STRASMORE/EXPLORE 2,170 QUERIES 22Y EQUITIES · 12Y OPTIONS

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The Low-Volatility Anomaly
Growth of $100: a calm name (KO), the market (SPY), a wild name (NVDA), weeklyseries · 2026-08-22 · 115×4Preview: a 16-point series, ending higher. Annualized volatility vs total return, 25 large caps, calmest to wildest (~2 years)ranking · 2026-08-22 · 25×3Preview: 16 ranked values, smallest first. Volatility thirds: median return, and the range from worst to best name in eachtable · 2026-08-22 · 3×6
The Lowest-Volatility Stocks
The calmest large caps: annualized realized volatility over the past year, lowest firstranking · 2026-08-22 · 15×2Preview: 15 ranked values, smallest first. Maximum drawdown of the calmest names: the worst peak-to-trough fall over the past yearranking · 2026-08-22 · 8×2Preview: 8 ranked values, largest first.
Growth of $100: a calm name (KO), the market (SPY), a wild name (NVDA), weekly

Growth of $100: a calm name (KO), the market (SPY), a wild name (NVDA), weekly

most recentas of series 115×4read in context →
Growth of $100: a calm name (KO), the market (SPY), a wild name (NVDA), weekly — 115 rows by 4 columns, computed from US exchange, SIP and OPRA data.
weekko_calmspy_marketnvda_wild
2024-06-1099.4100.1102
2024-06-1799.7100.398.2
2024-06-24101.1100.395.4
2024-07-01101.2102.397.2
2024-07-08101.2103.299.8
2024-07-15103.7101.291.2
2024-07-22106.5100.487.4
2024-07-29110.198.382.9
2024-08-05109.198.380.9
2024-08-12109.9102.296.3
2024-08-19110.8103.699.9
2024-08-26115.1103.992.3
2024-09-0211399.679.5
2024-09-09113.4103.692
2024-09-16113.8104.789.7
2024-09-23114105.393.8
2024-09-30111.4105.696.5
2024-10-07110.5106.8104.2
2024-10-14111.8107.8106.7
2024-10-21106.3106.8109.4
2024-10-28103.2105.3104.7
2024-11-04101.5110.3114
2024-11-1198108109.7
2024-11-18101.5109.8109.7
2024-11-25101.8111.1106.9
2024-12-0299.3112.1110.1
2024-12-09100.3111.4103.7
2024-12-1699.4108.9104.3
2024-12-2399.2109.7106
2024-12-3098109.1111.8
2025-01-0697107105
2025-01-1399.6110.2106.5
2025-01-2098.3112.1110.2
2025-01-27100.811192.8
2025-02-03101.4110.8100.4
2025-02-10109.4112.4107.3
2025-02-17113.4110.6103.8
2025-02-24113.1109.596.7
2025-03-03113.4106.287.1
2025-03-10109.8103.893.9
2025-03-1710910490.9
2025-03-24111.8102.584.7
2025-03-31111.193.272.9
2025-04-07113.398.485.6
2025-04-14115.99778.4
2025-04-21114.2101.585.8
2025-04-28113.8104.588.4
2025-05-0511210490.1
2025-05-12114.4109.6104.6
2025-05-19114106.8101.5
2025-05-26114.5108.7104.2
2025-06-02113.3110.4109.5
2025-06-09112.8110.1109.7
2025-06-16109.3109.6111.1
2025-06-23111.7113.4121.9
2025-06-30113.3115.3123.1
2025-07-07111115127.4
2025-07-14110.9115.7133.2
2025-07-21109.8117.4134.1
2025-07-28109.3114.6134.2
2025-08-04111.7117.5141.3
2025-08-11111.1118.6139.5
2025-08-18111.4119137.6
2025-08-25109.6118.9134.7
2025-09-01107.9119.3129.1
2025-09-08106.4121.2137.4
2025-09-15105.5122.4136.4
2025-09-22104.2122137.7
2025-09-29105.8123.4145
2025-10-06106.5120.4141.5
2025-10-13108.7122.5141.6
2025-10-20110.7124.8144
2025-10-27109.4125.7156.5
2025-11-03112123.7145.5
2025-11-10113123.9147.1
2025-11-17115.9121.5138.3
2025-11-24116.1126136.8
2025-12-01111.1126.4141
2025-12-08112125.7135.3
2025-12-15111.3125.5139.9
2025-12-22111127.3147.3
2025-12-29109.8126145.9
2026-01-05112127.9142.9
2026-01-12111.8127.5143.9
2026-01-19115.7127.1145.1
2026-01-26118.9127.6147.9
2026-02-02125.5127.3143.3
2026-02-09124.9125.7141.3
2026-02-16126.8127.1146.7
2026-02-23129.5126.5136.9
2026-03-02122.4124137.4
2026-03-09122.8122.1139.3
2026-03-16118.7119.6133.7
2026-03-23120.2116.9129.4
2026-03-30121.8120.9137.1
2026-04-06123125.2145.8
2026-04-13120.3130.9155.9
2026-04-20121.7131.6160.9
2026-04-27124.8132.9153.4
2026-05-04124.5136166.3
2026-05-11128.4136.3174.2
2026-05-18129.4137.5166.4
2026-05-25125.5139.5163.2
2026-06-01126.3136158.5
2026-06-08131.2136.7158.6
2026-06-15126.1137.6162.5
2026-06-22131.2134.4148.2
2026-06-29133.4137.3150.4
2026-07-06132.6139.2163.1
2026-07-13129.5137156.6
2026-07-20130.6136.2160
2026-07-27139.1137.7155.2
2026-08-03138.2142.6173.1
2026-08-10139.3143.1174
2026-08-17144.6141.4166.5
the exact SQL behind every number
WITH d AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS dt,
           argMax(toFloat64(close), toTimeZone(window_start, 'America/New_York')) AS c
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN ('KO','SPY','NVDA')
      AND window_start >= now() - INTERVAL 800 DAY
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
    GROUP BY ticker, dt
),
r AS (
    SELECT ticker, dt,
           c / lagInFrame(c) OVER (PARTITION BY ticker ORDER BY dt) - 1 AS ret
    FROM d
),
f AS (
    SELECT ticker, dt, ret FROM r WHERE ret IS NOT NULL AND ret > -0.5 AND ret < 0.5
),
cum AS (
    SELECT ticker, dt,
           100 * exp(sum(log(1 + ret)) OVER (PARTITION BY ticker ORDER BY dt)) AS idx
    FROM f
),
wk AS (
    SELECT ticker, toMonday(dt) AS week, argMax(idx, dt) AS wv
    FROM cum GROUP BY ticker, week
)
SELECT week,
       round(maxIf(wv, ticker = 'KO'), 1) AS ko_calm,
       round(maxIf(wv, ticker = 'SPY'), 1) AS spy_market,
       round(maxIf(wv, ticker = 'NVDA'), 1) AS nvda_wild
FROM wk
GROUP BY week
ORDER BY week
$