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One bond fund, two distribution yields, month by month

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 2026-08-22, from SEC 30-Day Yield vs Distribution Yield.

as of series 37×4read in context →
One bond fund, two distribution yields, month by month — 37 rows by 4 columns, computed from US exchange, SIP and OPRA data.
monthmonth_labelindicated_yield_pctttm_yield_pct
2023-05May 20233.123.04
2023-06Jun 20233.183.11
2023-07Jul 20233.333.27
2023-08Aug 20233.423.44
2023-09Sep 20233.93.83
2023-10Oct 20234.024.12
2023-11Nov 20233.753.81
2023-12Dec 20237.273.89
2024-02Feb 20243.953.88
2024-03Mar 20243.743.88
2024-04Apr 20244.254.23
2024-05May 20244.084.17
2024-06Jun 20244.034.16
2024-07Jul 20243.684.04
2024-08Aug 20243.924.01
2024-09Sep 20243.833.98
2024-10Oct 20244.14.26
2024-11Nov 20243.974.22
2024-12Dec 20249.294.99
2025-02Feb 20254.054.4
2025-03Mar 20253.824.45
2025-04Apr 20254.374.56
2025-05May 20254.564.74
2025-06Jun 20254.344.65
2025-07Jul 20254.534.74
2025-08Aug 20254.584.81
2025-09Sep 20254.414.67
2025-10Oct 20254.134.62
2025-11Nov 20254.324.64
2025-12Dec 20259.135.21
2026-02Feb 20264.394.62
2026-03Mar 20264.164.82
2026-04Apr 20264.834.95
2026-05May 20264.414.93
2026-06Jun 20264.664.9
2026-07Jul 20264.645.15
2026-08Aug 20264.825.14
Rows × columns
37 × 4
Period covered
to
Computed
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 One bond fund, two distribution yields, month by month, derived from the stored result.
ColumnTypeRangeNotes
month date 2023-05 to 2026-08
month_label text 37 distinct values (Apr 2024, Apr 2025, Apr 2026…)
indicated_yield_pct number 3.12 to 9.29 percent
ttm_yield_pct number 3.04 to 5.21 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.

the exact SQL behind every number
WITH
    monthly_px AS
    (
        SELECT
            toStartOfMonth(date)           AS m,
            argMax(toFloat64(close), date) AS px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'TLT'
          AND date >= today() - 1600
        GROUP BY m
    ),
    monthly_cash AS
    (
        SELECT
            toStartOfMonth(exd) AS m,
            sum(cash)           AS cash
        FROM
        (
            SELECT
                any(ex_dividend_date)       AS exd,
                any(toFloat64(cash_amount)) AS cash
            FROM global_markets.stocks_dividends
            WHERE ticker = 'TLT'
              AND ex_dividend_date >= today() - 1600
              AND ex_dividend_date <= today()
            GROUP BY id
        )
        GROUP BY m
    ),
    joined AS
    (
        SELECT
            p.m    AS m,
            p.px   AS px,
            c.cash AS cash
        FROM monthly_px AS p
        INNER JOIN monthly_cash AS c ON c.m = p.m
    ),
    rolled AS
    (
        SELECT
            m,
            px,
            cash,
            sum(cash) OVER (ORDER BY m ASC ROWS BETWEEN 11 PRECEDING AND CURRENT ROW) AS ttm_cash,
            count()   OVER (ORDER BY m ASC ROWS BETWEEN 11 PRECEDING AND CURRENT ROW) AS months_counted
        FROM joined
    )
SELECT
    formatDateTime(m, '%Y-%m')        AS month,
    formatDateTime(m, '%b %Y')        AS month_label,
    round((100 * 12 * cash) / px, 2)  AS indicated_yield_pct,
    round((100 * ttm_cash) / px, 2)   AS ttm_yield_pct
FROM rolled
WHERE months_counted = 12
ORDER BY m ASC

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