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SPY holding-period outcomes since 2015: 5th percentile, median, 95th percentile

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 2026-08-03, from Can an AI Trading Track Record Be Verified?.

as of table 4×7read in context →
SPY holding-period outcomes since 2015: 5th percentile, median, 95th percentile — 4 rows by 7 columns, computed from US exchange, SIP and OPRA data.
holding_periodwindow_countp05_return_pctmedian_return_pctp95_return_pctspread_pctstdev_pct
1 month2868-6.61.67.113.74.5
3 months2826-8.83.912.221.16.9
6 months2763-9.46.719.428.89
12 months2637-10.914.134.24513.6
Rows × columns
4 × 7
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 holding-period outcomes since 2015: 5th percentile, median, 95th percentile, derived from the stored result.
ColumnTypeRangeNotes
holding_period text 4 distinct values (1 month, 12 months, 3 months…)
window_count number 2,637 to 2,868 count
p05_return_pct number -10.9 to -6.6 percent
median_return_pct number 1.6 to 14.1 percent
p95_return_pct number 7.1 to 34.2 percent
spread_pct number 13.7 to 45 percent
stdev_pct number 4.5 to 13.6 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 session_date,
           toFloat64(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('2015-01-02')
      AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-06-30')
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY session_date
),
series AS (
    SELECT groupArray(close_px) AS px
    FROM (SELECT close_px FROM daily ORDER BY session_date)
),
horizons AS (
    SELECT arrayJoin([21, 63, 126, 252]) AS sessions, px
    FROM series
),
windows AS (
    SELECT sessions,
           arrayJoin(arrayMap(i -> (i, 100 * (px[i + sessions] / px[i] - 1)),
                              range(1, length(px) - sessions + 1))) AS w
    FROM horizons
),
measured AS (
    SELECT sessions,
           w.1 AS window_index,
           w.2 AS window_return_pct
    FROM windows
)
SELECT multiIf(sessions = 21, '1 month',
               sessions = 63, '3 months',
               sessions = 126, '6 months',
               '12 months') AS holding_period,
       count() AS window_count,
       round(quantileDeterministic(0.05)(window_return_pct,
                                         cityHash64(window_index * 1000 + sessions)), 1) AS p05_return_pct,
       round(quantileDeterministic(0.50)(window_return_pct,
                                         cityHash64(window_index * 1000 + sessions)), 1) AS median_return_pct,
       round(quantileDeterministic(0.95)(window_return_pct,
                                         cityHash64(window_index * 1000 + sessions)), 1) AS p95_return_pct,
       round(quantileDeterministic(0.95)(window_return_pct,
                                         cityHash64(window_index * 1000 + sessions))
             - quantileDeterministic(0.05)(window_return_pct,
                                           cityHash64(window_index * 1000 + sessions)), 1) AS spread_pct,
       round(stddevSamp(window_return_pct), 1) AS stdev_pct
FROM measured
GROUP BY sessions
ORDER BY sessions

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