QQQ top up-days vs top down-days: are they clustered within the same turbulent stretches?
QQQ’s single biggest up-day (+13.62% on 2025-04-09) landed within a few sessions of its single biggest down-day (-6.18% on 2025-04-04), which makes for a compelling anecdote — but is it the rule? We tested that directly by scanning roughly three years of daily returns (751 trading days), picking the ten largest up-days and ten largest down-days, and measuring the trading‑day distance from each extreme to its nearest opposite extreme.
The result is mixed. The median nearest‑opposite distance is 9.5 days (up→down) and 8.5 days (down→up); only 20% of top up-days and 40% of top down-days fall within three trading days of an opposite extreme, and the 20 extremes span 422 trading days (56.2% of the window). There are clear turbulent clusters (notably early April 2025), but most extremes are separated by about one to two weeks — a suggestive lean toward partial bundling, not a definitive collapse of the “just dodge the bad days” idea. Full methodology and charts follow below.
For QQQ over the past ~3 years, are its ten best single-day returns clustered right alongside its ten worst — close enough that sidestepping the crashes would also have cost you the rebounds? Thesis: the biggest up-days overwhelmingly land within a handful of sessions of the biggest down-days, both bunched into the same turbulent stretches, so the 'just dodge the bad days' market-timing fantasy collapses because the sharpest gains and losses come bundled together.
How this was measured
Resampled QQQ minute bars to daily closes over the last ~3 years (anchored to the latest available day), computed daily close-to-close returns, and identified the top-N up days and top-N down days (N up to 10, reduced if the sample is small). For each up-day, computed the trading-day distance to the nearest down-day among the N worst, and vice versa for each down-day to the nearest up-day. Summarized the distribution of nearest-opposite distances (median and share within 3 and 5 trading days) and showed category counts (≤1, ≤3, ≤5, ≤10, >10). Also reported the trading-day span from the earliest to latest of the 2N extremes relative to the full window to indicate whether extremes bunch into compact turbulent stretches.
The key numbers
Reading the numbers
Over 751 trading days we picked the 10 biggest up-days and 10 biggest down-days; the median distance from an up to the nearest down is 9.5 trading days (and 8.5 the other way). Only 2 of 10 up-days (20%) and 4 of 10 down-days (40%) fall within 3 trading days.
The charts
This daily-return line flags the ten biggest gains and losses: the single biggest up-day was 13.62% (2025-04-09) and the single biggest down-day was -6.18% (2025-04-04), so the top up and top down are five trading days apart and visibly sit in the same early-April spike. The ten top up-days average 4.55% and the ten top down-days average -4.14%, so when extremes occur they tend to be large but not all are paired. Look at that early-April cluster as an example of gains and losses bunched together, but note the rest of the highlighted points are scattered through the three-year line.
This distance-count bar chart breaks where each extreme’s nearest opposite fell: five of the 20 extremes are within one trading day of an opposite extreme, and the bins show 9 of 20 lie more than 10 days apart. Put another way, only 2 of the 10 up-days are within three trading days of a down-day and 4 of 10 down-days are within three days of an up-day, so tight one-week pairings are a minority, not the rule.
The box-style summaries show medians (reported elsewhere) around 9.5 days for up→down and 8.5 days for down→up, while the means are much larger (28.6 and 25.3 trading days) because a few extremes sit very far apart (max distances 92 and 81 days). That skew — short medians but long means and very large maxima — tells the story: some big gains and losses are bunched tightly (short distances), but many others are separated by weeks or months, so extremes are only partly bundled into turbulent stretches.
Top up-days and their nearest top down-day
| up_day | up_ret | nearest_down_day | nearest_down_ret | trading_days_apart |
|---|---|---|---|---|
| 2025-04-09 | 0.1362 | 2025-04-10 | -0.0491 | 1 |
| 2026-03-31 | 0.0434 | 2025-11-20 | -0.0373 | 88 |
| 2024-08-08 | 0.0429 | 2024-08-01 | -0.0384 | 5 |
| 2025-04-22 | 0.0426 | 2025-04-10 | -0.0491 | 7 |
| 2025-05-12 | 0.0388 | 2025-04-10 | -0.0491 | 21 |
| 2024-07-31 | 0.0323 | 2024-08-01 | -0.0384 | 1 |
| 2026-02-06 | 0.031 | 2025-11-20 | -0.0373 | 52 |
| 2026-04-07 | 0.0308 | 2025-11-20 | -0.0373 | 92 |
| 2025-04-24 | 0.0307 | 2025-04-10 | -0.0491 | 9 |
| 2024-08-15 | 0.0264 | 2024-08-01 | -0.0384 | 10 |
Top down-days and their nearest top up-day
| down_day | down_ret | nearest_up_day | nearest_up_ret | trading_days_apart |
|---|---|---|---|---|
| 2025-04-04 | -0.0618 | 2025-04-09 | 0.1362 | 3 |
| 2025-04-10 | -0.0491 | 2025-04-09 | 0.1362 | 1 |
| 2025-03-10 | -0.0438 | 2025-04-09 | 0.1362 | 22 |
| 2025-04-08 | -0.0437 | 2025-04-09 | 0.1362 | 1 |
| 2025-04-02 | -0.0387 | 2025-04-09 | 0.1362 | 5 |
| 2024-08-01 | -0.0384 | 2024-07-31 | 0.0323 | 1 |
| 2025-11-20 | -0.0373 | 2026-02-06 | 0.031 | 52 |
| 2025-10-10 | -0.0354 | 2026-02-06 | 0.031 | 81 |
| 2024-09-03 | -0.033 | 2024-08-15 | 0.0264 | 12 |
| 2024-12-18 | -0.0324 | 2025-04-09 | 0.1362 | 75 |
The takeaway
Short answer: partly — some of QQQ’s biggest up-days do sit in the same turbulent episodes as the biggest down-days, but overall the ten biggest gains are not overwhelmingly clustered next to the ten biggest losses. The largest up-day was +13.62% on 2025-04-09 and the largest down-day was -6.18% on 2025-04-04; the median nearest-opposite distances are 9.5 trading days (up→down) and 8.5 days (down→up). Only 2 of 10 top up-days (20%) and 4 of 10 top down-days (40%) fell within three trading days of an opposite extreme, and the 20 extremes span 422 trading days — 56.2% of the 751‑day window. That means there are tight clusters (notably early April 2025, where several extremes are 1–3 days apart) but most extremes are separated by about one to two weeks. Bottom line on strength: this is a suggestive lean in favor of partial bundling, not a conclusive refutation of timing — the signal is mixed and modest, so treat it as suggestive rather than definitive. Practical takeaway: dodging bad days would sometimes cause you to miss rapid rebounds, but it’s not a universal rule — the big wins and losses are clustered in some episodes and scattered in others.
The fine print
- Distances are in trading days (weekend/holiday gaps ignored).
- Extremes are selected in-sample over this ~3‑year window; a different period can change which days qualify.
- Analysis uses daily close-to-close returns only — intraday swings aren’t captured.
- Findings rest on just 10 top ups and 10 top downs — a small sample, so evidence is thin.