STRASMORE/EXPLORE 3,022 QUERIES

How far SPY travels after the minute your signal fired (June 2024)

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 2026-10-04, from Event-Driven vs Vectorized Backtesting.

as of ranking 5×3read in context →
How far SPY travels after the minute your signal fired (June 2024) — 5 rows by 3 columns, computed from US exchange, SIP and OPRA data.
delay_minutesmedian_move_bpsp95_move_bps
11.474.99
22.027.18
53.1111.49
155.2419.64
307.6626.73
Rows × columns
5 × 3
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 How far SPY travels after the minute your signal fired (June 2024), derived from the stored result.
ColumnTypeRangeNotes
delay_minutes number 1 to 30
median_move_bps number 1.47 to 7.66
p95_move_bps number 4.99 to 26.73

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.

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 minute_px AS
(
    SELECT
        toStartOfMinute(toTimeZone(window_start, 'America/New_York')) AS et_minute,
        toFloat64(any(close))                                         AS close
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker = 'SPY'
      AND window_start >= '2024-06-03 00:00:00'
      AND window_start <  '2024-06-29 00:00:00'
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) >= 570
      AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
           + toMinute(toTimeZone(window_start, 'America/New_York'))) < 960
    GROUP BY et_minute
),
fills AS
(
    SELECT
        et_minute,
        close,
        arrayZip([1, 2, 5, 15, 30],
                 [leadInFrame(close, 1)  OVER w,
                  leadInFrame(close, 2)  OVER w,
                  leadInFrame(close, 5)  OVER w,
                  leadInFrame(close, 15) OVER w,
                  leadInFrame(close, 30) OVER w]) AS fill_prices
    FROM minute_px
    WINDOW w AS (PARTITION BY toDate(et_minute) ORDER BY et_minute ASC ROWS BETWEEN CURRENT ROW AND 30 FOLLOWING)
),
pairs AS
(
    SELECT
        et_minute,
        close,
        arrayJoin(fill_prices) AS fill
    FROM fills
)
SELECT
    toUInt16(fill.1)                                                                                            AS delay_minutes,
    round(quantileDeterministic(0.5)(abs(fill.2 / close - 1) * 10000, toUInt32(toUnixTimestamp(et_minute))), 2)  AS median_move_bps,
    round(quantileDeterministic(0.95)(abs(fill.2 / close - 1) * 10000, toUInt32(toUnixTimestamp(et_minute))), 2) AS p95_move_bps
FROM pairs
WHERE fill.2 > 0
GROUP BY delay_minutes
ORDER BY delay_minutes
⌘/Ctrl + Enter

Work with this data in your AI assistant

Opens ready to query, with this page's data. Free, no account.

More from this analysisEvent-Driven vs Vectorized Backtesting
Median shares per executed print, 2024 ranking 5×2 → Distance from the signal close to the next session's open, 2024 ranking 5×3 → One rule, two fill conventions: SPY equity curves through 2024 series 12×4 → Three runs of backtest.py on the illustrative 60-bar file ranking 3×4 → Survivorship in the universe: names trading each year, share still listed in July 2026, and median return table 10×6 → Same-bar decision vs a one-session lag: SPY, average session gain, 2016-2025 table 10×5 → See all 3,022 queries →