gap_size_buckets
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 2026-10-09, from nse-pre-open-session-explained.
| gap_bucket | session_count | share_pct |
|---|---|---|
| 0.00-0.25% | 1318 | 33 |
| 0.25-0.50% | 899 | 22.5 |
| 0.50-1.00% | 939 | 23.5 |
| 1.00-2.00% | 583 | 14.6 |
| 2.00%+ | 261 | 6.5 |
- Rows × columns
- 5 × 3
- 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 |
|---|---|---|---|
gap_bucket |
text | 5 distinct values (0.00-0.25%, 0.25-0.50%, 0.50-1.00%…) | |
session_count |
number | 261 to 1,318 | count |
share_pct |
number | 6.5 to 33 | 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 bars AS
(
SELECT
ticker,
date,
argMax(toFloat64(open), _ingest_time) AS open_px,
argMax(toFloat64(close), _ingest_time) AS close_px
FROM global_markets.stocks_daily_aggs
WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'AMZN', 'JPM', 'JNJ', 'KO', 'XOM')
AND date >= '2024-10-01'
AND date < '2026-10-01'
GROUP BY ticker, date
),
gaps AS
(
SELECT
open_px,
lagInFrame(close_px, 1) OVER (PARTITION BY ticker ORDER BY date ASC
ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
FROM bars
),
bucketed AS
(
SELECT
multiIf(
abs(open_px / prev_close - 1) <= 0.0025, '0.00-0.25%',
abs(open_px / prev_close - 1) <= 0.005, '0.25-0.50%',
abs(open_px / prev_close - 1) <= 0.01, '0.50-1.00%',
abs(open_px / prev_close - 1) <= 0.02, '1.00-2.00%',
'2.00%+') AS gap_bucket,
count() AS session_count
FROM gaps
WHERE prev_close > 0
AND open_px > 0
GROUP BY gap_bucket
)
SELECT
gap_bucket,
session_count,
round(100 * session_count / sum(session_count) OVER (), 1) AS share_pct
FROM bucketed
ORDER BY gap_bucket
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