STRASMORE/EXPLORE 2,170 QUERIES

SPY under the microscope: nanosecond gaps, the spread two ways, quote quality

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 2026-07-26, from Microstructure Deep-Dive: June 29, 2026.

as of scalar 1×12read in context →
spy updates m
3.98
busiest second et
10:15:43
busiest second in 1015
1
busiest second updates
1,461
min gap between updates ns
1,375
median gap us
245.6
identical timestamp pairs
75,138
time weighted avg spread cents
2.34
per update avg spread cents
2.72
per update premium cents
0.38
crossed updates
1,544
zero bid or ask updates
0
Rows × columns
1 × 12
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 SPY under the microscope: nanosecond gaps, the spread two ways, quote quality, derived from the stored result.
ColumnTypeRangeNotes
spy_updates_m number every row is 3.98
busiest_second_et text 1 distinct value (10:15:43)
busiest_second_in_1015 number every row is 1
busiest_second_updates number every row is 1,461
min_gap_between_updates_ns number every row is 1,375
median_gap_us number every row is 245.6
identical_timestamp_pairs number every row is 75,138
time_weighted_avg_spread_cents number every row is 2.34
per_update_avg_spread_cents number every row is 2.72
per_update_premium_cents number every row is 0.38 US dollars
crossed_updates number every row is 1,544
zero_bid_or_ask_updates number every row is 0

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
    (
        SELECT (formatDateTime(toTimeZone(toStartOfSecond(sip_timestamp), 'America/New_York'), '%H:%i:%S'), count())
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'SPY' AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 20:00:00'
        GROUP BY toStartOfSecond(sip_timestamp)
        ORDER BY count() DESC, toStartOfSecond(sip_timestamp) ASC
        LIMIT 1
    ) AS busiest_sec,
    ordered AS (
        SELECT
            toFloat64(ask_price - bid_price) AS spread,
            bid_price,
            ask_price,
            toFloat64(sip_timestamp - lagInFrame(sip_timestamp) OVER (ORDER BY sip_timestamp, sequence_number)) AS gap_s,
            greatest(toFloat64(least(leadInFrame(sip_timestamp, 1, toDateTime64('2026-06-29 20:00:00', 9)) OVER (ORDER BY sip_timestamp, sequence_number ROWS BETWEEN CURRENT ROW AND 1 FOLLOWING), toDateTime64('2026-06-29 20:00:00', 9)) - sip_timestamp), 0) AS dwell_s,
            rowNumberInAllBlocks() AS rn
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'SPY' AND sip_timestamp >= '2026-06-29 13:30:00' AND sip_timestamp < '2026-06-29 20:00:00'
    )
SELECT
    round(count() / 1e6, 2) AS spy_updates_m,
    busiest_sec.1 AS busiest_second_et,
    toUInt8(startsWith(busiest_sec.1, '10:15')) AS busiest_second_in_1015,
    busiest_sec.2 AS busiest_second_updates,
    round(minIf(gap_s, rn > 0 AND gap_s > 0) * 1e9) AS min_gap_between_updates_ns,
    round(quantileExactIf(0.5)(gap_s, rn > 0) * 1e6, 1) AS median_gap_us,
    countIf(rn > 0 AND gap_s = 0) AS identical_timestamp_pairs,
    round(sumIf(spread * dwell_s, bid_price > 0 AND ask_price >= bid_price) / sumIf(dwell_s, bid_price > 0 AND ask_price >= bid_price) * 100, 2) AS time_weighted_avg_spread_cents,
    round(avgIf(spread, bid_price > 0 AND ask_price >= bid_price) * 100, 2) AS per_update_avg_spread_cents,
    round((avgIf(spread, bid_price > 0 AND ask_price >= bid_price) - sumIf(spread * dwell_s, bid_price > 0 AND ask_price >= bid_price) / sumIf(dwell_s, bid_price > 0 AND ask_price >= bid_price)) * 100, 2) AS per_update_premium_cents,
    countIf(bid_price > 0 AND ask_price > 0 AND ask_price < bid_price) AS crossed_updates,
    countIf(bid_price <= 0 OR ask_price <= 0) AS zero_bid_or_ask_updates
FROM ordered

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