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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?.

as of series 6×5read in context →
Overnight gaps by ticker, H1 2026: average absolute gap and the single biggest gap — 6 rows by 5 columns, computed from US exchange, SIP and OPRA data.
tickersessionsavg_abs_gap_pctbiggest_gap_pctbiggest_gap_day
SPY1230.452.6April 8, 2026
KO1230.425.41April 28, 2026
AAPL1230.532.82May 1, 2026
MSFT1230.89-8.66January 29, 2026
NVDA1230.993.6April 8, 2026
TSLA1231.054.97April 8, 2026
Rows × columns
6 × 5
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 Overnight gaps by ticker, H1 2026: average absolute gap and the single biggest gap, derived from the stored result.
ColumnTypeRangeNotes
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 date April 28, to May 1, 202

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,
       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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