Lowest and highest SPY close in the twelve months after each first hike

Name: Lowest and highest SPY close in the twelve months after each first hike
Creator: Strasmore
Published: 2026-09-20
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 2026-09-20, from Stock Returns After the First Fed Rate Hike.

as of 2026-09-20 table 3×5read in context →

Lowest and highest SPY close in the twelve months after each first hike — 3 rows by 5 columns, computed 2026-09-20 from US exchange, SIP and OPRA data.
cycle	low_vs_hike_pct	low_on	high_vs_hike_pct	high_on
Jun 30, 2004	-6.7	Aug 6, 2004	7.2	Mar 7, 2005
Dec 16, 2015	-12.1	Feb 11, 2016	9.5	Dec 13, 2016
Mar 16, 2022	-18.1	Oct 12, 2022	6	Mar 29, 2022

Rows × columns: 3 × 5
Computed: 2026-09-20
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 Lowest and highest SPY close in the twelve months after each first hike, derived from the stored result.
Column	Type	Range	Notes
`cycle`	text	3 distinct values (Dec 16, 2015, Jun 30, 2004, Mar 16, 2022)
`low_vs_hike_pct`	number	-18.1 to -6.7	percent
`low_on`	text	3 distinct values (Aug 6, 2004, Feb 11, 2016, Oct 12, 2022)
`high_vs_hike_pct`	number	6 to 9.5	percent
`high_on`	text	3 distinct values (Dec 13, 2016, Mar 29, 2022, Mar 7, 2005)

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 2026-09-20, 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 px AS
(
    SELECT
        date,
        toFloat64(close) AS px_close,
        multiIf(date <= '2005-06-30', toDate('2004-06-30'),
                date <= '2016-12-16', toDate('2015-12-16'),
                                      toDate('2022-03-16')) AS hike_date
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND ((date >= '2004-06-30' AND date <= '2005-06-30')
        OR (date >= '2015-12-16' AND date <= '2016-12-16')
        OR (date >= '2022-03-16' AND date <= '2023-03-16'))
)
SELECT
    formatDateTime(hike_date, '%b %e, %Y')                                AS cycle,
    round(100 * (min(px_close) / argMin(px_close, date) - 1), 1)          AS low_vs_hike_pct,
    formatDateTime(argMin(date, px_close), '%b %e, %Y')                   AS low_on,
    round(100 * (max(px_close) / argMin(px_close, date) - 1), 1)          AS high_vs_hike_pct,
    formatDateTime(argMax(date, px_close), '%b %e, %Y')                   AS high_on
FROM px
GROUP BY hike_date
ORDER BY hike_date

SQL to run

WITH px AS
(
    SELECT
        date,
        toFloat64(close) AS px_close,
        multiIf(date <= '2005-06-30', toDate('2004-06-30'),
                date <= '2016-12-16', toDate('2015-12-16'),
                                      toDate('2022-03-16')) AS hike_date
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND ((date >= '2004-06-30' AND date <= '2005-06-30')
        OR (date >= '2015-12-16' AND date <= '2016-12-16')
        OR (date >= '2022-03-16' AND date <= '2023-03-16'))
)
SELECT
    formatDateTime(hike_date, '%b %e, %Y')                                AS cycle,
    round(100 * (min(px_close) / argMin(px_close, date) - 1), 1)          AS low_vs_hike_pct,
    formatDateTime(argMin(date, px_close), '%b %e, %Y')                   AS low_on,
    round(100 * (max(px_close) / argMin(px_close, date) - 1), 1)          AS high_vs_hike_pct,
    formatDateTime(argMax(date, px_close), '%b %e, %Y')                   AS high_on
FROM px
GROUP BY hike_date
ORDER BY hike_date

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