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NBBO updates per session: four heavily traded names vs. two thin small caps

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 2026-08-22, from What Is the NBBO? National Best Bid and Offer.

as of series 6×5read in context →
NBBO updates per session: four heavily traded names vs. two thin small caps — 6 rows by 5 columns, computed from US exchange, SIP and OPRA data.
tickersessionsavg_updates_per_sessionavg_updates_per_session_mmultiple_of_quietest
SPY520572252.057866
NVDA515339051.534645
AAPL57974590.797336
KO53372450.337142
SENEA550830.0052
NATH523770.0021
Rows × columns
6 × 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 NBBO updates per session: four heavily traded names vs. two thin small caps, derived from the stored result.
ColumnTypeRangeNotes
ticker text 6 distinct values (AAPL, KO, NATH…)
sessions number every row is 5
avg_updates_per_session number 2,377 to 2,057,225
avg_updates_per_session_m number 0.002 to 2.057
multiple_of_quietest number 1 to 866

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 ticker,
       uniqExact(toDate(toTimeZone(sip_timestamp, 'America/New_York'))) AS sessions,
       round(count() / uniqExact(toDate(toTimeZone(sip_timestamp, 'America/New_York')))) AS avg_updates_per_session,
       round(count() / uniqExact(toDate(toTimeZone(sip_timestamp, 'America/New_York'))) / 1e6, 3) AS avg_updates_per_session_m,
       round((count() / uniqExact(toDate(toTimeZone(sip_timestamp, 'America/New_York')))) / min(count() / uniqExact(toDate(toTimeZone(sip_timestamp, 'America/New_York')))) OVER (), 0) AS multiple_of_quietest
FROM global_markets.cache_stocks_quotes
WHERE ticker IN ('SPY', 'NVDA', 'AAPL', 'KO', 'SENEA', 'NATH')
  AND sip_timestamp >= toDateTime(today() - 10)
  AND sip_timestamp < toDateTime(today() - 3)
  AND toDate(toTimeZone(sip_timestamp, 'America/New_York')) IN (
      SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS session_date
      FROM global_markets.delayed_stocks_minute_aggs
      WHERE ticker = 'SPY'
        AND window_start >= toDateTime(today() - 10)
        AND window_start < toDateTime(today() - 3)
      GROUP BY session_date
      HAVING countIf((toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) = 390
  )
GROUP BY ticker
ORDER BY indexOf(['SPY', 'NVDA', 'AAPL', 'KO', 'SENEA', 'NATH'], ticker)

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More from this analysisWhat Is the NBBO? National Best Bid and Offer
AAPL: average NBBO updates per minute by half-hour bucket (ET, 4 a.m. to 8 p.m.) series 32×3 → GME, 2024-05-14: NBBO updates and trades per minute across a five-minute LULD pause series 15×3 → KO: ten consecutive NBBO updates from 1:30 p.m. ET on a recent session series 10×7 → The smallest size the NBBO will show: six names across the price tiers ranking 6×4 → Exchange stamp to SIP stamp: the consolidation step, in microseconds ranking 4×3 → AAPL median quoted spread by 30-minute bucket (ET, extended hours included) series 32×2 → See all 2,170 queries →