SPY monthly price returns by calendar month, complete years only

Name: SPY monthly price returns by calendar month, complete years only
Creator: Strasmore
Published: 2026-09-26
License: https://www.strasmore.com/terms/

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 2026-09-26, from The September Effect: Is It Real? SPY Data.

as of 2026-09-26 table 12×6read in context →

SPY monthly price returns by calendar month, complete years only — 12 rows by 6 columns, computed 2026-09-26 from US exchange, SIP and OPRA data.
label	avg_return_pct	median_return_pct	stdev_pct	hit_rate_pct	sample_count
Jan	0.27	1.55	4.41	54.5	22
Feb	0.36	1.32	4.24	59.1	22
Mar	0.6	1	4.46	59.1	22
Apr	1.63	1.13	4.43	72.7	22
May	0.92	1.61	3.81	77.3	22
Jun	-0.1	-0.02	4.01	50	22
Jul	2.36	2.28	3.32	77.3	22
Aug	0.27	0.79	3.38	63.6	22
Sep	-0.71	0.48	4.59	59.1	22
Oct	1.16	2.21	5.74	60.9	23
Nov	2.45	2.75	3.88	82.6	23
Dec	0.45	0.7	3.46	65.2	23

Rows × columns: 12 × 6
Computed: 2026-09-26
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 monthly price returns by calendar month, complete years only, derived from the stored result.
Column	Type	Range	Notes
`label`	text	12 distinct values (Apr, Aug, Dec…)
`avg_return_pct`	number	-0.71 to 2.45	percent
`median_return_pct`	number	-0.02 to 2.75	percent
`stdev_pct`	number	3.32 to 5.74	percent
`hit_rate_pct`	number	50 to 82.6	percent
`sample_count`	number	22 to 23	count

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 2026-09-26, 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 month_ends AS
(
    SELECT
        toStartOfMonth(date)           AS month_start,
        argMax(toFloat64(close), date) AS month_end_close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND date < toStartOfYear(today())
    GROUP BY month_start
),
monthly_returns AS
(
    SELECT
        month_start,
        month_end_close,
        lagInFrame(month_end_close, 1) OVER (ORDER BY month_start ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS prev_close
    FROM month_ends
)
SELECT
    formatDateTime(month_start, '%b')                                                                    AS label,
    round(avg(month_end_close / prev_close - 1) * 100, 2)                                                AS avg_return_pct,
    round(quantileDeterministic(0.5)(month_end_close / prev_close - 1, toYYYYMM(month_start)) * 100, 2) AS median_return_pct,
    round(stddevSamp(month_end_close / prev_close - 1) * 100, 2)                                         AS stdev_pct,
    round(countIf(month_end_close > prev_close) / count() * 100, 1)                                      AS hit_rate_pct,
    count()                                                                                              AS sample_count
FROM monthly_returns
WHERE prev_close > 0
GROUP BY toMonth(month_start), label
ORDER BY toMonth(month_start)

SQL to run

WITH month_ends AS
(
    SELECT
        toStartOfMonth(date)           AS month_start,
        argMax(toFloat64(close), date) AS month_end_close
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND date < toStartOfYear(today())
    GROUP BY month_start
),
monthly_returns AS
(
    SELECT
        month_start,
        month_end_close,
        lagInFrame(month_end_close, 1) OVER (ORDER BY month_start ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS prev_close
    FROM month_ends
)
SELECT
    formatDateTime(month_start, '%b')                                                                    AS label,
    round(avg(month_end_close / prev_close - 1) * 100, 2)                                                AS avg_return_pct,
    round(quantileDeterministic(0.5)(month_end_close / prev_close - 1, toYYYYMM(month_start)) * 100, 2) AS median_return_pct,
    round(stddevSamp(month_end_close / prev_close - 1) * 100, 2)                                         AS stdev_pct,
    round(countIf(month_end_close > prev_close) / count() * 100, 1)                                      AS hit_rate_pct,
    count()                                                                                              AS sample_count
FROM monthly_returns
WHERE prev_close > 0
GROUP BY toMonth(month_start), label
ORDER BY toMonth(month_start)

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