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Who left the tape: March 2021 symbols by daily dollar volume, checked against late July 2026ranking · 2026-08-05 · 5×4Preview: 5 ranked values, largest first. Survivorship, measured: March cohorts of US symbols still trading in late July 2026table · 2026-08-05 · 10×5 One week of the US minute tape: symbols and bars per session, Jul 20-24, 2026series · 2026-08-05 · 5×4Preview: a 5-point series, ending lower.
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Trading sessions per month: SPY bars, July 2025 through June 2026series · 2026-08-01 · 12×4Preview: a 12-point series, roughly flat. Where the volume sits: share of June 2026 volume by half hour, New York timeseries · 2026-08-01 · 32×3Preview: a 16-point series, roughly flat. One company, two tickers: share class volume split, June 2026ranking · 2026-08-01 · 6×4Preview: 6 ranked values, largest first. Minute bars per session: nine widely held names, June 2026series · 2026-08-01 · 9×4Preview: a 9-point series, ending lower.
Who left the tape: March 2021 symbols by daily dollar volume, checked against late July 2026

Who left the tape: March 2021 symbols by daily dollar volume, checked against late July 2026

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Who left the tape: March 2021 symbols by daily dollar volume, checked against late July 2026 — 5 rows by 4 columns, computed from US exchange, SIP and OPRA data.
liquidity_bucketnames_countgone_countgone_pct
under $1M3968228057.5
$1M to $10M3137129841.4
$10M to $100M234864227.3
$100M to $1B81510813.3
$1B or more8933.4
the exact SQL behind every number 
WITH on_tape_now AS (
    SELECT ticker
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2026-07-20')
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-07-31')
    GROUP BY ticker
),
march_2021 AS (
    SELECT ticker,
           sum(toFloat64(close) * toFloat64(volume))
             / uniqExact(toDate(toTimeZone(window_start, 'America/New_York'))) AS avg_daily_dollar_volume
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2021-03-01')
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2021-03-31')
    GROUP BY ticker
)
SELECT multiIf(d.avg_daily_dollar_volume >= 1000000000, '$1B or more',
               d.avg_daily_dollar_volume >= 100000000, '$100M to $1B',
               d.avg_daily_dollar_volume >= 10000000, '$10M to $100M',
               d.avg_daily_dollar_volume >= 1000000, '$1M to $10M',
               'under $1M') AS liquidity_bucket,
       count() AS names_count,
       countIf(n.ticker = '') AS gone_count,
       round(100 * countIf(n.ticker = '') / count(), 1) AS gone_pct
FROM march_2021 AS d
LEFT JOIN on_tape_now AS n ON d.ticker = n.ticker
GROUP BY liquidity_bucket
ORDER BY min(d.avg_daily_dollar_volume)
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