year_by_year
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 2026-10-02, from the-january-barometer.
| year | january_pct | year_pct | feb_to_dec_pct |
|---|---|---|---|
| 2004 | 1.98 | 8.62 | 6.51 |
| 2005 | -2.24 | 3.01 | 5.37 |
| 2006 | 2.4 | 13.74 | 11.07 |
| 2007 | 1.5 | 3.24 | 1.71 |
| 2008 | -6.05 | -38.28 | -34.31 |
| 2009 | -8.21 | 23.49 | 34.54 |
| 2010 | -3.63 | 12.84 | 17.1 |
| 2011 | 2.33 | -0.2 | -2.47 |
| 2012 | 4.64 | 13.47 | 8.45 |
| 2013 | 5.12 | 29.69 | 23.37 |
| 2014 | -3.52 | 11.29 | 15.36 |
| 2015 | -2.96 | -0.81 | 2.22 |
| 2016 | -4.98 | 9.64 | 15.39 |
| 2017 | 1.79 | 19.38 | 17.29 |
| 2018 | 5.64 | -6.35 | -11.34 |
| 2019 | 8.01 | 28.79 | 19.24 |
| 2020 | -0.04 | 16.16 | 16.21 |
| 2021 | -1.02 | 27.04 | 28.34 |
| 2022 | -5.27 | -19.48 | -15 |
| 2023 | 6.29 | 24.29 | 16.93 |
| 2024 | 1.59 | 23.3 | 21.37 |
| 2025 | 2.69 | 16.35 | 13.31 |
- Rows × columns
- 22 × 4
- 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 |
|---|---|---|---|
year |
number | 2,004 to 2,025 | |
january_pct |
number | -8.21 to 8.01 | percent |
year_pct |
number | -38.28 to 29.69 | percent |
feb_to_dec_pct |
number | -34.31 to 34.54 | 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 yearly AS
(
SELECT
toYear(date) AS y,
argMax(close, date) AS dec_close,
argMaxIf(close, date, toMonth(date) = 1) AS jan_close,
countIf(toMonth(date) = 1) AS jan_sessions,
countIf(toMonth(date) = 12) AS dec_sessions
FROM global_markets.stocks_daily_aggs
WHERE ticker = 'SPY'
GROUP BY y
)
SELECT
cur.y AS year,
round(100 * (toFloat64(cur.jan_close) / toFloat64(prev.dec_close) - 1), 2) AS january_pct,
round(100 * (toFloat64(cur.dec_close) / toFloat64(prev.dec_close) - 1), 2) AS year_pct,
round(100 * (toFloat64(cur.dec_close) / toFloat64(cur.jan_close) - 1), 2) AS feb_to_dec_pct
FROM yearly AS cur
INNER JOIN yearly AS prev ON prev.y = cur.y - 1
WHERE cur.jan_sessions >= 15
AND cur.dec_sessions >= 15
ORDER BY year
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.