break_rate

Name: break_rate
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 4×4read in context →

break_rate — 4 rows by 4 columns, computed 2026-09-27 from US exchange, SIP and OPRA data.
ticker	tests	closed_above_after	break_pct
NVDA	41	34	82.9
AAPL	78	62	79.5
MSFT	106	82	77.4
SPY	301	216	71.8

Rows × columns: 4 × 4
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 break_rate, derived from the stored result.
Column	Type	Range	Notes
`ticker`	text	4 distinct values (AAPL, MSFT, NVDA…)
`tests`	number	41 to 301
`closed_above_after`	number	34 to 216
`break_pct`	number	71.8 to 82.9	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
)
SELECT
    ticker,
    count()                                                         AS tests,
    countIf(best_close_next5 > swing_high)                          AS closed_above_after,
    round(100 * countIf(best_close_next5 > swing_high) / count(), 1) AS break_pct
FROM levels
WHERE date >= '2019-04-01'
  AND date <  '2026-09-01'
  AND close_px <= swing_high
  AND close_px >= swing_high * 0.995
GROUP BY ticker
ORDER BY break_pct DESC

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
)
SELECT
    ticker,
    count()                                                         AS tests,
    countIf(best_close_next5 > swing_high)                          AS closed_above_after,
    round(100 * countIf(best_close_next5 > swing_high) / count(), 1) AS break_pct
FROM levels
WHERE date >= '2019-04-01'
  AND date <  '2026-09-01'
  AND close_px <= swing_high
  AND close_px >= swing_high * 0.995
GROUP BY ticker
ORDER BY break_pct DESC

⌘/Ctrl + Enter

Use dis data for your AI assistant

E go open ready to query, with dis page data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent