STRASMORE/EXPLORE 2,707 QUERIES

One 10% risk budget, six names, six different weights

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 Volatility Targeting for Position Sizing.

as of ranking 6×4read in context →
One 10% risk budget, six names, six different weights — 6 rows by 4 columns, computed from US exchange, SIP and OPRA data.
symbolrealised_vol_pctraw_weightcapped_weight
SPY130.770.77
KO18.90.530.53
AAPL24.60.410.41
MSFT32.50.310.31
NVDA37.80.260.26
TSLA46.30.220.22
Rows × columns
6 × 4
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 10% risk budget, six names, six different weights, derived from the stored result.
ColumnTypeRangeNotes
symbol text 6 distinct values (AAPL, KO, MSFT…)
realised_vol_pct number 13 to 46.3 percent
raw_weight number 0.22 to 0.77
capped_weight number 0.22 to 0.77

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.

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
    px AS
    (
        SELECT
            ticker,
            date                  AS d,
            toFloat64(any(close)) AS c
        FROM global_markets.stocks_daily_aggs
        WHERE ticker IN ('KO', 'SPY', 'MSFT', 'AAPL', 'NVDA', 'TSLA')
          AND date >= subtractYears(today(), 1)
          AND date <  today()
        GROUP BY ticker, d
    ),
    px_sorted AS
    (
        SELECT
            ticker,
            arraySort(p -> p.1, groupArray((d, c))) AS pts
        FROM px
        GROUP BY ticker
    ),
    vols AS
    (
        SELECT
            ticker AS symbol,
            arrayReduce('stddevSamp',
                arrayFilter(x -> abs(x) < 0.4,
                    arrayMap((a, b) -> log(b.2 / a.2),
                             arraySlice(pts, 1, length(pts) - 1),
                             arraySlice(pts, 2)))) * sqrt(252) AS ann_vol
        FROM px_sorted
    )
SELECT
    symbol,
    round(ann_vol * 100, 1)              AS realised_vol_pct,
    round(0.10 / ann_vol, 2)             AS raw_weight,
    round(least(0.10 / ann_vol, 2.0), 2) AS capped_weight
FROM vols
ORDER BY realised_vol_pct
⌘/Ctrl + Enter

Work with this data in your AI assistant

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

More from this analysisVolatility Targeting for Position Sizing
What the lookback window costs in daily turnover (SPY, 10% target, 2x cap) ranking 4×4 → SPY realised volatility by month against a 10% target series 72×4 → Weekly realised volatility and the weight it implied, Nov 2019 to Apr 2020 series 25×4 → Symbols that printed a final daily bar, by year ranking 10×3 → The January 2019 universe, grouped by what happened to each name ranking 9×4 → A 20/50 moving-average crossover on SPY, year by year, against holding ranking 9×4 → See all 2,707 queries →