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.
| ticker | median_days_between | closest_pair_days | widest_pair_days | median_move_pct |
|---|---|---|---|---|
| WMT | 93 | 38 | 188 | 3.91 |
| MSFT | 89 | 3 | 93 | 5.6 |
| COST | 69 | 1 | 214 | 2.6 |
| JNJ | 69 | 2 | 189 | 2.27 |
| KO | 51 | 4 | 197 | 3.04 |
| AAPL | 10 | 3 | 179 | 5.11 |
| NVDA | 8 | 1 | 175 | 6.49 |
- Rows × columns
- 7 × 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 | 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.
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.
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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