rounding_residual

Name: rounding_residual
Creator: Strasmore
Published: 2026-09-28
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 2026-09-28, from how-many-puts-to-hedge-a-portfolio.

as of 2026-09-28 table 7×6read in context →

rounding_residual — 7 rows by 6 columns, computed 2026-09-28 from US exchange, SIP and OPRA data.
portfolio_size	exact_contracts	contracts_rounded_down	unhedged_usd_thousands	unhedged_pct	overhedge_pct
$60k	0.82	0	63	105	22.94
$120k	1.64	1	49.2	41.03	22.94
$180k	2.46	2	35.5	19.71	22.94
$300k	4.1	4	7.9	2.65	22.94
$500k	6.84	6	64.4	12.88	2.47
$700k	9.57	9	44.1	6.3	4.66
$900k	12.31	12	23.8	2.65	5.88

Rows × columns: 7 × 6
Computed: 2026-09-28
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 rounding_residual, derived from the stored result.
Column	Type	Range	Notes
`portfolio_size`	text	7 distinct values ($120k, $180k, $300k…)
`exact_contracts`	number	0.82 to 12.31	count
`contracts_rounded_down`	number	0 to 12	count
`unhedged_usd_thousands`	number	7.9 to 64.4	US dollars
`unhedged_pct`	number	2.65 to 105	percent
`overhedge_pct`	number	2.47 to 22.94	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 2026-09-28, 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.

SELECT
    portfolio_size,
    round(exact_raw, 2)                                                                AS exact_contracts,
    toUInt16(floor(exact_raw))                                                         AS contracts_rounded_down,
    round((beta_notional - floor(exact_raw) * contract_notional) / 1000, 1)             AS unhedged_usd_thousands,
    round(100 * (beta_notional - floor(exact_raw) * contract_notional) / portfolio, 2)  AS unhedged_pct,
    round(100 * (ceil(exact_raw) * contract_notional - beta_notional) / portfolio, 2)   AS overhedge_pct
FROM
(
    SELECT
        v.portfolio                                          AS portfolio,
        concat('$', toString(intDiv(v.portfolio, 1000)), 'k') AS portfolio_size,
        v.portfolio * 1.05                                   AS beta_notional,
        s.px * 100                                           AS contract_notional,
        v.portfolio * 1.05 / (s.px * 100)                    AS exact_raw
    FROM
    (
        SELECT arrayJoin([60000, 120000, 180000, 300000, 500000, 700000, 900000]) AS portfolio
    ) AS v
    CROSS JOIN
    (
        SELECT argMax(toFloat64(close), date) AS px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'SPY'
          AND date >= today() - 30
    ) AS s
)
ORDER BY exact_contracts

SQL to run

SELECT
    portfolio_size,
    round(exact_raw, 2)                                                                AS exact_contracts,
    toUInt16(floor(exact_raw))                                                         AS contracts_rounded_down,
    round((beta_notional - floor(exact_raw) * contract_notional) / 1000, 1)             AS unhedged_usd_thousands,
    round(100 * (beta_notional - floor(exact_raw) * contract_notional) / portfolio, 2)  AS unhedged_pct,
    round(100 * (ceil(exact_raw) * contract_notional - beta_notional) / portfolio, 2)   AS overhedge_pct
FROM
(
    SELECT
        v.portfolio                                          AS portfolio,
        concat('$', toString(intDiv(v.portfolio, 1000)), 'k') AS portfolio_size,
        v.portfolio * 1.05                                   AS beta_notional,
        s.px * 100                                           AS contract_notional,
        v.portfolio * 1.05 / (s.px * 100)                    AS exact_raw
    FROM
    (
        SELECT arrayJoin([60000, 120000, 180000, 300000, 500000, 700000, 900000]) AS portfolio
    ) AS v
    CROSS JOIN
    (
        SELECT argMax(toFloat64(close), date) AS px
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'SPY'
          AND date >= today() - 30
    ) AS s
)
ORDER BY exact_contracts

⌘/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

More from this analysishow-many-puts-to-hedge-a-portfolio

hedge_math table 3×6 → beta_drift ranking 6×4 → put_cost_curve ranking 4×3 → The 2s10s spread, every print of the half table 124×2 → The 2s10s spread, every print of the half table 124×2 → Every half-year since 1976: the 2y and 10y change, the twist between them, and the half's lowest 2s10s print table 100×7 → See all 2,749 queries →