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Spacing between each name's eight biggest overnight moves, July 2024 to June 2026

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 2026-08-03, from How to Find a Stock's Earnings Date.

as of table 7×5read in context →
Spacing between each name's eight biggest overnight moves, July 2024 to June 2026 — 7 rows by 5 columns, computed from US exchange, SIP and OPRA data.
tickermedian_days_betweenclosest_pair_dayswidest_pair_daysmedian_move_pct
WMT93381883.91
MSFT893935.6
COST6912142.6
JNJ6921892.27
KO5141973.04
AAPL1031795.11
NVDA811756.49
Rows × columns
7 × 5
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 Spacing between each name's eight biggest overnight moves, July 2024 to June 2026, derived from the stored result.
ColumnTypeRangeNotes
ticker text 7 distinct values (AAPL, COST, JNJ…)
median_days_between number 8 to 93
closest_pair_days number 1 to 38
widest_pair_days number 93 to 214
median_move_pct number 2.27 to 6.49 percent

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 daily AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS session,
           argMin(close, window_start) AS first_price,
           argMax(close, window_start) AS last_price
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'WMT', 'COST', 'KO', 'JNJ')
      AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2024-07-01')
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-06-30')
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, session
),
overnight AS (
    SELECT ticker,
           session,
           first_price,
           lagInFrame(last_price) OVER (PARTITION BY ticker ORDER BY session
                                        ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prior_close
    FROM daily
),
moves AS (
    SELECT ticker,
           session,
           abs(toFloat64(first_price) / toFloat64(prior_close) - 1) * 100 AS abs_gap_pct
    FROM overnight
    WHERE toFloat64(prior_close) > 0
      AND abs(toFloat64(first_price) / toFloat64(prior_close) - 1) < 0.3
),
biggest AS (
    SELECT ticker,
           session,
           abs_gap_pct,
           row_number() OVER (PARTITION BY ticker ORDER BY abs_gap_pct DESC) AS rk
    FROM moves
),
spacing AS (
    SELECT ticker,
           session,
           abs_gap_pct,
           dateDiff('day',
                    lagInFrame(session) OVER (PARTITION BY ticker ORDER BY session
                                              ROWS BETWEEN 1 PRECEDING AND CURRENT ROW),
                    session) AS days_between
    FROM biggest
    WHERE rk <= 8
)
SELECT ticker,
       round(quantileDeterministic(0.5)(toFloat64(days_between), cityHash64(session)), 0) AS median_days_between,
       min(days_between) AS closest_pair_days,
       max(days_between) AS widest_pair_days,
       round(quantileDeterministic(0.5)(abs_gap_pct, cityHash64(session)), 2) AS median_move_pct
FROM spacing
WHERE days_between BETWEEN 1 AND 400
GROUP BY ticker
ORDER BY median_days_between DESC

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