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The memory and storage names: change vs Wednesday's close, range timing, and dollar volume

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 2026-07-26, from Market Recap: July 2, 2026, The Day in Numbers.

as of table 4×10read in context →
The memory and storage names: change vs Wednesday's close, range timing, and dollar volume — 4 rows by 10 columns, computed from US exchange, SIP and OPRA data.
tickerprior_closeday_closepct_chgday_highday_high_etday_lowday_low_etrange_pctday_dollar_bn
MU1033.29975.77-5.571064.6410:03950.2815:2612.0351.4
SNDK2035.071743.59-14.322052.5409:35169315:2621.2426.57
STX915.25820.25-10.38923.0609:53795.6614:2316.014.74
WDC598.37538.99-9.92609.4609:35525.8413:5915.94.31
Rows × columns
4 × 10
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 The memory and storage names: change vs Wednesday's close, range timing, and dollar volume, derived from the stored result.
ColumnTypeRangeNotes
ticker text 4 distinct values (MU, SNDK, STX…)
prior_close number 598.37 to 2,035.07 US dollars
day_close number 538.99 to 1,743.59 US dollars
pct_chg number -14.32 to -5.57 percent
day_high number 609.46 to 2,052.54 US dollars
day_high_et text 3 distinct values (09:35, 09:53, 10:03)
day_low number 525.84 to 1,693 US dollars
day_low_et text 3 distinct values (13:59, 14:23, 15:26)
range_pct number 12.03 to 21.24 percent
day_dollar_bn number 4.31 to 51.4

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 
WITH per_name AS (
    SELECT
        ticker,
        toFloat64(argMaxIf(close, window_start, window_start < '2026-07-02 00:00:00')) AS prior_close,
        toFloat64(argMaxIf(close, window_start, window_start >= '2026-07-02 00:00:00')) AS day_close,
        maxIf(toFloat64(high), window_start >= '2026-07-02 00:00:00') AS day_high,
        minIf(toFloat64(low), window_start >= '2026-07-02 00:00:00') AS day_low,
        argMinIf(window_start, toFloat64(low), window_start >= '2026-07-02 00:00:00') AS low_bar,
        argMaxIf(window_start, toFloat64(high), window_start >= '2026-07-02 00:00:00') AS high_bar,
        round(sumIf(toFloat64(close) * toFloat64(volume), window_start >= '2026-07-02 00:00:00') / 1e9, 2) AS day_dollar_bn
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('MU', 'SNDK', 'STX', 'WDC')
      AND ((window_start >= '2026-07-01 13:30:00' AND window_start < '2026-07-01 20:00:00')
        OR (window_start >= '2026-07-02 13:30:00' AND window_start < '2026-07-02 20:00:00'))
    GROUP BY ticker
)
SELECT
    ticker,
    round(prior_close, 2) AS prior_close,
    round(day_close, 2) AS day_close,
    round((day_close / prior_close - 1) * 100, 2) AS pct_chg,
    round(day_high, 2) AS day_high,
    formatDateTime(toTimeZone(high_bar, 'America/New_York'), '%H:%i') AS day_high_et,
    round(day_low, 2) AS day_low,
    formatDateTime(toTimeZone(low_bar, 'America/New_York'), '%H:%i') AS day_low_et,
    round((day_high / day_low - 1) * 100, 2) AS range_pct,
    day_dollar_bn
FROM per_name
ORDER BY ticker

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