Weekend gaps: prior close to next open, six widely held names, August 2024 to July 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 2026-08-01, from How Risky Is Options Trading? The Mechanics.
| ticker | gap_count | median_weekend_gap_pct | p95_weekend_gap_pct | largest_weekend_gap_pct |
|---|---|---|---|---|
| TSLA | 104 | 1.44 | 6.46 | 10.81 |
| NVDA | 104 | 1.17 | 4.29 | 14.19 |
| AAPL | 104 | 0.44 | 2.58 | 9.42 |
| MSFT | 104 | 0.51 | 1.9 | 4.73 |
| SPY | 104 | 0.38 | 1.51 | 4 |
| KO | 104 | 0.24 | 0.98 | 5.3 |
- 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…) | |
gap_count |
number | every row is 104 | count |
median_weekend_gap_pct |
number | 0.24 to 1.44 | percent |
p95_weekend_gap_pct |
number | 0.98 to 6.46 | percent |
largest_weekend_gap_pct |
number | 4 to 14.19 | 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 sessions AS (
SELECT ticker,
toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
argMin(toFloat64(open), window_start) AS session_open,
argMax(toFloat64(close), window_start) AS session_close
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'TSLA', 'KO', 'SPY')
AND window_start >= toDateTime('2024-08-01 00:00:00')
AND window_start < toDateTime('2026-08-01 00:00:00')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY ticker, session_date
),
linked AS (
SELECT ticker,
session_date,
session_open,
any(session_close) OVER (PARTITION BY ticker ORDER BY session_date
ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prev_close,
any(session_date) OVER (PARTITION BY ticker ORDER BY session_date
ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prev_date
FROM sessions
),
gaps AS (
SELECT ticker,
session_date,
abs(session_open / prev_close - 1) * 100 AS gap_pct
FROM linked
WHERE prev_close > 0
AND dateDiff('day', prev_date, session_date) >= 3
)
SELECT ticker,
count() AS gap_count,
round(quantileDeterministic(0.5)(gap_pct, cityHash64(session_date)), 2) AS median_weekend_gap_pct,
round(quantileDeterministic(0.95)(gap_pct, cityHash64(session_date)), 2) AS p95_weekend_gap_pct,
round(max(gap_pct), 2) AS largest_weekend_gap_pct
FROM gaps
GROUP BY ticker
ORDER BY p95_weekend_gap_pct DESC
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