The information flow in one row: volume, composition, and co-tags
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 2026-07-26, from NVDA: NVIDIA's Full June 2026, Tick by Tick.
- Rows × columns
- 1 × 12
- Period covered
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
june_articles |
number | every row is 642 | |
publishers |
number | every row is 4 | |
first_tagged_day |
date | 2026-05-31 | |
peak_day_date |
date | 2026-06-01 | |
peak_day_articles |
number | every row is 45 | |
top_publisher |
text | 1 distinct value (The Motley Fool) | |
top_publisher_articles |
number | every row is 403 | |
top_publisher_pct |
number | every row is 63 | percent |
tsla_co_articles |
number | every row is 69 | |
amd_co_articles |
number | every row is 101 | |
msft_co_articles |
number | every row is 169 | |
aapl_co_articles |
number | every row is 134 |
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
(
SELECT (toString(d), n)
FROM (
SELECT toDate(toTimeZone(published_utc, 'America/New_York')) AS d, count() AS n
FROM global_markets.stocks_news
WHERE has(tickers, 'NVDA')
AND published_utc >= toDateTime('2026-06-01 00:00:00')
AND published_utc < toDateTime('2026-07-01 04:00:00')
GROUP BY d ORDER BY n DESC, d ASC LIMIT 1
)
) AS peak_day,
(
SELECT (JSONExtractString(any(publisher), 'name'), count())
FROM global_markets.stocks_news
WHERE has(tickers, 'NVDA')
AND published_utc >= toDateTime('2026-06-01 00:00:00')
AND published_utc < toDateTime('2026-07-01 04:00:00')
GROUP BY JSONExtractString(publisher, 'name') ORDER BY count() DESC LIMIT 1
) AS top_pub
SELECT
count() AS june_articles,
uniqExact(JSONExtractString(publisher, 'name')) AS publishers,
toString(min(toDate(toTimeZone(published_utc, 'America/New_York')))) AS first_tagged_day,
peak_day.1 AS peak_day_date,
peak_day.2 AS peak_day_articles,
top_pub.1 AS top_publisher,
top_pub.2 AS top_publisher_articles,
round(100.0 * top_pub.2 / count(), 0) AS top_publisher_pct,
countIf(has(tickers, 'TSLA')) AS tsla_co_articles,
countIf(has(tickers, 'AMD')) AS amd_co_articles,
countIf(has(tickers, 'MSFT')) AS msft_co_articles,
countIf(has(tickers, 'AAPL')) AS aapl_co_articles
FROM global_markets.stocks_news
WHERE has(tickers, 'NVDA')
AND published_utc >= toDateTime('2026-06-01 00:00:00')
AND published_utc < toDateTime('2026-07-01 04:00:00')
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