/ 01 · SECTION
The record
June 23, 2026 set all-time highs across notional, share volume, and trade count in the three-venue overnight ATS dataset. BlueOcean alone printed its largest of 157 sessions in the proprietary pipeline.
BlueOcean (BOATS) alone printed $13.52B / 487M shares on June 23, ranking #1 of 157 sessions since September 2, 2025 on both metrics. The prior BlueOcean peak was $11.95B on June 8. This deep-dive isolates the ETF subset of the night and runs four independent analyses against it.
/ 02 · SECTION
The ETF subset
Of the 1,093 distinct symbols that printed on BlueOcean, 419 are ETFs. They moved $5.55B — 41% of the night's notional.
Single-name equities carried the remaining $7.97B, led by Micron (MU) at $2.69B and SanDisk, Marvell, Intel, NVIDIA behind it. The ETF complex was the second leg of the same trade: leveraged and thematic vehicles expressing the semis/memory thesis. The rest of this report is the ETF subset only.
/ 03 · SECTION
Issuer dominance
One fund family — Direxion — accounted for nearly half of all ETF notional. Five issuers carried 83% of the night.
Direxion's 3x semis bull/bear suite (SOXL and SOXS) was the night's expression vehicle. Roundhill's single-stock leveraged products (MUU on Micron, MULL on Marvell) carried the memory-thematic flow. Vanguard — the largest ETF issuer in the world — was a rounding error here at 0.8%. The overnight ETF tape is a different product universe than the one that dominates daytime AUM rankings.
/ 04 · SECTION
ETF type breakdown
Two-thirds of the ETF notional was in leveraged or inverse products. Broad-market ETFs were 13%. This was a tactical-trading night, not a passive-allocation night.
| ETF Type | Notional | Volume | ETFs | % of ETF tape |
|---|---|---|---|---|
| Leveraged 3x | $1,929M | 8.9M | 17 | 34.8% |
| Leveraged 2x | $1,247M | 20.5M | 164 | 22.5% |
| Sector / Thematic | $990M | 9.9M | 114 | 17.9% |
| Broad Market | $732M | 1.7M | 28 | 13.2% |
| Inverse 3x | $418M | 72.9M | 12 | 7.5% |
| Commodities | $112M | 1.4M | 15 | 2.0% |
| Inverse 2x | $61M | 8.2M | 33 | 1.1% |
| Inverse 1x | $36M | 2.6M | 19 | 0.7% |
| Fixed Income | $19M | 0.2M | 9 | 0.3% |
Highlighted rows: leveraged + inverse 3x = 64.8% of ETF notional, with 11.1% added by 2x leveraged.
One row is worth pausing on: Inverse 3x carried 72.9M shares against only $418M notional. That's SOXS (semis bear 3x) at $25-share prices being hammered with massive lot sizes as a hedge wrap on the long-semis trade. The share-count leaderboard and the dollar leaderboard tell different stories — which is why we rank by notional throughout the rest of this report.
/ 05 · SECTION
K-Means clusters
K-Means on standardized log-feature space (notional, volume, AUM, habitual activity, leverage, inverse flag, RVOL). Silhouette-selected k=6 from candidates [3..7]. Two clusters carry the night.
| Cluster | Profile | ETFs | Notional | Median AUM | Avg Lev | Inverse% | Exemplars |
|---|---|---|---|---|---|---|---|
| C3 | Moderate-leverage (1.9x avg) — the trade | 17 | $3.35B | $1.9B | 1.9× | 6% | SOXL, MUU, DRAM |
| C4 | Large cap-weighted broad | 41 | $1.21B | $39.2B | 1.0× | 0% | QQQ, EWY, SPY |
| C1 | Moderate-leverage (2.1x avg) | 75 | $0.79B | $0.4B | 2.1× | 1% | TQQQ, SQQQ, INTW |
| C0 | Inverse / hedging ETFs | 62 | $0.12B | $0.0B | 1.9× | 100% | MUD, ZSL, MSTZ |
| C2 | Mid-tier core | 126 | $0.04B | $2.9B | 1.0× | 0% | XBI, AIPO, GPIQ |
| C5 | Moderate-leverage (2.1x), small AUM | 98 | $0.04B | $0.0B | 2.1× | 0% | LABU, SMCL, LABX |
Silhouette score 0.33 — moderate cluster separation. C3 has only 17 ETFs but holds 60% of the ETF notional.
