STRASMORE/EXPLORE 3,256 QUERIES 22Y EQUITIES · 12Y OPTIONS

3,256 answered market questions

every one with its exact SQL, its result and the date it was computed · free, no signup

How OHLCV Bars Are Built From Ticks
SPY volume by minute into the close, June 10 2026series · 2026-08-08 · 25×3Preview: a 16-point series, ending higher. One-minute AAPL bars rebuilt from individual trades, June 10 2026series · 2026-08-08 · 15×6Preview: a 15-point series, ending lower.
Trade Condition Codes Explained
Share of a session's prints carrying price-ineligible conditionsranking · 2026-08-07 · 5×4Preview: 5 ranked values, largest first. Two versions of the same AAPL session high, fifteen minutes at a timeseries · 2026-08-07 · 26×3Preview: a 16-point series, ending lower. Condition code families defined for US stocksranking · 2026-08-07 · 8×2Preview: 8 ranked values, largest first. One AAPL session, every print grouped by its sale conditionranking · 2026-08-07 · 12×3Preview: 12 ranked values, largest first.
Level 1 vs Level 2 vs Level 3 Market Data
Executed volume by price level: the traded ladder (KO, 30 minutes)table · 2026-08-07 · 63×3 Level 1 top of book: quoted spread across a full session (AAPL, June 17 2026)series · 2026-08-07 · 32×3Preview: a 16-point series, ending lower. Top-of-book message rate, minute by minute (NVDA, one hour)series · 2026-08-07 · 60×3Preview: a 16-point series, roughly flat. Top-of-book spread and quote message count by name (10:00 to 11:00 a.m. ET)ranking · 2026-08-07 · 5×3Preview: 5 ranked values, smallest first.
Why Stock Quotes Are Delayed 15 Minutes
How often the last price changes, minute by minute (June 2026)ranking · 2026-08-06 · 6×3Preview: 6 ranked values, largest first. The 15 minute gap across six household tickers (June 2026)ranking · 2026-08-06 · 6×3Preview: 6 ranked values, largest first. How far SPY travels in 15 minutes, by hour of the session (June 2026)series · 2026-08-06 · 7×4Preview: a 7-point series, ending higher. Live price against a 15 minute delayed view, one SPY sessionseries · 2026-08-06 · 37×5Preview: a 16-point series, ending lower.
AI Daily Market Research Reports: What Breaks
SPY: average overnight repricing vs average regular-session move, monthlyseries · 2026-08-01 · 24×3Preview: a 16-point series, roughly flat. What the next session did: 36 large caps bucketed by the prior day's move, Aug 2024 to Jul 2026table · 2026-08-01 · 5×5 When headlines actually land: article counts by ET clock hour, July 2026ranking · 2026-08-01 · 24×4Preview: 16 ranked values, smallest first. Headline coverage by size of daily move: 36 large caps, May to July 2026ranking · 2026-08-01 · 5×4Preview: 5 ranked values, smallest first. Daily turnover in the top ten movers: fixed 36 name large-cap list, July 2026series · 2026-08-01 · 21×3Preview: a 16-point series, ending lower.
What Is the Efficient Market Hypothesis?
The index against the names inside it: calendar years 2021 to 2025, a 34-name large-cap baskettable · 2026-07-31 · 5×5 What followed each kind of session: next-day outcome by the prior day's move, same twelve namesranking · 2026-07-31 · 5×4Preview: 5 ranked values, largest first. Lag-one autocorrelation of daily returns: twelve household names, July 2021 to June 2026ranking · 2026-07-31 · 12×4Preview: 12 ranked values, smallest first.
What Missing the Best Days Costs
SPY total return since 2016, after removing the best single daysranking · 2026-07-16 · 5×2Preview: 5 ranked values, smallest first. SPY's 20 best and 20 worst days since 2016, counted by yearranking · 2026-07-16 · 11×3Preview: 11 ranked values, smallest first. The ten biggest single-day gains for SPY since 2016ranking · 2026-07-16 · 10×2Preview: 10 ranked values, largest first.
How Markets Recover From Crashes
The S&P 500's underwater curve: worst drawdown from a prior high, by monthseries · 2026-07-16 · 127×2Preview: a 16-point series, ending higher. The S&P 500's underwater record since 2016 (SPY, one scorecard)scalar · 2026-07-16 · 1×634.2 S&P 500 worst intra-year drawdown vs the year's price return, since 2016ranking · 2026-07-16 · 11×3Preview: 11 ranked values, largest first. How far below its high the S&P 500 sits: share of trading days since 2016ranking · 2026-07-16 · 5×2Preview: 5 ranked values, largest first.
Buy When Others Are Fearful: The Data
Average S&P 500 forward return after fearful, greedy, and ordinary days (2024-2026)table · 2026-07-16 · 3×5 News sentiment coverage by year: how many tagged insights, and the share downbeatranking · 2026-07-16 · 3×3Preview: 3 ranked values, smallest first.
Does a Stock Go Up After a Split?
The best-known recent stock splits: the six months before vs the three months afterseries · 2026-07-15 · 7×6Preview: a 7-point series, ending lower. Every liquid forward split, 2016-2025: median 3-month return after the split vs the S&P 500, by periodtable · 2026-07-15 · 3×7
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NVDA quote updates per second, grouped by how busy the second was

NVDA quote updates per second, grouped by how busy the second was

most recentas of ranking 5×3read in context →
NVDA quote updates per second, grouped by how busy the second was — 5 rows by 3 columns, computed from US exchange, SIP and OPRA data.
updates_in_the_secondshare_of_seconds_pctshare_of_messages_pct
1 update0.20
2 to 51.70.1
6 to 208.11.3
21 to 10062.442.9
over 10027.655.8
the exact SQL behind every number
WITH
    per_second AS
    (
        SELECT
            toDateTime(sip_timestamp) AS second_bucket,
            count()                   AS messages
        FROM global_markets.cache_stocks_quotes
        WHERE ticker = 'NVDA'
          AND sip_timestamp >= '2026-09-15 13:30:00'
          AND sip_timestamp <  '2026-09-15 15:30:00'
        GROUP BY second_bucket
    ),
    totals AS
    (
        SELECT
            count()       AS second_count,
            sum(messages) AS message_count
        FROM per_second
    )
SELECT
    multiIf(messages = 1,    '1 update',
            messages <= 5,   '2 to 5',
            messages <= 20,  '6 to 20',
            messages <= 100, '21 to 100',
            'over 100')                                                  AS updates_in_the_second,
    round(100.0 * count() / (SELECT second_count FROM totals), 1)         AS share_of_seconds_pct,
    round(100.0 * sum(messages) / (SELECT message_count FROM totals), 1) AS share_of_messages_pct
FROM per_second
GROUP BY updates_in_the_second
ORDER BY min(messages)
$