STRASMORE/EXPLORE 2,170 QUERIES

News-feed attention from listing day to the latest reading: counts, concentration, and the trailing week

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 2026-08-20, from SPCX: SpaceX Stock Price Decline From Peak.

as of scalar 1×14read in context →
total articles
627
publishers
4
top publisher
The Motley Fool
top publisher share pct
80.7
busiest month
6
busiest dom
12
ipo day articles
42
peak session articles
31
max daily after peak
29
ipo day minus max after
13
first week avg
25.7
last7 avg
3
tsla co tagged
123
nasdaq titled
32
Rows × columns
1 × 14
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 News-feed attention from listing day to the latest reading: counts, concentration, and the trailing week, derived from the stored result.
ColumnTypeRangeNotes
total_articles number every row is 627
publishers number every row is 4
top_publisher text 1 distinct value (The Motley Fool)
top_publisher_share_pct number every row is 80.7 percent
busiest_month number every row is 6
busiest_dom number every row is 12
ipo_day_articles number every row is 42
peak_session_articles number every row is 31
max_daily_after_peak number every row is 29
ipo_day_minus_max_after number every row is 13
first_week_avg number every row is 25.7
last7_avg number every row is 3
tsla_co_tagged number every row is 123
nasdaq_titled number every row is 32

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 daily_counts AS (
    SELECT toDate(toTimeZone(published_utc, 'America/New_York')) AS d, count() AS n
    FROM global_markets.stocks_news
    WHERE has(tickers, 'SPCX')
      AND published_utc >= toDateTime('2026-06-12 04:00:00') AND published_utc < now()
    GROUP BY d
),
top_pub AS (
    SELECT JSONExtractString(publisher, 'name') AS p, count() AS n
    FROM global_markets.stocks_news
    WHERE has(tickers, 'SPCX')
      AND published_utc >= toDateTime('2026-06-12 04:00:00') AND published_utc < now()
    GROUP BY p ORDER BY n DESC, p ASC LIMIT 1
)
SELECT
    count() AS total_articles,
    uniqExact(JSONExtractString(publisher, 'name')) AS publishers,
    (SELECT p FROM top_pub) AS top_publisher,
    round(100.0 * (SELECT n FROM top_pub) / count(), 1) AS top_publisher_share_pct,
    (SELECT toMonth(argMax(d, (n, -toInt32(toDayOfYear(d))))) FROM daily_counts) AS busiest_month,
    (SELECT toDayOfMonth(argMax(d, (n, -toInt32(toDayOfYear(d))))) FROM daily_counts) AS busiest_dom,
    countIf(toDate(toTimeZone(published_utc, 'America/New_York')) = toDate('2026-06-12')) AS ipo_day_articles,
    countIf(toDate(toTimeZone(published_utc, 'America/New_York')) = toDate('2026-06-16')) AS peak_session_articles,
    (SELECT max(n) FROM daily_counts WHERE d > toDate('2026-06-16')) AS max_daily_after_peak,
    countIf(toDate(toTimeZone(published_utc, 'America/New_York')) = toDate('2026-06-12')) - (SELECT max(n) FROM daily_counts WHERE d > toDate('2026-06-16')) AS ipo_day_minus_max_after,
    (SELECT round(sum(n) / 7, 1) FROM daily_counts WHERE d <= toDate('2026-06-18')) AS first_week_avg,
    (SELECT round(sum(n) / 7, 1) FROM daily_counts WHERE d > today() - 7) AS last7_avg,
    countIf(has(tickers, 'TSLA')) AS tsla_co_tagged,
    countIf(positionCaseInsensitive(title, 'nasdaq') > 0) AS nasdaq_titled
FROM global_markets.stocks_news
WHERE has(tickers, 'SPCX')
  AND published_utc >= toDateTime('2026-06-12 04:00:00') AND published_utc < now()

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