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The ten biggest single-day gains for SPY since 2016

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 2026-07-16, from What Missing the Best Days Costs.

as of ranking 10×2read in context →
The ten biggest single-day gains for SPY since 2016 — 10 rows by 2 columns, computed from US exchange, SIP and OPRA data.
episodedaily_return_pct
Mar 24, 20209.5
Apr 9, 20259.4
Mar 13, 20209.2
Apr 6, 20206.7
Mar 17, 20206.2
Mar 26, 20205.9
Nov 10, 20225.5
Mar 10, 20205.1
Dec 26, 20184.9
Mar 2, 20204.4
Rows × columns
10 × 2
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 ten biggest single-day gains for SPY since 2016, derived from the stored result.
ColumnTypeRangeNotes
episode text 10 distinct values (Apr 6, 2020, Apr 9, 2025, Dec 26, 2018…)
daily_return_pct number 4.4 to 9.5 percent

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 daily AS (
    SELECT toDate(toTimeZone(window_start, 'America/New_York')) AS dt,
           argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY' AND window_start >= '2016-01-01'
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY dt
),
rets AS (
    SELECT dt, c / lagInFrame(c) OVER (ORDER BY dt) - 1 AS ret FROM daily
),
clean AS (
    SELECT dt, ret FROM rets WHERE ret IS NOT NULL AND ret > -0.5 AND ret < 0.5
),
ranked AS (
    SELECT dt, ret, row_number() OVER (ORDER BY ret DESC) AS best_rnk FROM clean
)
SELECT formatDateTime(dt, '%b %e, %Y') AS episode,
       round(ret * 100, 1) AS daily_return_pct
FROM ranked
WHERE best_rnk <= 10
ORDER BY daily_return_pct DESC

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