The structure is sharper than the silhouette implies. Cluster C3 — only 17 ETFs — captures the actual trade: mid-leverage (~1.9x average) names like SOXL, MUU, KORU, DRAM that printed $3.35B. Cluster C4 is the large broad-index hedge layer (QQQ, SPY, EWY) for another $1.21B. Everything else — 361 ETFs across the four other clusters — added up to $0.99B combined. Sixty percent of the ETF notional fits in a basket of seventeen ETFs.
/ 06 · SECTION
Random Forest — what predicts overnight notional?
Regression model: predict each ETF's log-notional on 6/23 from its static features. 300 trees, max_depth=8, min_samples_leaf=5. R² = 0.81 in-sample (n=419).
| Feature | Importance | Interpretation |
|---|---|---|
| log(habitual avg overnight notional) | 88.5% | An ETF's existing overnight franchise dominates |
| log(AUM) | 6.0% | Fund size adds a little signal |
| days traded overnight | 4.1% | Regularity of overnight presence |
| leverage factor | 1.1% | Surprisingly small once activity is controlled |
| inverse flag | 0.3% | Negligible |
Catalyst events don't recruit new ETFs to the overnight tape — they amplify the names already there. The model is unambiguous: 88.5% of the explainable variance in overnight notional comes from each ETF's typical level of overnight activity. Leverage and inverse exposure barely move the needle once you control for habit. The reason leveraged ETFs dominate the tape isn't because their leverage attracts overnight flow on catalyst nights. It's because they are the ETFs that live on the overnight tape every night.
Methodological note: R² is in-sample. The point of this analysis is feature attribution, not forecasting. An out-of-sample fit would be lower; ranking of feature importance would be stable.
/ 07 · SECTION
Hierarchical correlation tree
Ward linkage on (1 - Pearson) distance over the 60-session log-notional panel of the top-30 ETFs by 6/23 notional. Five groups emerge.
The trade. These 14 ETFs move on the same overnight catalyst structure: memory and broader semis with a Korea (Samsung/SK Hynix) tilt via EWY and KORU.
Macro hedge cluster — the broad-index and inverse-semi pair that risk-balances Group 4.
Memory-thesis vehicles that correlate with each other but not with broader semis.
Risk-balance cluster. TSLL grouping with metals is unintuitive but emerges from co-movement.
The correlation tree is one of the strongest results in this report. The 14 ETFs in Group 4 don't share an obvious surface attribute (different issuers, different leverage factors, different geographic exposures) but their overnight activity moves together. When semis catch a catalyst, that whole basket lights up as one trade — which is precisely what June 23 looked like.
/ 08 · SECTION
Overnight 3 vs Daytime 16 — head to head
Same 4-week window (2026-04-06 to 2026-05-03), ETFs only, our 3 overnight ATSes (BlueOcean + Bruce + Moon) side by side with the 16 institutional dark pools FINRA publishes (UBS-ATS, MS-Pool, JPM-X, Sigma X, BIDS, Barclays, and the rest). This is the cleanest contiguous stretch where both Tier 1 and Tier 2 are published, making it apples-to-apples across the full NMS ETF universe.
/ Weekly run-rate
| Week | BlueOcean | Bruce | Moon | 3-Venue Sum | FINRA 16 | Our Share |
|---|---|---|---|---|---|---|
| 2026-04-06 | $8.68B | $0.68B | $0.35B | $9.71B | $13.11B | 42.5% |
| 2026-04-13 | $6.85B | $0.61B | $0.26B | $7.73B | $14.04B | 35.5% |
| 2026-04-20 | $7.10B | $0.29B | $0.10B | $7.49B | $13.17B | 36.3% |
| 2026-04-27 | $7.65B | (partial) | (partial) | $7.65B+ | $13.09B | 36.9%+ |
| 4-week Total | $30.27B | $1.58B | $0.71B | $32.59B | $53.41B | 37.9% |
ETF notional only (1,137-symbol universe from etf_enriched). BlueOcean from the proprietary 3-venue pipeline parquet. Bruce + Moon from venue_overview top-100 per-day data. FINRA 16 from Rule 4552 weekly file, summed across all institutional dark pools. The 4/27 week shows partial Bruce/Moon coverage because venue_overview lost some late-April sessions during a pipeline gap, so the 37.9% aggregate is a floor.
