AAPL 25-session moves since January 2021, by size

Name: AAPL 25-session moves since January 2021, by size
Creator: Strasmore
Published: 2026-08-13
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 2026-08-13, from Ratio Spreads: Breakevens and Naked Risk.

as of 2026-08-13 ranking 6×3read in context →

AAPL 25-session moves since January 2021, by size — 6 rows by 3 columns, computed 2026-08-13 from US exchange, SIP and OPRA data.
move_bucket	window_count	share_of_windows_pct
down more than 5%	299	21.7
down 0 to 5%	272	19.8
up 0 to 5%	292	21.2
up 5 to 10%	283	20.6
up 10 to 15%	149	10.8
up more than 15%	80	5.8

Rows × columns: 6 × 3
Computed: 2026-08-13
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 AAPL 25-session moves since January 2021, by size, derived from the stored result.
Column	Type	Range	Notes
`move_bucket`	text	6 distinct values
`window_count`	number	80 to 299	count
`share_of_windows_pct`	number	5.8 to 21.7	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 2026-08-13, 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
    daily AS
    (
        SELECT
            date,
            toFloat64(max(close)) AS close_px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'AAPL'
          AND date >= '2021-01-04'
          AND date <= '2026-07-31'
        GROUP BY date
    ),
    indexed AS
    (
        SELECT
            date,
            close_px,
            row_number() OVER (ORDER BY date) AS n
        FROM daily
    ),
    moves AS
    (
        SELECT 100 * (b.close_px / a.close_px - 1) AS fwd_move_pct
        FROM indexed AS a
        INNER JOIN indexed AS b ON b.n = a.n + 25
    ),
    totals AS
    (
        SELECT count() AS all_windows FROM moves
    )
SELECT
    multiIf(
        fwd_move_pct < -5,  'down more than 5%',
        fwd_move_pct <  0,  'down 0 to 5%',
        fwd_move_pct <  5,  'up 0 to 5%',
        fwd_move_pct < 10,  'up 5 to 10%',
        fwd_move_pct < 15,  'up 10 to 15%',
                            'up more than 15%')  AS move_bucket,
    count()                                       AS window_count,
    round(100 * count() / any(t.all_windows), 1)  AS share_of_windows_pct
FROM moves AS m
CROSS JOIN totals AS t
GROUP BY move_bucket
ORDER BY min(m.fwd_move_pct)

SQL to run

WITH
    daily AS
    (
        SELECT
            date,
            toFloat64(max(close)) AS close_px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'AAPL'
          AND date >= '2021-01-04'
          AND date <= '2026-07-31'
        GROUP BY date
    ),
    indexed AS
    (
        SELECT
            date,
            close_px,
            row_number() OVER (ORDER BY date) AS n
        FROM daily
    ),
    moves AS
    (
        SELECT 100 * (b.close_px / a.close_px - 1) AS fwd_move_pct
        FROM indexed AS a
        INNER JOIN indexed AS b ON b.n = a.n + 25
    ),
    totals AS
    (
        SELECT count() AS all_windows FROM moves
    )
SELECT
    multiIf(
        fwd_move_pct < -5,  'down more than 5%',
        fwd_move_pct <  0,  'down 0 to 5%',
        fwd_move_pct <  5,  'up 0 to 5%',
        fwd_move_pct < 10,  'up 5 to 10%',
        fwd_move_pct < 15,  'up 10 to 15%',
                            'up more than 15%')  AS move_bucket,
    count()                                       AS window_count,
    round(100 * count() / any(t.all_windows), 1)  AS share_of_windows_pct
FROM moves AS m
CROSS JOIN totals AS t
GROUP BY move_bucket
ORDER BY min(m.fwd_move_pct)

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