STRASMORE/EXPLORE 3,256 QUERIES

move_buckets

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 2026-10-09, from what-is-earnings-season.

as of ranking 6×3read in context →
move_buckets — 6 rows by 3 columns, computed from US exchange, SIP and OPRA data.
labelhit_countshare_pct
0~1%244149
1~2%141428.4
2~3%56611.4
3~5%4138.3
5~8%1002
8% 이상521
Rows × columns
6 × 3
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 move_buckets, derived from the stored result.
ColumnTypeRangeNotes
label text 6 distinct values (0~1%, 1~2%, 2~3%…)
hit_count number 52 to 2,441 count
share_pct number 1 to 49 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.

WITH moves AS
(
    SELECT
        ticker,
        date,
        abs(toFloat64(close)
            / lagInFrame(toFloat64(close)) OVER (PARTITION BY ticker ORDER BY date) - 1) * 100 AS move_pct
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('JPM', 'WFC', 'BAC', 'GS', 'MS', 'NFLX', 'TSLA', 'MSFT', 'AAPL', 'AMZN')
      AND date >= '2024-07-01'
      AND date <  '2026-07-01'
)
SELECT
    multiIf(move_pct < 1, '0~1%',
            move_pct < 2, '1~2%',
            move_pct < 3, '2~3%',
            move_pct < 5, '3~5%',
            move_pct < 8, '5~8%',
                          '8% 이상')              AS label,
    count()                                        AS hit_count,
    round(count() * 100.0 / sum(count()) OVER (), 1) AS share_pct
FROM moves
WHERE isFinite(move_pct)
  AND move_pct > 0
GROUP BY label
ORDER BY indexOf(['0~1%', '1~2%', '2~3%', '3~5%', '5~8%', '8% 이상'], label)
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