range_after_hike

Name: range_after_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-hike.

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

range_after_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 range_after_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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