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?.
| holding_period | window_count | p05_return_pct | median_return_pct | p95_return_pct | spread_pct | stdev_pct |
|---|---|---|---|---|---|---|
| 1 month | 2868 | -6.6 | 1.6 | 7.1 | 13.7 | 4.5 |
| 3 months | 2826 | -8.8 | 3.9 | 12.2 | 21.1 | 6.9 |
| 6 months | 2763 | -9.4 | 6.7 | 19.4 | 28.8 | 9 |
| 12 months | 2637 | -10.9 | 14.1 | 34.2 | 45 | 13.6 |
- Rows × columns
- 4 × 7
- 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 |
|---|---|---|---|
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.
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 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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