How far the price travels inside a day: seven ETFs, July 2026 sessions
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 2026-08-02, from Mutual Funds vs ETFs: What Actually Differs.
| ticker | sessions | avg_range_pct | widest_range_pct |
|---|---|---|---|
| QQQ | 22 | 2.25 | 3.92 |
| XLU | 22 | 1.71 | 2.42 |
| IWM | 22 | 1.62 | 2.53 |
| VNQ | 22 | 1.38 | 2.55 |
| AMLP | 22 | 1.35 | 2.11 |
| SPY | 22 | 1.13 | 2.39 |
| VOO | 22 | 1.12 | 2.25 |
- Rows × columns
- 7 × 4
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
ticker |
text | 7 distinct values (AMLP, IWM, QQQ…) | |
sessions |
number | every row is 22 | |
avg_range_pct |
number | 1.12 to 2.25 | percent |
widest_range_pct |
number | 2.11 to 3.92 | 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 , 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 daily AS (
SELECT ticker,
toDate(toTimeZone(window_start, 'America/New_York')) AS session_date,
max(high) AS day_high,
min(low) AS day_low,
argMax(close, window_start) AS day_close
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY', 'VOO', 'QQQ', 'IWM', 'VNQ', 'XLU', 'AMLP')
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2026-07-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-07-31')
GROUP BY ticker, session_date
)
SELECT ticker,
count() AS sessions,
round(avg(100 * toFloat64(day_high - day_low) / toFloat64(day_close)), 2) AS avg_range_pct,
round(max(100 * toFloat64(day_high - day_low) / toFloat64(day_close)), 2) AS widest_range_pct
FROM daily
GROUP BY ticker
ORDER BY avg_range_pct DESC
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