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Top-of-book message rate, minute by minute (NVDA, one hour)

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 2026-08-07, from Level 1 vs Level 2 vs Level 3 Market Data.

as of series 60×3read in context →
Top-of-book message rate, minute by minute (NVDA, one hour) — 60 rows by 3 columns, computed from US exchange, SIP and OPRA data.
et_timeavg_updates_per_secondpeak_updates_in_one_second
10:00163.8371
10:01200.9497
10:02205.7601
10:03183.4428
10:04184.8403
10:05157.1390
10:06196.31355
10:07163442
10:08179.6379
10:09183.6413
10:10208.4586
10:11168.6381
10:12152.3321
10:13134507
10:14127.6267
10:15126.6308
10:16127.1257
10:17132.5663
10:18165.7410
10:19120.8458
10:20170.6449
10:21122.1432
10:22171.3534
10:23146.6433
10:24136.3813
10:25133.2313
10:26117.7306
10:27144.2301
10:28123597
10:29106.1264
10:30116.4321
10:31120.5470
10:32109335
10:33109.4405
10:3496389
10:35125.9362
10:36143.8362
10:37126.1319
10:38123464
10:3996.3264
10:40133651
10:41133.1489
10:42133.9280
10:43116.8307
10:4491.2306
10:45126.9342
10:46119349
10:47399.81434
10:48216.6573
10:49151.5423
10:50148.2291
10:51154.1390
10:52198.2558
10:53142.5312
10:54134.6361
10:55143.1358
10:56130381
10:57113301
10:58188.7500
10:59171.8833
Rows × columns
60 × 3
Period covered
to
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 Top-of-book message rate, minute by minute (NVDA, one hour), derived from the stored result.
ColumnTypeRangeNotes
et_time date 10:00 to 10:59
avg_updates_per_second number 91.2 to 399.8
peak_updates_in_one_second number 257 to 1,434

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
    et_time,
    round(avg(sec_updates), 1) AS avg_updates_per_second,
    max(sec_updates)           AS peak_updates_in_one_second
FROM
(
    SELECT
        formatDateTime(toStartOfMinute(toTimeZone(sip_timestamp, 'America/New_York')), '%H:%i') AS et_time,
        toDateTime(sip_timestamp)                                                              AS et_second,
        count()                                                                                AS sec_updates
    FROM global_markets.cache_stocks_quotes
    WHERE ticker = 'NVDA'
      AND sip_timestamp >= '2026-06-17 14:00:00'
      AND sip_timestamp <  '2026-06-17 15:00:00'
    GROUP BY et_time, et_second
)
GROUP BY et_time
ORDER BY et_time

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