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.
| episode | daily_return_pct |
|---|---|
| Mar 24, 2020 | 9.5 |
| Apr 9, 2025 | 9.4 |
| Mar 13, 2020 | 9.2 |
| Apr 6, 2020 | 6.7 |
| Mar 17, 2020 | 6.2 |
| Mar 26, 2020 | 5.9 |
| Nov 10, 2022 | 5.5 |
| Mar 10, 2020 | 5.1 |
| Dec 26, 2018 | 4.9 |
| Mar 2, 2020 | 4.4 |
- Rows × columns
- 10 × 2
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
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.
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 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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