STRASMORE/EXPLORE 3,256 QUERIES 22Y EQUITIES · 12Y OPTIONS

3,256 answered market questions

every one with its exact SQL, its result and the date it was computed · free, no signup

Can a Death Cross Be Bullish? The SPY Record
The window and the cross counts behind the textscalar · 2026-10-04 · 1×115,803 Each SPY death cross and the golden cross that ended itranking · 2026-10-04 · 11×4Preview: 11 ranked values, largest first. SPY close versus its 50-day and 200-day averages, February to July 2020series · 2026-10-04 · 126×4Preview: a 16-point series, ending higher. Forward record after a SPY death cross, by horizontable · 2026-10-04 · 4×9 Every SPY death cross in the daily data, with forward returnstable · 2026-10-04 · 11×6 The 2020 peak, low, death cross and golden cross, dated from the same barsscalar · 2026-10-04 · 1×16338.34
Do Volume Indicators Predict Anything?
OBV and VPT rebuilt from daily closes and volume, AAPL, Q2 2026series · 2026-10-01 · 62×4Preview: a 16-point series, ending higher. Forward returns after an OBV divergence vs matched confirmation daystable · 2026-10-01 · 3×7 Divergence gap in 20-session forward return across four lookbackstable · 2026-10-01 · 4×5 20-session OBV change vs trailing and forward returns, by tickerranking · 2026-10-01 · 10×4Preview: 10 ranked values, largest first.
What Is the TTM Squeeze? Formula and Limits
Bollinger width against Keltner width, KO, first quarter 2026series · 2026-09-27 · 61×5Preview: a 16-point series, ending higher. Share of sessions in a squeeze, six liquid names, three yearsranking · 2026-09-27 · 6×3Preview: 6 ranked values, largest first. Squeeze sessions found by each parameter set, KO, three yearsranking · 2026-09-27 · 6×3Preview: 6 ranked values, largest first. What the next ten sessions did, squeeze against no squeezeranking · 2026-09-27 · 2×4Preview: 2 ranked values, smallest first.
Anchored VWAP Explained: Formula and Uses
How much the newest session can move an anchored VWAP (AAPL)ranking · 2026-08-07 · 13×2Preview: 13 ranked values, smallest first. Anchored at each name's own lowest close of the past twelve monthstable · 2026-08-07 · 5×5 Daily session VWAP against a VWAP anchored on one date (AAPL)series · 2026-08-07 · 62×4Preview: a 16-point series, ending higher. The same stock and the same last price, twelve different anchors (AAPL)ranking · 2026-08-07 · 12×3Preview: 12 ranked values, smallest first.
Do Stock Gaps Always Get Filled? The Data
Average one-minute range and volume by time of day, 2025series · 2026-08-06 · 26×3Preview: a 16-point series, ending lower. Same-session gap fill rate by gap size, eight large caps, 2021 to 2026ranking · 2026-08-06 · 5×3Preview: 5 ranked values, largest first. Gap fill rate by how heavy the gap day's volume wasranking · 2026-08-06 · 3×4Preview: 3 ranked values, largest first. Share of 1%+ gaps filled, by how long you waitranking · 2026-08-06 · 4×3Preview: 4 ranked values, largest first.
The window and the cross counts behind the text

The window and the cross counts behind the text

most recentas of scalar 1×11read in context →
first session
Sep 10, 2003
last session
Oct 2, 2026
session count
5,803
median off high pct
10.2
min off high pct
5.2
max off high pct
22.7
death cross count
11
golden cross count
11
min sessions to golden
37
median sessions to golden
77
max sessions to golden
377
the exact SQL behind every number
WITH
bars AS
(
    SELECT
        date,
        toFloat64(argMax(close, _ingest_time)) AS px
    FROM global_markets.stocks_daily_aggs
    WHERE ticker = 'SPY'
    GROUP BY date
),
smas AS
(
    SELECT
        date,
        px,
        row_number() OVER (ORDER BY date) AS rn,
        avg(px) OVER (ORDER BY date ROWS BETWEEN 49 PRECEDING AND CURRENT ROW) AS avg_50,
        avg(px) OVER (ORDER BY date ROWS BETWEEN 199 PRECEDING AND CURRENT ROW) AS avg_200,
        max(px) OVER (ORDER BY date ROWS BETWEEN 251 PRECEDING AND CURRENT ROW) AS high_1y
    FROM bars
),
states AS
(
    SELECT
        *,
        (avg_50 < avg_200) AS below,
        lagInFrame((avg_50 < avg_200), 1) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS prev_below
    FROM smas
),
flags AS
(
    SELECT
        *,
        (rn >= 201 AND below = 1 AND prev_below = 0) AS is_death,
        (rn >= 201 AND below = 0 AND prev_below = 1) AS is_golden
    FROM states
),
events AS
(
    SELECT date, rn, is_death
    FROM flags
    WHERE is_death OR is_golden
),
paired AS
(
    SELECT
        rn,
        is_death,
        leadInFrame(rn, 1) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND UNBOUNDED FOLLOWING) AS next_rn
    FROM events
),
cross_stats AS
(
    SELECT
        countIf(is_death)                                                           AS death_cross_count,
        countIf(NOT is_death)                                                       AS golden_cross_count,
        minIf(toInt64(next_rn) - toInt64(rn), is_death AND next_rn > 0)            AS min_sessions_to_golden,
        quantileExactIf(toInt64(next_rn) - toInt64(rn), is_death AND next_rn > 0)  AS median_sessions_to_golden,
        maxIf(toInt64(next_rn) - toInt64(rn), is_death AND next_rn > 0)            AS max_sessions_to_golden
    FROM paired
),
span AS
(
    SELECT
        concat(formatDateTime(min(date), '%b'), ' ', toString(toDayOfMonth(min(date))), ', ', toString(toYear(min(date)))) AS first_session,
        concat(formatDateTime(max(date), '%b'), ' ', toString(toDayOfMonth(max(date))), ', ', toString(toYear(max(date)))) AS last_session,
        count()                                                        AS session_count,
        round(quantileExactIf((1 - px / high_1y) * 100, is_death), 1)  AS median_off_high_pct,
        round(minIf((1 - px / high_1y) * 100, is_death), 1)            AS min_off_high_pct,
        round(maxIf((1 - px / high_1y) * 100, is_death), 1)            AS max_off_high_pct
    FROM flags
)
SELECT *
FROM span, cross_stats
$