impact_curve

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 bars AS
(
    SELECT
        ticker,
        toStartOfMinute(toTimeZone(window_start, 'America/New_York')) AS et_minute,
        toFloat64(open)   AS open_px,
        toFloat64(close)  AS close_px,
        toFloat64(volume) AS vol
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('SPY', 'AAPL', 'KO', 'SJM', 'LANC')
      AND window_start >= '2026-07-01 00:00:00'
      AND window_start <  '2026-09-26 00:00:00'
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) >= 570
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) < 960
      AND volume > 0
      AND open > 0
),
ranked AS
(
    SELECT
        ticker,
        avg(vol) AS avg_minute_vol
    FROM bars
    GROUP BY ticker
    HAVING count() >= 500
),
picks AS
(
    SELECT
        argMax(ticker, avg_minute_vol) AS liquid_ticker,
        argMin(ticker, avg_minute_vol) AS thin_ticker
    FROM ranked
),
sided AS
(
    SELECT
        if(b.ticker = p.thin_ticker, 'thin', 'liquid') AS side,
        b.et_minute                                    AS et_minute,
        b.open_px                                      AS open_px,
        b.close_px                                     AS close_px,
        b.vol                                          AS vol
    FROM bars AS b
    CROSS JOIN picks AS p
    WHERE b.ticker = p.thin_ticker
       OR b.ticker = p.liquid_ticker
),
cuts AS
(
    SELECT
        side,
        quantilesDeterministic(0.2, 0.4, 0.6, 0.8)(vol, toUInt64(toUnixTimestamp(et_minute))) AS edges
    FROM sided
    GROUP BY side
),
tagged AS
(
    SELECT
        s.side                                            AS side,
        1 + length(arrayFilter(x -> s.vol >= x, c.edges)) AS quintile,
        abs(s.close_px / s.open_px - 1) * 10000           AS move_bps
    FROM sided AS s
    INNER JOIN cuts AS c ON c.side = s.side
)
SELECT
    concat('Q', toString(quintile))             AS volume_quintile,
    round(avgIf(move_bps, side = 'thin'), 1)    AS thin_abs_move_bps,
    round(avgIf(move_bps, side = 'liquid'), 1)  AS liquid_abs_move_bps,
    round(avgIf(move_bps, side = 'thin')
          / avgIf(move_bps, side = 'liquid'), 2) AS thin_over_liquid
FROM tagged
GROUP BY quintile
HAVING countIf(side = 'thin') > 0
   AND countIf(side = 'liquid') > 0
ORDER BY quintile