zone_width

Name: zone_width
Creator: Strasmore
Published: 2026-09-27
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 2026-09-27, from support-and-resistance-explained.

as of 2026-09-27 ranking 5×3read in context →

zone_width — 5 rows by 3 columns, computed 2026-09-27 from US exchange, SIP and OPRA data.
distance_band	tests	break_pct
0.00 to 0.25% below	256	81.2
0.25 to 0.50% below	267	68.5
0.50 to 1.00% below	458	64.4
1.00 to 2.00% below	766	49
2.00 to 4.00% below	1054	26

Rows × columns: 5 × 3
Computed: 2026-09-27
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 zone_width, derived from the stored result.
Column	Type	Range	Notes
`distance_band`	text	5 distinct values
`tests`	number	256 to 1,054
`break_pct`	number	26 to 81.2	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 2026-09-27, 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,
        toFloat64(close) AS close_px,
        toFloat64(high)  AS high_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('SPY', 'AAPL', 'MSFT', 'NVDA')
      AND date >= '2019-01-01'
      AND date <  '2026-09-20'
),
levels AS
(
    SELECT
        ticker,
        date,
        close_px,
        max(high_px)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 63 PRECEDING AND 1 PRECEDING) AS swing_high,
        max(close_px) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 FOLLOWING AND 5 FOLLOWING)  AS best_close_next5
    FROM bars
),
tagged AS
(
    SELECT
        100 * (swing_high - close_px) / swing_high AS gap_pct,
        best_close_next5 > swing_high              AS broke
    FROM levels
    WHERE date >= '2019-04-01'
      AND date <  '2026-09-01'
      AND close_px <  swing_high
      AND close_px >= swing_high * 0.96
)
SELECT
    multiIf(gap_pct < 0.25, '0.00 to 0.25% below',
            gap_pct < 0.50, '0.25 to 0.50% below',
            gap_pct < 1.00, '0.50 to 1.00% below',
            gap_pct < 2.00, '1.00 to 2.00% below',
                            '2.00 to 4.00% below') AS distance_band,
    count()                                        AS tests,
    round(100 * countIf(broke) / count(), 1)       AS break_pct
FROM tagged
GROUP BY distance_band
ORDER BY distance_band

SQL to run

WITH bars AS
(
    SELECT
        ticker,
        date,
        toFloat64(close) AS close_px,
        toFloat64(high)  AS high_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('SPY', 'AAPL', 'MSFT', 'NVDA')
      AND date >= '2019-01-01'
      AND date <  '2026-09-20'
),
levels AS
(
    SELECT
        ticker,
        date,
        close_px,
        max(high_px)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 63 PRECEDING AND 1 PRECEDING) AS swing_high,
        max(close_px) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 FOLLOWING AND 5 FOLLOWING)  AS best_close_next5
    FROM bars
),
tagged AS
(
    SELECT
        100 * (swing_high - close_px) / swing_high AS gap_pct,
        best_close_next5 > swing_high              AS broke
    FROM levels
    WHERE date >= '2019-04-01'
      AND date <  '2026-09-01'
      AND close_px <  swing_high
      AND close_px >= swing_high * 0.96
)
SELECT
    multiIf(gap_pct < 0.25, '0.00 to 0.25% below',
            gap_pct < 0.50, '0.25 to 0.50% below',
            gap_pct < 1.00, '0.50 to 1.00% below',
            gap_pct < 2.00, '1.00 to 2.00% below',
                            '2.00 to 4.00% below') AS distance_band,
    count()                                        AS tests,
    round(100 * countIf(broke) / count(), 1)       AS break_pct
FROM tagged
GROUP BY distance_band
ORDER BY distance_band

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