recent_events

Name: recent_events
Creator: Strasmore
Published: 2026-10-07
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 2026-10-07, from dragonfly-doji-follow-through.

as of 2026-10-07 series 12×6read in context →

recent_events — 12 rows by 6 columns, computed 2026-10-07 from US exchange, SIP and OPRA data.
session_date	session_label	ticker	fwd_1_pct	fwd_5_pct	fwd_20_pct
2026-04-10	10-04-2026	JPM	1.23	0.14	-2.51
2026-04-15	15-04-2026	CSCO	2.6	9.03	23.69
2026-04-16	16-04-2026	META	1.73	-2.62	-8.63
2026-04-16	16-04-2026	MSFT	0.6	-1.07	-2.58
2026-05-07	07-05-2026	XOM	-1.37	4.23	2.28
2026-05-13	13-05-2026	CVX	0.34	2.87	-0.1
2026-05-29	29-05-2026	CVX	1.85	2.66	-7.67
2026-05-29	29-05-2026	META	-5.07	-6.25	-11.05
2026-06-12	12-06-2026	MSFT	2.31	-5.99	-1.49
2026-06-23	23-06-2026	UNH	-0.84	1.56	5.39
2026-07-10	10-07-2026	GOOGL	-1.31	-2.91	-0.81
2026-08-25	25-08-2026	JPM	-0.05	-0.49	-5.37

Rows × columns: 12 × 6
Period covered: 2026-04-10 to 2026-08-25
Computed: 2026-10-07
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 recent_events, derived from the stored result.
Column	Type	Range	Notes
`session_date`	date	2026-04-10 to 2026-08-25
`session_label`	text	10 distinct values (07-05-2026, 10-04-2026, 10-07-2026…)
`ticker`	text	8 distinct values (CSCO, CVX, GOOGL…)
`fwd_1_pct`	number	-5.07 to 2.6	percent
`fwd_5_pct`	number	-6.25 to 9.03	percent
`fwd_20_pct`	number	-11.05 to 23.69	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 2026-10-07, 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 bars AS
(
    SELECT
        ticker,
        date,
        toFloat64(open)  AS o,
        toFloat64(high)  AS h,
        toFloat64(low)   AS l,
        toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('AAPL','MSFT','NVDA','AMZN','GOOGL','META','JPM','XOM','JNJ','KO','PG','WMT','HD','UNH','CVX','MRK','CSCO','PEP','SPY','QQQ')
      AND date >= '2006-01-01'
      AND date <  '2026-10-01'
      AND high > low
      AND low > 0
),
shaped AS
(
    SELECT
        ticker,
        date,
        c,
        h - l              AS rng,
        abs(c - o)         AS body,
        h - greatest(o, c) AS upper_wick,
        0.01 * o           AS min_range,
        leadInFrame(c, 1)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd1,
        leadInFrame(c, 5)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd5,
        leadInFrame(c, 20) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd20
    FROM bars
)
SELECT
    toString(date)                        AS session_date,
    formatDateTime(date, '%d-%m-%Y')      AS session_label,
    ticker,
    round(100 * (c_fwd1  / c - 1), 2)     AS fwd_1_pct,
    round(100 * (c_fwd5  / c - 1), 2)     AS fwd_5_pct,
    round(100 * (c_fwd20 / c - 1), 2)     AS fwd_20_pct
FROM
(
    SELECT *
    FROM shaped
    WHERE rng >= min_range
      AND body       <= 0.10 * rng
      AND upper_wick <= 0.10 * rng
      AND c_fwd1  > 0
      AND c_fwd5  > 0
      AND c_fwd20 > 0
    ORDER BY date DESC
    LIMIT 12
)
ORDER BY session_date

SQL to run

WITH bars AS
(
    SELECT
        ticker,
        date,
        toFloat64(open)  AS o,
        toFloat64(high)  AS h,
        toFloat64(low)   AS l,
        toFloat64(close) AS c
    FROM global_markets.stocks_daily_aggs
    WHERE ticker IN ('AAPL','MSFT','NVDA','AMZN','GOOGL','META','JPM','XOM','JNJ','KO','PG','WMT','HD','UNH','CVX','MRK','CSCO','PEP','SPY','QQQ')
      AND date >= '2006-01-01'
      AND date <  '2026-10-01'
      AND high > low
      AND low > 0
),
shaped AS
(
    SELECT
        ticker,
        date,
        c,
        h - l              AS rng,
        abs(c - o)         AS body,
        h - greatest(o, c) AS upper_wick,
        0.01 * o           AS min_range,
        leadInFrame(c, 1)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd1,
        leadInFrame(c, 5)  OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd5,
        leadInFrame(c, 20) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN CURRENT ROW AND 20 FOLLOWING) AS c_fwd20
    FROM bars
)
SELECT
    toString(date)                        AS session_date,
    formatDateTime(date, '%d-%m-%Y')      AS session_label,
    ticker,
    round(100 * (c_fwd1  / c - 1), 2)     AS fwd_1_pct,
    round(100 * (c_fwd5  / c - 1), 2)     AS fwd_5_pct,
    round(100 * (c_fwd20 / c - 1), 2)     AS fwd_20_pct
FROM
(
    SELECT *
    FROM shaped
    WHERE rng >= min_range
      AND body       <= 0.10 * rng
      AND upper_wick <= 0.10 * rng
      AND c_fwd1  > 0
      AND c_fwd5  > 0
      AND c_fwd20 > 0
    ORDER BY date DESC
    LIMIT 12
)
ORDER BY session_date

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