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The window decides the answer: SPY calendar-year price return and intra-year high-to-low range, 2016-2025ranking · 2026-07-31 · 10×4Preview: 10 ranked values, smallest first. Where the money trades: US dollar volume by liquidity rank tier, regular hours, June 30 2026ranking · 2026-07-31 · 5×4Preview: 5 ranked values, smallest first. How big a typical session is: SPY close-to-close moves by size band, calendar 2025ranking · 2026-07-31 · 5×3Preview: 5 ranked values, largest first. The cost floor: median quoted spread in basis points of the midpoint, regular hours, June 22-26 2026ranking · 2026-07-31 · 6×4Preview: 6 ranked values, smallest first.
The window decides the answer: SPY calendar-year price return and intra-year high-to-low range, 2016-2025

The window decides the answer: SPY calendar-year price return and intra-year high-to-low range, 2016-2025

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The window decides the answer: SPY calendar-year price return and intra-year high-to-low range, 2016-2025 — 10 rows by 4 columns, computed from US exchange, SIP and OPRA data.
yearsessionsprice_return_pcthigh_low_range_pct
201625211.224.4
201725118.519.1
2018251-6.925.3
201925228.632.3
202025315.168
202125228.729.4
2022251-2034
202325024.825.6
20242522430.1
202525016.639
the exact SQL behind every number
WITH daily AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMax(close, window_start) AS close_px
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2016-01-01')
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2025-12-31')
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY d
),
yr AS (
    SELECT toYear(d) AS year,
           argMin(close_px, d) AS first_close,
           argMax(close_px, d) AS last_close,
           max(close_px) AS high_close,
           min(close_px) AS low_close,
           count() AS sessions
    FROM daily
    GROUP BY year
)
SELECT year,
       sessions,
       round(100 * (toFloat64(last_close) / toFloat64(first_close) - 1), 1) AS price_return_pct,
       round(100 * (toFloat64(high_close) / toFloat64(low_close) - 1), 1) AS high_low_range_pct
FROM yr
ORDER BY year
$