STRASMORE/EXPLORE 2,549 QUERIES

How big a one-day move usually is, sessions since 2016

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 Best Time of Day to Sell a Mutual Fund.

as of ranking 5×2read in context →
How big a one-day move usually is, sessions since 2016 — 5 rows by 2 columns, computed from US exchange, SIP and OPRA data.
move_bucketfrequency_pct
under 0.25%30.4
0.25% to 0.50%19.8
0.50% to 1.00%26
1.00% to 2.00%17.9
over 2.00%5.9
Rows × columns
5 × 2
Computed
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 How big a one-day move usually is, sessions since 2016, derived from the stored result.
ColumnTypeRangeNotes
move_bucket text 5 distinct values
frequency_pct number 5.9 to 30.4 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 , 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
    move_bucket,
    round(count() * 100.0 / sum(count()) OVER (), 1) AS frequency_pct
FROM
(
    SELECT
        multiIf(move_pct < 0.25, 'under 0.25%',
                move_pct < 0.50, '0.25% to 0.50%',
                move_pct < 1.00, '0.50% to 1.00%',
                move_pct < 2.00, '1.00% to 2.00%',
                'over 2.00%') AS move_bucket,
        move_pct
    FROM
    (
        SELECT abs(close_px / prior_close - 1) * 100 AS move_pct
        FROM
        (
            SELECT
                date,
                max(toFloat64(close)) AS close_px,
                lagInFrame(max(toFloat64(close)))
                    OVER (ORDER BY date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prior_close
            FROM global_markets.stocks_daily_aggs
            WHERE ticker = 'SPY'
              AND date >= '2016-01-01'
              AND date <  today() - 2
            GROUP BY date
        )
        WHERE prior_close > 0
    )
)
GROUP BY move_bucket
ORDER BY min(move_pct) ASC
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