qqq_dividendos

Name: qqq_dividendos
Creator: Strasmore
Published: 2026-10-04
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 2026-10-04, from buying-qqq-from-spain-and-mexico.

as of 2026-10-04 ranking 6×4read in context →

qqq_dividendos — 6 rows by 4 columns, computed 2026-10-04 from US exchange, SIP and OPRA data.
year	gross_per_share	gross_yield_pct	withholding_cost_pct
2020	1.737	0.55	0.083
2021	1.697	0.43	0.064
2022	2.135	0.8	0.12
2023	2.536	0.62	0.093
2024	2.846	0.56	0.084
2025	2.795	0.45	0.068

Rows × columns: 6 × 4
Computed: 2026-10-04
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 qqq_dividendos, derived from the stored result.
Column	Type	Range	Notes
`year`	text	6 distinct values (2020, 2021, 2022…)
`gross_per_share`	number	1.697 to 2.846
`gross_yield_pct`	number	0.43 to 0.8	percent
`withholding_cost_pct`	number	0.064 to 0.12	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 2026-10-04, 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
    divs AS
    (
        SELECT
            toYear(ex_date)             AS y,
            toFloat64(sum(amount))      AS gross
        FROM
        (
            SELECT
                max(ex_dividend_date) AS ex_date,
                max(cash_amount)      AS amount
            FROM global_markets.stocks_dividends
            WHERE ticker = 'QQQ'
              AND toYear(ex_dividend_date) BETWEEN toYear(today()) - 6 AND toYear(today()) - 1
            GROUP BY id
        )
        GROUP BY y
    ),
    px AS
    (
        SELECT
            toYear(date)                   AS y,
            argMax(toFloat64(close), date) AS year_close
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'QQQ'
          AND toYear(date) BETWEEN toYear(today()) - 6 AND toYear(today()) - 1
        GROUP BY y
    )
SELECT
    toString(divs.y)                            AS year,
    round(divs.gross, 3)                        AS gross_per_share,
    round(100 * divs.gross / px.year_close, 2)  AS gross_yield_pct,
    round(15 * divs.gross / px.year_close, 3)   AS withholding_cost_pct
FROM divs
INNER JOIN px ON divs.y = px.y
ORDER BY year

SQL to run

WITH
    divs AS
    (
        SELECT
            toYear(ex_date)             AS y,
            toFloat64(sum(amount))      AS gross
        FROM
        (
            SELECT
                max(ex_dividend_date) AS ex_date,
                max(cash_amount)      AS amount
            FROM global_markets.stocks_dividends
            WHERE ticker = 'QQQ'
              AND toYear(ex_dividend_date) BETWEEN toYear(today()) - 6 AND toYear(today()) - 1
            GROUP BY id
        )
        GROUP BY y
    ),
    px AS
    (
        SELECT
            toYear(date)                   AS y,
            argMax(toFloat64(close), date) AS year_close
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'QQQ'
          AND toYear(date) BETWEEN toYear(today()) - 6 AND toYear(today()) - 1
        GROUP BY y
    )
SELECT
    toString(divs.y)                            AS year,
    round(divs.gross, 3)                        AS gross_per_share,
    round(100 * divs.gross / px.year_close, 2)  AS gross_yield_pct,
    round(15 * divs.gross / px.year_close, 3)   AS withholding_cost_pct
FROM divs
INNER JOIN px ON divs.y = px.y
ORDER BY year

⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.

Claude ChatGPT Gemini Muse Your own agent