STRASMORE/EXPLORE 2,170 QUERIES

What the lookback window costs in daily turnover (SPY, 10% target, 2x cap)

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 Volatility Targeting for Position Sizing.

as of ranking 4×4read in context →
What the lookback window costs in daily turnover (SPY, 10% target, 2x cap) — 4 rows by 4 columns, computed from US exchange, SIP and OPRA data.
lookback_windowavg_weightavg_daily_turnover_pctdays_at_cap_pct
10-session0.816.920.7
21-session0.752.950
63-session0.690.850
126-session0.660.420
Rows × columns
4 × 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 What the lookback window costs in daily turnover (SPY, 10% target, 2x cap), derived from the stored result.
ColumnTypeRangeNotes
lookback_window text 4 distinct values (10-session, 126-session, 21-session…)
avg_weight number 0.66 to 0.81
avg_daily_turnover_pct number 0.42 to 6.92 percent
days_at_cap_pct number 0 to 0.7 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
    px AS
    (
        SELECT
            date                  AS d,
            toFloat64(any(close)) AS c
        FROM global_markets.stocks_daily_aggs
        WHERE ticker = 'SPY'
          AND date >= subtractYears(today(), 5)
          AND date <  today()
        GROUP BY d
    ),
    px_sorted AS
    (
        SELECT arraySort(p -> p.1, groupArray((d, c))) AS pts
        FROM px
    ),
    rets AS
    (
        SELECT arrayFilter(x -> abs(x) < 0.4,
                   arrayMap((a, b) -> log(b.2 / a.2),
                            arraySlice(pts, 1, length(pts) - 1),
                            arraySlice(pts, 2))) AS r
        FROM px_sorted
    ),
    grid AS
    (
        SELECT
            r,
            arrayJoin([10, 21, 63, 126]) AS lb
        FROM rets
    ),
    weights AS
    (
        SELECT
            lb,
            arrayMap(i -> least(2.0,
                        0.10 / (arrayReduce('stddevSamp', arraySlice(r, i - lb + 1, lb)) * sqrt(252))),
                     range(lb, length(r) + 1)) AS w
        FROM grid
    )
SELECT
    concat(toString(lb), '-session') AS lookback_window,
    round(arrayAvg(w), 2)            AS avg_weight,
    round(100 * arrayAvg(arrayMap((a, b) -> abs(b - a),
                                  arraySlice(w, 1, length(w) - 1),
                                  arraySlice(w, 2))), 2) AS avg_daily_turnover_pct,
    round(100.0 * arrayCount(x -> x > 1.999, w) / length(w), 1) AS days_at_cap_pct
FROM weights
ORDER BY lb

Run your own version of this

The same 22 years of US equities and 12 years of options data are queryable in SQL or plain English. A free account runs 100 queries a day and takes no card.

More from this analysisVolatility Targeting for Position Sizing
One 10% risk budget, six names, six different weights ranking 6×4 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 2,170 queries →