STRASMORE/EXPLORE 3,256 QUERIES

gap_by_month

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.

as of series 24×4read in context →
gap_by_month — 24 rows by 4 columns, computed from US exchange, SIP and OPRA data.
monthsession_countgap_over_0_5_pctgap_over_1_pct
2024-101764217.6
2024-1116040.616.2
2024-1216827.411.3
2025-0116051.228.1
2025-0215240.116.4
2025-0316850.630.4
2025-0416867.948.2
2025-0516856.530.4
2025-0616035.613.1
2025-0717630.75.1
2025-0816824.49.5
2025-0916826.813.7
2025-1018439.717.4
2025-1115239.519.1
2025-1217622.78
2026-0116046.220.6
2026-0215241.416.4
2026-0317659.724.4
2026-0416853.626.2
2026-0516050.618.1
2026-0616858.928.6
2026-0717664.236.4
2026-0816844.622.6
2026-0916854.228
Rows × columns
24 × 4
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 gap_by_month, derived from the stored result.
ColumnTypeRangeNotes
month text 24 distinct values (2024-10, 2024-11, 2024-12…)
session_count number 152 to 184 count
gap_over_0_5_pct number 22.7 to 67.9 percent
gap_over_1_pct number 5.1 to 48.2 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
        date,
        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
)
SELECT
    formatDateTime(toStartOfMonth(date), '%Y-%m')                            AS month,
    count()                                                                  AS session_count,
    round(100 * countIf(abs(open_px / prev_close - 1) > 0.005) / count(), 1) AS gap_over_0_5_pct,
    round(100 * countIf(abs(open_px / prev_close - 1) > 0.01) / count(), 1)  AS gap_over_1_pct
FROM gaps
WHERE prev_close > 0
  AND open_px > 0
GROUP BY month
ORDER BY month
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