breaks_by_year

Name: breaks_by_year
Creator: Strasmore
Published: 2026-10-01
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 2026-10-01, from buy-side-vs-sell-side-liquidity.

as of 2026-10-01 table 12×6read in context →

breaks_by_year — 12 rows by 6 columns, computed 2026-10-01 from US exchange, SIP and OPRA data.
year	sessions	cleared_prior_high	closed_back_below	rejection_rate_pct	period_avg_rejection_pct
2015	251	27	12	44.4	39.9
2016	252	53	19	35.8	39.9
2017	251	66	28	42.4	39.9
2018	251	40	10	25	39.9
2019	252	75	29	38.7	39.9
2020	253	71	26	36.6	39.9
2021	252	84	31	36.9	39.9
2022	251	33	13	39.4	39.9
2023	250	58	28	48.3	39.9
2024	252	73	37	50.7	39.9
2025	250	69	29	42	39.9
2026	187	33	10	30.3	39.9

Rows × columns: 12 × 6
Computed: 2026-10-01
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 breaks_by_year, derived from the stored result.
Column	Type	Range	Notes
`year`	text	12 distinct values (2015, 2016, 2017…)
`sessions`	number	187 to 253
`cleared_prior_high`	number	27 to 84	US dollars
`closed_back_below`	number	10 to 37
`rejection_rate_pct`	number	25 to 50.7	percent
`period_avg_rejection_pct`	number	every row is 39.9	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 2026-10-01, 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
        date,
        max(toFloat64(high))  AS day_high,
        max(toFloat64(close)) AS day_close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND date >= '2015-01-01'
      AND date <  '2026-10-01'
    GROUP BY date
),
flagged AS
(
    SELECT
        date,
        day_high,
        day_close,
        max(day_high) OVER (ORDER BY date ASC ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING) AS prior_high
    FROM bars
),
yearly AS
(
    SELECT
        toString(toYear(date))                                    AS year,
        count()                                                   AS sessions,
        countIf(day_high > prior_high)                            AS cleared_prior_high,
        countIf(day_high > prior_high AND day_close < prior_high)  AS closed_back_below
    FROM flagged
    WHERE prior_high > 0
    GROUP BY year
)
SELECT
    year,
    sessions,
    cleared_prior_high,
    closed_back_below,
    round(100 * closed_back_below / cleared_prior_high, 1)                           AS rejection_rate_pct,
    round(100 * sum(closed_back_below) OVER () / sum(cleared_prior_high) OVER (), 1) AS period_avg_rejection_pct
FROM yearly
ORDER BY year

SQL to run

WITH bars AS
(
    SELECT
        date,
        max(toFloat64(high))  AS day_high,
        max(toFloat64(close)) AS day_close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND date >= '2015-01-01'
      AND date <  '2026-10-01'
    GROUP BY date
),
flagged AS
(
    SELECT
        date,
        day_high,
        day_close,
        max(day_high) OVER (ORDER BY date ASC ROWS BETWEEN 20 PRECEDING AND 1 PRECEDING) AS prior_high
    FROM bars
),
yearly AS
(
    SELECT
        toString(toYear(date))                                    AS year,
        count()                                                   AS sessions,
        countIf(day_high > prior_high)                            AS cleared_prior_high,
        countIf(day_high > prior_high AND day_close < prior_high)  AS closed_back_below
    FROM flagged
    WHERE prior_high > 0
    GROUP BY year
)
SELECT
    year,
    sessions,
    cleared_prior_high,
    closed_back_below,
    round(100 * closed_back_below / cleared_prior_high, 1)                           AS rejection_rate_pct,
    round(100 * sum(closed_back_below) OVER () / sum(cleared_prior_high) OVER (), 1) AS period_avg_rejection_pct
FROM yearly
ORDER BY year

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