STRASMORE/EXPLORE 2,170 QUERIES

SPY total return since 2016, after removing the best single days

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 5×2read in context →
SPY total return since 2016, after removing the best single days — 5 rows by 2 columns, computed from US exchange, SIP and OPRA data.
best_days_missedtotal_return_pct
0275.5
1097
2042.4
309
50-32.5
Rows × columns
5 × 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 SPY total return since 2016, after removing the best single days, derived from the stored result.
ColumnTypeRangeNotes
best_days_missed number 0 to 50
total_return_pct number -32.5 to 275.5 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
),
ranked AS (
    SELECT log(1 + ret) AS lr, row_number() OVER (ORDER BY ret DESC) AS rnk
    FROM rets
    WHERE ret IS NOT NULL AND ret > -0.5 AND ret < 0.5
)
SELECT k AS best_days_missed,
       round((exp(sumIf(lr, rnk > k)) - 1) * 100, 1) AS total_return_pct
FROM ranked
CROSS JOIN (SELECT arrayJoin([0, 10, 20, 30, 50]) AS k) AS kvals
GROUP BY k
ORDER BY k

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