follow_through

Name: follow_through
Creator: Strasmore
Published: 2026-09-26
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 2026-09-26, from msft-premarket-and-after-hours-prices.

as of 2026-09-26 ranking 4×4read in context →

follow_through — 4 rows by 4 columns, computed 2026-09-26 from US exchange, SIP and OPRA data.
gap_bucket	sessions	continuation_pct	median_aligned_pct
0.25-0.50%	649	48.2	-0.04
0.50-1.00%	658	48	-0.09
1.00-2.00%	365	53.4	0.12
2.00%+	138	51.4	0.14

Rows × columns: 4 × 4
Computed: 2026-09-26
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 follow_through, derived from the stored result.
Column	Type	Range	Notes
`gap_bucket`	text	4 distinct values (0.25-0.50%, 0.50-1.00%, 1.00-2.00%…)
`sessions`	number	138 to 658
`continuation_pct`	number	48 to 53.4	percent
`median_aligned_pct`	number	-0.09 to 0.14	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 2026-09-26, 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,
        any(open)  AS open_px,
        any(close) AS close_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'MSFT'
      AND date >= '2016-01-01'
      AND date <  '2026-09-01'
    GROUP BY date
),
gaps AS
(
    SELECT
        date,
        round((toFloat64(open_px) / prev_close - 1) * 100, 3)        AS gap_pct,
        round((toFloat64(close_px) / toFloat64(open_px) - 1) * 100, 3) AS session_pct
    FROM
    (
        SELECT
            date,
            open_px,
            close_px,
            lagInFrame(toFloat64(close_px)) OVER (ORDER BY date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
        FROM bars
    )
    WHERE prev_close > 0
)
SELECT
    multiIf(abs(gap_pct) < 0.50, '0.25-0.50%',
            abs(gap_pct) < 1.00, '0.50-1.00%',
            abs(gap_pct) < 2.00, '1.00-2.00%',
                                 '2.00%+')                                                                  AS gap_bucket,
    count()                                                                                                  AS sessions,
    round(100.0 * countIf(sign(session_pct) = sign(gap_pct)) / count(), 1)                                    AS continuation_pct,
    round(quantileDeterministic(0.5)(session_pct * sign(gap_pct), toUInt64(toYYYYMMDD(date))), 2)             AS median_aligned_pct
FROM gaps
WHERE abs(gap_pct) >= 0.25
GROUP BY gap_bucket
HAVING count() > 0
ORDER BY min(abs(gap_pct)) ASC

SQL to run

WITH bars AS
(
    SELECT
        date,
        any(open)  AS open_px,
        any(close) AS close_px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'MSFT'
      AND date >= '2016-01-01'
      AND date <  '2026-09-01'
    GROUP BY date
),
gaps AS
(
    SELECT
        date,
        round((toFloat64(open_px) / prev_close - 1) * 100, 3)        AS gap_pct,
        round((toFloat64(close_px) / toFloat64(open_px) - 1) * 100, 3) AS session_pct
    FROM
    (
        SELECT
            date,
            open_px,
            close_px,
            lagInFrame(toFloat64(close_px)) OVER (ORDER BY date ASC ROWS BETWEEN 1 PRECEDING AND CURRENT ROW) AS prev_close
        FROM bars
    )
    WHERE prev_close > 0
)
SELECT
    multiIf(abs(gap_pct) < 0.50, '0.25-0.50%',
            abs(gap_pct) < 1.00, '0.50-1.00%',
            abs(gap_pct) < 2.00, '1.00-2.00%',
                                 '2.00%+')                                                                  AS gap_bucket,
    count()                                                                                                  AS sessions,
    round(100.0 * countIf(sign(session_pct) = sign(gap_pct)) / count(), 1)                                    AS continuation_pct,
    round(quantileDeterministic(0.5)(session_pct * sign(gap_pct), toUInt64(toYYYYMMDD(date))), 2)             AS median_aligned_pct
FROM gaps
WHERE abs(gap_pct) >= 0.25
GROUP BY gap_bucket
HAVING count() > 0
ORDER BY min(abs(gap_pct)) ASC

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