window_vs_december
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 2026-09-17, from santa-claus-rally.
| label | years_measured | up_years | hit_rate_pct | mean_pct | median_pct | worst_pct | worst_year |
|---|---|---|---|---|---|---|---|
| Santa Claus window | 23 | 16 | 69.6 | 0.61 | 0.53 | -2.9 | 2014 |
| December before the window | 23 | 17 | 73.9 | 0.21 | 0.85 | -12.68 | 2018 |
| Calendar December | 23 | 15 | 65.2 | 0.45 | 0.7 | -9.33 | 2018 |
- Rows × columns
- 3 × 8
- 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 |
|---|---|---|---|
label |
text | 3 distinct values | |
years_measured |
number | every row is 23 | |
up_years |
number | 15 to 17 | |
hit_rate_pct |
number | 65.2 to 73.9 | percent |
mean_pct |
number | 0.21 to 0.61 | percent |
median_pct |
number | 0.53 to 0.85 | percent |
worst_pct |
number | -12.68 to -2.9 | percent |
worst_year |
text | 2 distinct values (2014, 2018) |
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
spy_days AS
(
SELECT
date,
toYear(date) AS y,
toMonth(date) AS m,
argMax(toFloat64(close), _ingest_time) AS close
FROM global_markets.stocks_daily_aggs
WHERE ticker = 'SPY'
AND toMonth(date) IN (11, 12, 1)
GROUP BY date
),
ranked AS
(
SELECT
date,
y,
m,
close,
row_number() OVER (PARTITION BY y, m ORDER BY date DESC) AS sessions_from_end,
row_number() OVER (PARTITION BY y, m ORDER BY date ASC) AS sessions_from_start
FROM spy_days
),
windows AS
(
SELECT
toUInt16(if(m = 1, y - 1, y)) AS year,
anyIf(close, m = 11 AND sessions_from_end = 1) AS november_close,
anyIf(close, m = 12 AND sessions_from_end = 6) AS base_close,
anyIf(close, m = 12 AND sessions_from_end = 1) AS december_close,
anyIf(close, m = 1 AND sessions_from_start = 2) AS end_close
FROM ranked
GROUP BY year
HAVING countIf(m = 11 AND sessions_from_end = 1) = 1
AND countIf(m = 12 AND sessions_from_end = 6) = 1
AND countIf(m = 1 AND sessions_from_start = 2) = 1
),
unpivoted AS
(
SELECT
year,
arrayJoin([
('Santa Claus window', (end_close / base_close - 1) * 100),
('December before the window', (base_close / november_close - 1) * 100),
('Calendar December', (december_close / november_close - 1) * 100)
]) AS pair,
tupleElement(pair, 1) AS label,
tupleElement(pair, 2) AS return_pct
FROM windows
)
SELECT
label,
count() AS years_measured,
countIf(return_pct > 0) AS up_years,
round(countIf(return_pct > 0) * 100 / count(), 1) AS hit_rate_pct,
round(avg(return_pct), 2) AS mean_pct,
round(quantileDeterministic(0.5)(return_pct, toUInt32(year)), 2) AS median_pct,
round(min(return_pct), 2) AS worst_pct,
toString(argMin(year, return_pct)) AS worst_year
FROM unpivoted
GROUP BY label
ORDER BY label DESC
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