Overnight gaps by ticker, H1 2026: average absolute gap and the single biggest gap
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 Why Do Stocks Gap Up or Down Overnight?.
| ticker | sessions | avg_abs_gap_pct | biggest_gap_pct | biggest_gap_day |
|---|---|---|---|---|
| SPY | 123 | 0.45 | 2.6 | April 8, 2026 |
| KO | 123 | 0.42 | 5.41 | April 28, 2026 |
| AAPL | 123 | 0.53 | 2.82 | May 1, 2026 |
| MSFT | 123 | 0.89 | -8.66 | January 29, 2026 |
| NVDA | 123 | 0.99 | 3.6 | April 8, 2026 |
| TSLA | 123 | 1.05 | 4.97 | April 8, 2026 |
- Rows × columns
- 6 × 5
- 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 |
|---|---|---|---|
ticker |
text | 6 distinct values (AAPL, KO, MSFT…) | |
sessions |
number | every row is 123 | |
avg_abs_gap_pct |
number | 0.42 to 1.05 | percent |
biggest_gap_pct |
number | -8.66 to 5.41 | percent |
biggest_gap_day |
text | 4 distinct values |
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.
Run it yourself
This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.
SELECT ticker,
count() AS sessions,
round(avg(abs(gap_pct)), 2) AS avg_abs_gap_pct,
round(argMax(gap_pct, (abs(gap_pct), day)), 2) AS biggest_gap_pct,
concat(monthName(argMax(day, (abs(gap_pct), day))), ' ', toString(toDayOfMonth(argMax(day, (abs(gap_pct), day)))), ', ', toString(toYear(argMax(day, (abs(gap_pct), day))))) AS biggest_gap_day
FROM (
SELECT ticker, day, 100 * (rth_open - prior_close) / prior_close AS gap_pct
FROM (
SELECT ticker, day, rth_open,
lagInFrame(rth_close) OVER (PARTITION BY ticker ORDER BY day) AS prior_close
FROM (
SELECT ticker, toDate(toTimeZone(window_start, 'America/New_York')) AS day,
argMinIf(toFloat64(open), window_start, rth) AS rth_open,
argMaxIf(toFloat64(close), window_start, rth) AS rth_close
FROM (
SELECT ticker, window_start, open, close,
toTimeZone(window_start, 'America/New_York') >= toDateTime(concat(toString(toDate(toTimeZone(window_start, 'America/New_York'))), ' 09:30:00'), 'America/New_York')
AND toTimeZone(window_start, 'America/New_York') < toDateTime(concat(toString(toDate(toTimeZone(window_start, 'America/New_York'))), ' 16:00:00'), 'America/New_York') AS rth
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY', 'KO', 'AAPL', 'MSFT', 'NVDA', 'TSLA')
AND window_start >= '2025-12-29 04:00:00'
AND window_start < '2026-07-01 08:00:00'
)
GROUP BY ticker, day
)
)
WHERE day >= '2026-01-01' AND prior_close > 0 AND isFinite(prior_close)
)
GROUP BY ticker
ORDER BY indexOf(['SPY', 'KO', 'AAPL', 'MSFT', 'NVDA', 'TSLA'], ticker)
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