cushion_outcomes

Name: cushion_outcomes
Creator: Strasmore
Published: 2026-10-02
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 2026-10-02, from why-would-anyone-sell-a-put-option.

as of 2026-10-02 table 4×6read in context →

cushion_outcomes — 4 rows by 6 columns, computed 2026-10-02 from US exchange, SIP and OPRA data.
strike_cushion	expired_worthless_pct	avg_loss_when_itm_pct	avg_loss_all_windows_pct	worst_window_pct	sample
2% below spot	68.2	4.99	1.59	22.2	1418 windows
5% below spot	77.3	3.36	0.76	19.2	1418 windows
10% below spot	95.1	2.73	0.13	14.2	1418 windows
15% below spot	99.4	3.42	0.02	9.2	1418 windows

Rows × columns: 4 × 6
Computed: 2026-10-02
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 cushion_outcomes, derived from the stored result.
Column	Type	Range	Notes
`strike_cushion`	text	4 distinct values
`expired_worthless_pct`	number	68.2 to 99.4	percent
`avg_loss_when_itm_pct`	number	2.73 to 4.99	percent
`avg_loss_all_windows_pct`	number	0.02 to 1.59	percent
`worst_window_pct`	number	9.2 to 22.2	percent
`sample`	text	1 distinct value (1418 windows)

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 2026-10-02, 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.

SELECT
    concat(toString(cushion_pct), '% below spot')                                      AS strike_cushion,
    round(100 * countIf(move_pct > -1 * cushion_pct) / count(), 1)                     AS expired_worthless_pct,
    round(avgIf(-1 * (move_pct + cushion_pct), move_pct <= -1 * cushion_pct), 2)       AS avg_loss_when_itm_pct,
    round(avg(if(move_pct <= -1 * cushion_pct, -1 * (move_pct + cushion_pct), 0)), 2)  AS avg_loss_all_windows_pct,
    round(-1 * min(move_pct) - cushion_pct, 2)                                         AS worst_window_pct,
    concat(toString(count()), ' windows')                                              AS sample
FROM
(
    SELECT (end_px / start_px - 1) * 100 AS move_pct
    FROM
    (
        SELECT
            close_px                                                                                 AS start_px,
            leadInFrame(close_px, 21) OVER (ORDER BY date ROWS BETWEEN CURRENT ROW AND 21 FOLLOWING) AS end_px
        FROM
        (
            SELECT
                date,
                max(toFloat64(close)) AS close_px
            FROM global_markets.stocks_daily_aggs
            WHERE ticker = 'AAPL'
              AND date BETWEEN '2021-01-04' AND '2026-09-25'
            GROUP BY date
        ) AS daily
    ) AS shifted
    WHERE end_px > 0
) AS windows
CROSS JOIN
(
    SELECT arrayJoin([2, 5, 10, 15]) AS cushion_pct
) AS cushions
GROUP BY cushion_pct
HAVING countIf(move_pct <= -1 * cushion_pct) > 0
ORDER BY cushion_pct

SQL to run

SELECT
    concat(toString(cushion_pct), '% below spot')                                      AS strike_cushion,
    round(100 * countIf(move_pct > -1 * cushion_pct) / count(), 1)                     AS expired_worthless_pct,
    round(avgIf(-1 * (move_pct + cushion_pct), move_pct <= -1 * cushion_pct), 2)       AS avg_loss_when_itm_pct,
    round(avg(if(move_pct <= -1 * cushion_pct, -1 * (move_pct + cushion_pct), 0)), 2)  AS avg_loss_all_windows_pct,
    round(-1 * min(move_pct) - cushion_pct, 2)                                         AS worst_window_pct,
    concat(toString(count()), ' windows')                                              AS sample
FROM
(
    SELECT (end_px / start_px - 1) * 100 AS move_pct
    FROM
    (
        SELECT
            close_px                                                                                 AS start_px,
            leadInFrame(close_px, 21) OVER (ORDER BY date ROWS BETWEEN CURRENT ROW AND 21 FOLLOWING) AS end_px
        FROM
        (
            SELECT
                date,
                max(toFloat64(close)) AS close_px
            FROM global_markets.stocks_daily_aggs
            WHERE ticker = 'AAPL'
              AND date BETWEEN '2021-01-04' AND '2026-09-25'
            GROUP BY date
        ) AS daily
    ) AS shifted
    WHERE end_px > 0
) AS windows
CROSS JOIN
(
    SELECT arrayJoin([2, 5, 10, 15]) AS cushion_pct
) AS cushions
GROUP BY cushion_pct
HAVING countIf(move_pct <= -1 * cushion_pct) > 0
ORDER BY cushion_pct

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