spy_vs_peers
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 2026-11-01, from what-is-spy-etf.
| month | month_label | spy_pct | voo_pct | ivv_pct | spy_minus_voo_pct |
|---|---|---|---|---|---|
| 2025-11 | Nov 2025 | -0.33 | -0.32 | -0.31 | 0.01 |
| 2025-12 | Dec 2025 | 0.46 | 0.47 | 0.4 | 0.02 |
| 2026-01 | Jan 2026 | 0.91 | 0.89 | 0.91 | 0.03 |
| 2026-02 | Feb 2026 | -0.52 | -0.5 | -0.47 | 0.03 |
| 2026-03 | Mar 2026 | -4.18 | -4.27 | -4.19 | 0.09 |
| 2026-04 | Apr 2026 | 9.9 | 9.9 | 9.93 | 0 |
| 2026-05 | May 2026 | 4.88 | 4.89 | 4.9 | 0 |
| 2026-06 | Jun 2026 | -1.14 | -1.11 | -1.33 | 0.02 |
| 2026-07 | Jul 2026 | 0.27 | 0.28 | 0.26 | 0.01 |
| 2026-08 | Aug 2026 | 2.35 | 2.33 | 2.35 | 0.02 |
| 2026-09 | Sep 2026 | 0.08 | 0.06 | 0.03 | 0.02 |
| 2026-10 | Oct 2026 | -1.13 | -1.1 | -1.11 | 0.03 |
- Rows × columns
- 12 × 6
- 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 |
|---|---|---|---|
month |
text | 12 distinct values (2025-11, 2025-12, 2026-01…) | |
month_label |
text | 12 distinct values (Apr 2026, Aug 2026, Dec 2025…) | |
spy_pct |
number | -4.18 to 9.9 | percent |
voo_pct |
number | -4.27 to 9.9 | percent |
ivv_pct |
number | -4.19 to 9.93 | percent |
spy_minus_voo_pct |
number | 0 to 0.09 | 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.
SELECT
month,
any(month_label) AS month_label,
round(100 * (anyIf(end_close, ticker = 'SPY') / anyIf(start_open, ticker = 'SPY') - 1), 2) AS spy_pct,
round(100 * (anyIf(end_close, ticker = 'VOO') / anyIf(start_open, ticker = 'VOO') - 1), 2) AS voo_pct,
round(100 * (anyIf(end_close, ticker = 'IVV') / anyIf(start_open, ticker = 'IVV') - 1), 2) AS ivv_pct,
round(abs(
100 * (anyIf(end_close, ticker = 'SPY') / anyIf(start_open, ticker = 'SPY') - 1)
- 100 * (anyIf(end_close, ticker = 'VOO') / anyIf(start_open, ticker = 'VOO') - 1)
), 2) AS spy_minus_voo_pct
FROM
(
SELECT
ticker,
formatDateTime(toStartOfMonth(date), '%Y-%m') AS month,
formatDateTime(toStartOfMonth(date), '%b %Y') AS month_label,
toFloat64(argMin(open, date)) AS start_open,
toFloat64(argMax(close, date)) AS end_close
FROM global_markets.stocks_daily_aggs
WHERE ticker IN ('SPY', 'VOO', 'IVV')
AND date >= toStartOfMonth(today() - 365)
AND date <= today()
GROUP BY ticker, toStartOfMonth(date)
)
GROUP BY month
HAVING countIf(ticker = 'SPY') > 0
AND countIf(ticker = 'VOO') > 0
AND countIf(ticker = 'IVV') > 0
ORDER BY month
Use dis data for your AI assistant
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