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SIP receive lag by venue, AAPL, 10 June 2026 (microseconds)

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 2026-08-14, from Market Data Timestamps: SIP vs Exchange Clocks.

as of ranking 11×4read in context →
SIP receive lag by venue, AAPL, 10 June 2026 (microseconds) — 11 rows by 4 columns, computed from US exchange, SIP and OPRA data.
venueprint_countmedian_lag_usp99_lag_us
NYSE Arca, Inc.10809346.2420.8
NSX229345.3350.5
New York Stock Exchange1468343.6358.4
Investors Exchange2765224.91097.5
Members Exchange1539196.31198.3
Cboe EDGA415187.6196.8
Cboe EDGX2498187.6320.1
Cboe BYX543186.6192.6
Cboe BZX3905185.8203.2
Nasdaq1644214.754.5
Nasdaq Texas, Inc.44913.717.8
Rows × columns
11 × 4
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 SIP receive lag by venue, AAPL, 10 June 2026 (microseconds), derived from the stored result.
ColumnTypeRangeNotes
venue text 11 distinct values (Cboe BYX, Cboe BZX, Cboe EDGA…)
print_count number 229 to 16,442 count
median_lag_us number 13.7 to 346.2
p99_lag_us number 17.8 to 1,198.3

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 
WITH venues AS
(
    SELECT
        toUInt32(id)                             AS exchange_id,
        any(coalesce(nullIf(acronym, ''), name)) AS venue_name
    FROM global_markets.stocks_exchanges
    WHERE asset_class = 'stocks'
    GROUP BY exchange_id
)
SELECT
    if(v.venue_name = '', concat('Venue ', toString(t.exchange)), v.venue_name) AS venue,
    count()                                                                     AS print_count,
    round(quantileDeterministic(0.5)(
        toFloat64(toUnixTimestamp64Nano(t.sip_timestamp)
                - toUnixTimestamp64Nano(t.participant_timestamp)) / 1000,
        toUInt64(t.sequence_number)), 1)                                        AS median_lag_us,
    round(quantileDeterministic(0.99)(
        toFloat64(toUnixTimestamp64Nano(t.sip_timestamp)
                - toUnixTimestamp64Nano(t.participant_timestamp)) / 1000,
        toUInt64(t.sequence_number)), 1)                                        AS p99_lag_us
FROM global_markets.stocks_trades AS t
LEFT JOIN venues AS v ON v.exchange_id = toUInt32(t.exchange)
WHERE t.ticker = 'AAPL'
  AND t.sip_timestamp >= '2026-06-10 14:30:00'
  AND t.sip_timestamp <  '2026-06-10 15:00:00'
  AND ifNull(toUnixTimestamp64Nano(t.trf_timestamp), 0) = 0
GROUP BY venue
HAVING count() >= 200
ORDER BY median_lag_us DESC
LIMIT 15

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More from this analysisMarket Data Timestamps: SIP vs Exchange Clocks
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