risk_stats

Name: risk_stats
Creator: Strasmore
Published: 2026-11-01
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 2026-11-01, from what-is-qqq-etf-for-russian-investors.

as of 2026-11-01 table 2×7read in context →

risk_stats — 2 rows by 7 columns, computed 2026-11-01 from US exchange, SIP and OPRA data.
ticker	first_close_on	last_close_on	annualized_return_pct	realized_vol_pct	biggest_drop_pct	biggest_gain_pct
QQQ	01.11.2021	30.10.2026	12.3	23.1	-6.2	12
SPY	01.11.2021	30.10.2026	10.5	17.2	-5.9	10.5

Rows × columns: 2 × 7
Computed: 2026-11-01
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 risk_stats, derived from the stored result.
Column	Type	Range	Notes
`ticker`	text	2 distinct values (QQQ, SPY)
`first_close_on`	text	1 distinct value (01.11.2021)
`last_close_on`	text	1 distinct value (30.10.2026)
`annualized_return_pct`	number	10.5 to 12.3	percent
`realized_vol_pct`	number	17.2 to 23.1	percent
`biggest_drop_pct`	number	-6.2 to -5.9	percent
`biggest_gain_pct`	number	10.5 to 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 2026-11-01, 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,
        toFloat64(any(close)) AS close_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('QQQ', 'SPY')
      AND date >= subtractYears(today(), 5)
      AND date <  today()
    GROUP BY ticker, date
),
daily AS
(
    SELECT
        ticker,
        date,
        close_px,
        close_px / lagInFrame(close_px, 1, close_px)
            OVER (PARTITION BY ticker ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) - 1 AS daily_return
    FROM px
)
SELECT
    ticker,
    formatDateTime(min(date), '%d.%m.%Y')                 AS first_close_on,
    formatDateTime(max(date), '%d.%m.%Y')                 AS last_close_on,
    round((pow(argMax(close_px, date) / argMin(close_px, date),
               365.25 / dateDiff('day', min(date), max(date))) - 1) * 100, 1) AS annualized_return_pct,
    round(stddevSamp(daily_return) * sqrt(252) * 100, 1)  AS realized_vol_pct,
    round(min(daily_return) * 100, 1)                     AS biggest_drop_pct,
    round(max(daily_return) * 100, 1)                     AS biggest_gain_pct
FROM daily
GROUP BY ticker
ORDER BY ticker

SQL to run

WITH px AS
(
    SELECT
        ticker,
        date,
        toFloat64(any(close)) AS close_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('QQQ', 'SPY')
      AND date >= subtractYears(today(), 5)
      AND date <  today()
    GROUP BY ticker, date
),
daily AS
(
    SELECT
        ticker,
        date,
        close_px,
        close_px / lagInFrame(close_px, 1, close_px)
            OVER (PARTITION BY ticker ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) - 1 AS daily_return
    FROM px
)
SELECT
    ticker,
    formatDateTime(min(date), '%d.%m.%Y')                 AS first_close_on,
    formatDateTime(max(date), '%d.%m.%Y')                 AS last_close_on,
    round((pow(argMax(close_px, date) / argMin(close_px, date),
               365.25 / dateDiff('day', min(date), max(date))) - 1) * 100, 1) AS annualized_return_pct,
    round(stddevSamp(daily_return) * sqrt(252) * 100, 1)  AS realized_vol_pct,
    round(min(daily_return) * 100, 1)                     AS biggest_drop_pct,
    round(max(daily_return) * 100, 1)                     AS biggest_gain_pct
FROM daily
GROUP BY ticker
ORDER BY ticker

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