gap_buckets

Name: gap_buckets
Creator: Strasmore
Published: 2026-10-09
License: https://www.strasmore.com/terms/

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 day-trading-strategies-explained.

as of 2026-10-09 ranking 5×4read in context →

gap_buckets — 5 rows by 4 columns, computed 2026-10-09 from US exchange, SIP and OPRA data.
gap_bucket	sessions	median_rvol	median_range_pct
under 0.5%	1043	0.89	1.68
0.5% to 1%	485	0.92	2.08
1% to 2%	317	0.97	2.63
2% to 4%	142	0.97	3.56
4% and up	45	1.38	5.2

Rows × columns: 5 × 4
Computed: 2026-10-09
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_buckets, derived from the stored result.
Column	Type	Range	Notes
`gap_bucket`	text	5 distinct values (0.5% to 1%, 1% to 2%, 2% to 4%…)
`sessions`	number	45 to 1,043
`median_rvol`	number	0.89 to 1.38
`median_range_pct`	number	1.68 to 5.2	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 2026-10-09, 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 dedup AS (
    SELECT
        ticker,
        date,
        toFloat64(any(open))   AS o,
        toFloat64(any(close))  AS c,
        toFloat64(any(high))   AS h,
        toFloat64(any(low))    AS l,
        toFloat64(max(volume)) AS vol
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'SPY', 'KO', 'AMD', 'F', 'T')
      AND date >= today() - 420
      AND date <  today()
    GROUP BY ticker, date
),
windowed AS (
    SELECT
        ticker,
        date,
        o, c, h, l, vol,
        lagInFrame(c, 1) OVER (
            PARTITION BY ticker ORDER BY date
            ROWS BETWEEN 1 PRECEDING AND CURRENT ROW
        ) AS prev_close,
        avg(vol) OVER (
            PARTITION BY ticker ORDER BY date
            ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING
        ) AS base_vol
    FROM dedup
),
gaps AS (
    SELECT
        ticker,
        date,
        abs(o / prev_close - 1) * 100 AS gap_pct,
        vol / base_vol                AS rvol,
        (h - l) / o * 100             AS range_pct
    FROM windowed
    WHERE prev_close > 0
      AND base_vol > 0
      AND o > 0
      AND date >= today() - 370
)
SELECT
    multiIf(gap_pct < 0.5, 'under 0.5%',
            gap_pct < 1.0, '0.5% to 1%',
            gap_pct < 2.0, '1% to 2%',
            gap_pct < 4.0, '2% to 4%',
            '4% and up') AS gap_bucket,
    count()                                                                   AS sessions,
    round(quantileDeterministic(0.5)(rvol, cityHash64(ticker, date)), 2)      AS median_rvol,
    round(quantileDeterministic(0.5)(range_pct, cityHash64(ticker, date)), 2) AS median_range_pct
FROM gaps
GROUP BY gap_bucket
ORDER BY min(gap_pct)

SQL to run

WITH dedup AS (
    SELECT
        ticker,
        date,
        toFloat64(any(open))   AS o,
        toFloat64(any(close))  AS c,
        toFloat64(any(high))   AS h,
        toFloat64(any(low))    AS l,
        toFloat64(max(volume)) AS vol
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('AAPL', 'MSFT', 'NVDA', 'SPY', 'KO', 'AMD', 'F', 'T')
      AND date >= today() - 420
      AND date <  today()
    GROUP BY ticker, date
),
windowed AS (
    SELECT
        ticker,
        date,
        o, c, h, l, vol,
        lagInFrame(c, 1) OVER (
            PARTITION BY ticker ORDER BY date
            ROWS BETWEEN 1 PRECEDING AND CURRENT ROW
        ) AS prev_close,
        avg(vol) OVER (
            PARTITION BY ticker ORDER BY date
            ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING
        ) AS base_vol
    FROM dedup
),
gaps AS (
    SELECT
        ticker,
        date,
        abs(o / prev_close - 1) * 100 AS gap_pct,
        vol / base_vol                AS rvol,
        (h - l) / o * 100             AS range_pct
    FROM windowed
    WHERE prev_close > 0
      AND base_vol > 0
      AND o > 0
      AND date >= today() - 370
)
SELECT
    multiIf(gap_pct < 0.5, 'under 0.5%',
            gap_pct < 1.0, '0.5% to 1%',
            gap_pct < 2.0, '1% to 2%',
            gap_pct < 4.0, '2% to 4%',
            '4% and up') AS gap_bucket,
    count()                                                                   AS sessions,
    round(quantileDeterministic(0.5)(rvol, cityHash64(ticker, date)), 2)      AS median_rvol,
    round(quantileDeterministic(0.5)(range_pct, cityHash64(ticker, date)), 2) AS median_range_pct
FROM gaps
GROUP BY gap_bucket
ORDER BY min(gap_pct)

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