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China's Dragon-Tiger List, Explained
How many names a volatility cutoff keeps as the cutoff movesranking · 2026-11-01 · 6×3Preview: 6 ranked values, largest first. Share of reported volume marked short, with no trader namedranking · 2026-11-01 · 6×3Preview: 6 ranked values, largest first. How fast US insider transactions reach the public file, by monthseries · 2026-11-01 · 14×4Preview: a 14-point series, ending lower.
What Is a Liquidity Sweep in Trading?
SPY 2025: the ten widest pokes above the prior day's high that closed back below itseries · 2026-09-28 · 10×7Preview: a 10-point series, ending lower. Inside the widest 2025 SPY sweep day: fifteen-minute high, low and volumeseries · 2026-09-28 · 26×4Preview: a 16-point series, ending lower. Volume in the fifteen minutes that printed the high, beside the session's median and busiest slicesranking · 2026-09-28 · 3×2Preview: 3 ranked values, smallest first. SPY: share of sessions that swept the prior day's high or low, by yearranking · 2026-09-28 · 5×3Preview: 5 ranked values, smallest first. How often a session pokes through the prior day's high or low and closes back inside, 2021 to 2025table · 2026-09-28 · 7×6
How many names a volatility cutoff keeps as the cutoff moves

How many names a volatility cutoff keeps as the cutoff moves

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How many names a volatility cutoff keeps as the cutoff moves — 6 rows by 3 columns, computed from US exchange, SIP and OPRA data.
amplitude_thresholdavg_daily_name_countshare_of_name_days_pct
3%215549.64
5%111725.72
7%62714.44
10%3137.21
15%1343.09
20%711.63
the exact SQL behind every number
SELECT
    concat(toString(th), '%')                                 AS amplitude_threshold,
    round(countIf(amplitude_pct >= th) / countDistinct(date)) AS avg_daily_name_count,
    round(100 * countIf(amplitude_pct >= th) / count(), 2)    AS share_of_name_days_pct
FROM
(
    SELECT
        date,
        100 * (toFloat64(high) - toFloat64(low)) / toFloat64(low) AS amplitude_pct
    FROM global_markets.stocks_daily_aggs
    WHERE date >= today() - 150
      AND date <  today() - 2
      AND volume >= 200000
      AND toFloat64(low) >= 2
      AND ticker NOT IN ('SPCX')
)
ARRAY JOIN [3, 5, 7, 10, 15, 20] AS th
GROUP BY th
ORDER BY th
$