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.
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
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
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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