · 1×26.41M Correction indicator counts on one session, fifteen large names, 2023-03-15ranking · 2026-09-15 · 5×3Preview: 5 ranked values, smallest first.
Trade Markouts Explained: Execution Quality
Effective spread split into realized spread and adverse selection, by half hourseries · 2026-08-15 · 13×5Preview: a 13-point series, ending lower. INTC markout curve, June 10 2026, measured from two reference basesranking · 2026-08-15 · 5×4Preview: 5 ranked values, largest first. The same curve, split by print size: small fills against blocksranking · 2026-08-15 · 5×4Preview: 5 ranked values, largest first.
Iceberg Orders Explained: Hidden Liquidity
Most repeated price and size pairings, AAPL, June 17, 2026table · 2026-08-06 · 12×5 Every AAPL print on June 17, 2026, grouped by trade sizeranking · 2026-08-06 · 6×4Preview: 6 ranked values, largest first. Average shares per print, monthly, MSFT and KOseries · 2026-08-06 · 90×4Preview: a 16-point series, ending lower. The busiest price and size pairing, half hour by half hourscalar · 2026-08-06 · 1×367
Where one session of AAPL prints actually happened, by venue

Where one session of AAPL prints actually happened, by venue

most recentas of ranking 15×4read in context →
Where one session of AAPL prints actually happened, by venue — 15 rows by 4 columns, computed from US exchange, SIP and OPRA data.
venueshares_millionspct_of_sharesprints
FINRA Alternative Display Facility11.4847.6381723
Nasdaq5.723.6112627
NYSE Arca, Inc.1.93858899
Cboe BZX1.295.434106
Investors Exchange0.933.923913
New York Stock Exchange0.83.321812
Cboe EDGX0.672.814560
Members Exchange0.411.710334
NYSE Texas, Inc.0.31.31310
Cboe BYX0.120.53199
Cboe EDGA0.120.54146
Nasdaq Texas, Inc.0.110.43516
MIAX Pearl0.070.31645
Texas Stock Exchange LLC0.060.31720
Nasdaq Philadelphia Exchange LLC0.050.21272
the exact SQL behind every number
SELECT
    venue,
    round(shares / 1e6, 2)                        AS shares_millions,
    round(100 * shares / sum(shares) OVER (), 1)  AS pct_of_shares,
    prints
FROM
(
    SELECT
        any(if(empty(x.name), concat('Venue ', toString(t.exchange)), x.name)) AS venue,
        sum(t.size)                                                           AS shares,
        count()                                                               AS prints
    FROM global_markets.stocks_trades AS t
    LEFT JOIN global_markets.stocks_exchanges AS x ON x.id = t.exchange
    WHERE t.ticker = 'AAPL'
      AND t.sip_timestamp >= '2026-09-15 00:00:00'
      AND t.sip_timestamp <  '2026-09-16 00:00:00'
      AND (toHour(toTimeZone(t.sip_timestamp, 'America/New_York')) * 60
           + toMinute(toTimeZone(t.sip_timestamp, 'America/New_York'))) >= 570
      AND (toHour(toTimeZone(t.sip_timestamp, 'America/New_York')) * 60
           + toMinute(toTimeZone(t.sip_timestamp, 'America/New_York'))) < 960
    GROUP BY t.exchange
)
ORDER BY shares_millions DESC
LIMIT 15
$