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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
Most repeated price and size pairings, AAPL, June 17, 2026

Most repeated price and size pairings, AAPL, June 17, 2026

most recentas of table 12×5read in context →
Most repeated price and size pairings, AAPL, June 17, 2026 — 12 rows by 5 columns, computed from US exchange, SIP and OPRA data.
level_and_sizeprintsfirst_etlast_ethours_spanned
300 shares at $300.546709:3409:580.4
200 shares at $295.154915:0615:530.8
300 shares at $300.934609:3309:340
200 shares at $295.14114:5615:541
200 shares at $2953614:5715:530.9
200 shares at $295.093414:5515:531
200 shares at $295.172714:5615:541
200 shares at $297.922311:0311:190.3
200 shares at $295.272115:0815:540.8
200 shares at $295.892014:1115:591.8
200 shares at $295.22014:5515:490.9
200 shares at $295.132014:5615:520.9
the exact SQL behind every number
SELECT
    concat(toString(size), ' shares at $', toString(round(toFloat64(price), 2)))                  AS level_and_size,
    count()                                                                                       AS prints,
    formatDateTime(toTimeZone(min(sip_timestamp), 'America/New_York'), '%H:%i')                    AS first_et,
    formatDateTime(toTimeZone(max(sip_timestamp), 'America/New_York'), '%H:%i')                    AS last_et,
    round(dateDiff('minute', min(sip_timestamp), max(sip_timestamp)) / 60.0, 1)                    AS hours_spanned
FROM global_markets.stocks_trades
WHERE ticker = 'AAPL'
  AND sip_timestamp >= toDateTime('2026-06-17 04:00:00', 'UTC')
  AND sip_timestamp <  toDateTime('2026-06-18 04:00:00', 'UTC')
  AND size >= 200
GROUP BY price, size
ORDER BY prints DESC
LIMIT 12
$