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

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 2026-08-06, from Iceberg Orders Explained: Hidden Liquidity.

as 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
Rows × columns
12 × 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 Most repeated price and size pairings, AAPL, June 17, 2026, derived from the stored result.
ColumnTypeRangeNotes
level_and_size text 12 distinct values
prints number 20 to 67
first_et text 9 distinct values (09:33, 09:34, 11:03…)
last_et text 8 distinct values (09:34, 09:58, 11:19…)
hours_spanned number 0 to 1.8

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 
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

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