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MU by month: annualized realized volatility, average session range, biggest single-day move

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 2026-07-13, from MU: The Biggest Tape of June 2026.

as of table 6×6read in context →
MU by month: annualized realized volatility, average session range, biggest single-day move — 6 rows by 6 columns, computed from US exchange, SIP and OPRA data.
period_startrealized_vol_ann_pctpct_of_peak_volavg_day_range_pctbiggest_day_move_pctreturn_days
2026-01-0164.850.85.41019
2026-02-0167.853.16.31019
2026-03-0178.261.369.922
2026-04-0159.246.45.59.121
2026-05-01101.479.47.219.320
2026-06-01127.61007.615.821
Rows × columns
6 × 6
Period covered
to
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 MU by month: annualized realized volatility, average session range, biggest single-day move, derived from the stored result.
ColumnTypeRangeNotes
period_start date 2026-01-01 to 2026-06-01
realized_vol_ann_pct number 59.2 to 127.6 percent
pct_of_peak_vol number 46.4 to 100 percent
avg_day_range_pct number 5.4 to 7.6 percent
biggest_day_move_pct number 9.1 to 19.3 percent
return_days number 19 to 22

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.

the exact SQL behind every number
SELECT
    toString(toStartOfMonth(et_date)) AS period_start,
    round(stddevSamp(log_ret) * sqrt(252) * 100, 1) AS realized_vol_ann_pct,
    round(100 * stddevSamp(log_ret) / max(stddevSamp(log_ret)) OVER (), 1) AS pct_of_peak_vol,
    round(avg(day_range_pct), 1) AS avg_day_range_pct,
    round(max(abs(day_move_pct)), 1) AS biggest_day_move_pct,
    count() AS return_days
FROM (
    SELECT et_date, day_range_pct,
        if(prev_close > 0, ln(close_usd / prev_close), NULL) AS log_ret,
        if(prev_close > 0, (close_usd / prev_close - 1) * 100, NULL) AS day_move_pct
    FROM (
        SELECT et_date, close_usd, day_range_pct,
            lagInFrame(close_usd) OVER (ORDER BY et_date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
        FROM (
            SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS et_date,
                argMaxIf(toFloat64(close), window_start, (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS close_usd,
                (maxIf(toFloat64(high), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) / minIf(toFloat64(low), (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) - 1) * 100 AS day_range_pct
            FROM global_markets.delayed_stocks_minute_aggs
            WHERE ticker = 'MU'
              AND window_start >= toDateTime('2026-01-01 00:00:00') AND window_start < toDateTime('2026-07-01 00:00:00')
            GROUP BY et_date
        )
    )
)
WHERE isFinite(log_ret) AND log_ret IS NOT NULL
GROUP BY period_start
ORDER BY period_start

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