daily_split

Name: daily_split
Creator: Strasmore
Published: 2026-09-20
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 2026-09-20, from bullish-vs-bearish-meaning.

as of 2026-09-20 table 3×5read in context →

daily_split — 3 rows by 5 columns, computed 2026-09-20 from US exchange, SIP and OPRA data.
label	sessions	share_pct	history_from	history_to
Up day	3167	54.7	Sep 2003	Sep 2026
Down day	2609	45	Sep 2003	Sep 2026
Flat day	16	0.3	Sep 2003	Sep 2026

Rows × columns: 3 × 5
Computed: 2026-09-20
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 daily_split, derived from the stored result.
Column	Type	Range	Notes
`label`	text	3 distinct values (Down day, Flat day, Up day)
`sessions`	number	16 to 3,167
`share_pct`	number	0.3 to 54.7	percent
`history_from`	text	1 distinct value (Sep 2003)
`history_to`	text	1 distinct value (Sep 2026)

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 2026-09-20, 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
        date,
        toFloat64(any(close)) AS close_price
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND close > 0
    GROUP BY date
),
moves AS
(
    SELECT
        date,
        close_price,
        lagInFrame(close_price, 1) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS prev_close
    FROM bars
),
totals AS
(
    SELECT
        countIf(close_price > prev_close)    AS up_days,
        countIf(close_price < prev_close)    AS down_days,
        countIf(close_price = prev_close)    AS flat_days,
        count()                              AS all_days,
        formatDateTime(min(date), '%b %Y')   AS history_from,
        formatDateTime(max(date), '%b %Y')   AS history_to
    FROM moves
    WHERE prev_close > 0
)
SELECT
    label,
    sessions,
    round(100 * sessions / all_days, 1) AS share_pct,
    history_from,
    history_to
FROM totals
ARRAY JOIN
    ['Up day', 'Down day', 'Flat day'] AS label,
    [up_days, down_days, flat_days]    AS sessions

SQL to run

WITH bars AS
(
    SELECT
        date,
        toFloat64(any(close)) AS close_price
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
      AND close > 0
    GROUP BY date
),
moves AS
(
    SELECT
        date,
        close_price,
        lagInFrame(close_price, 1) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS prev_close
    FROM bars
),
totals AS
(
    SELECT
        countIf(close_price > prev_close)    AS up_days,
        countIf(close_price < prev_close)    AS down_days,
        countIf(close_price = prev_close)    AS flat_days,
        count()                              AS all_days,
        formatDateTime(min(date), '%b %Y')   AS history_from,
        formatDateTime(max(date), '%b %Y')   AS history_to
    FROM moves
    WHERE prev_close > 0
)
SELECT
    label,
    sessions,
    round(100 * sessions / all_days, 1) AS share_pct,
    history_from,
    history_to
FROM totals
ARRAY JOIN
    ['Up day', 'Down day', 'Flat day'] AS label,
    [up_days, down_days, flat_days]    AS sessions

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