/ Tier 1 vs Tier 2 breakdown
/ Ticker-level head-to-head
The aggregate 37.9% figure averages across the full 1,137-ETF universe and is pulled down by hundreds of thinly-traded ETFs that show up on FINRA dark pools but not on overnight venues. Looking only at the ETFs that are actively traded overnight, the structural picture sharpens: for the top 25 ETFs by 4-week overnight notional, the overnight venues hold the majority of ATS flow in 16 of 25 names. Five names exceed 80% overnight share. The mega-cap broad-index complex (QQQ, VOO, SPY, IWM) is no exception.
| ETF | Tier | Overnight 4-wk | FINRA-16 4-wk | O/N Share | Type / Family |
|---|---|---|---|---|---|
| SOXL | T2 | $5,677M | $3,141M | 64% | Leveraged 3x · Direxion |
| QQQ | T1 | $3,385M | $3,132M | 52% | Broad · Invesco |
| TQQQ | T2 | $2,153M | $1,602M | 57% | Leveraged 3x · ProShares |
| SPY | T1 | $1,885M | $7,086M | 21% | Broad · State Street |
| SQQQ | T2 | $1,680M | $1,345M | 56% | Inverse 3x · ProShares |
| SOXS | T2 | $1,374M | $1,109M | 55% | Inverse 3x · Direxion |
| USO | T1 | $1,115M | $39M | 97% | Commodity · USCF |
| SLV | T1 | $1,114M | $122M | 90% | Commodity · iShares |
| GLD | T1 | $1,044M | $216M | 83% | Commodity · SPDR |
| SNXX | T2 | $605M | $618M | 49% | Leveraged 2x · Tradr |
| EWY | T1 | $571M | $476M | 55% | Korea · iShares |
| TSLL | T2 | $551M | $525M | 51% | Leveraged 2x · Direxion |
| AGQ | T2 | $485M | $352M | 58% | Leveraged 2x · ProShares |
| IBIT | T1 | $458M | $93M | 83% | Bitcoin · iShares |
| SGOV | T1 | $423M | $109M | 80% | Treasury · iShares |
| VOO | T1 | $412M | $167M | 71% | Broad · Vanguard |
| IWM | T1 | $408M | $1,699M | 19% | Broad · iShares |
| MUU | T2 | $405M | $450M | 47% | Leveraged 2x · Direxion |
| SMH | T1 | $318M | $1,061M | 23% | Sector · VanEck |
| SOXX | T1 | $316M | $540M | 37% | Sector · iShares |
| QQQM | T1 | $240M | $37M | 87% | Broad · Invesco |
| NVDL | T2 | $226M | $405M | 36% | Leveraged 2x · Graniteshares |
| KORU | T2 | $202M | $186M | 52% | Leveraged 3x · Direxion |
| QLD | T2 | $200M | $211M | 49% | Leveraged 2x · ProShares |
| DRAM | T2 | $193M | $695M | 22% | Memory · Roundhill |
Top 25 ETFs ranked by overnight 4-week notional (4/6 - 5/3). Overnight = BlueOcean (parquet) + Bruce + Moon (venue_overview top-100, partial coverage; so figures here are a floor). FINRA-16 = T1 + T2 across all 16 institutional dark pools over the same 4 weeks. Tier from FINRA classification. Highlighted rows: ETFs with 80%+ overnight share.
Three structural findings emerge from running the comparison at the venue level rather than the per-symbol level:
(a) Nearly four in ten institutional ATS ETF dollars now flow through three overnight venues.
Over the 4-week April window where both FINRA tiers are published, our 3 overnight ATSes processed $32.6B in ETF notional while the 16 daytime institutional dark pools FINRA publishes processed $53.4B. Combined that is $86.0B in tracked institutional ATS ETF flow; the overnight share is 37.9%. Three venues operating 8 hours a night moved nearly four dollars for every ten the entire daytime institutional dark-pool complex moved. And the 37.9% is a floor: Bruce and Moon coverage is partial in our weekly aggregation, so the true overnight share is higher.
(b) In the Tier 2 ETF category, the overnight venues are now at parity with the institutional dark-pool complex.
Tier 2 is where leveraged ETFs (SOXL, TQQQ), inverse ETFs (SOXS, SQQQ), single-stock leveraged ETFs (MUU, MULL, MVLL, NVDL, TSLL), and thematic/niche products live. Over the 4-week window the 3 overnight venues processed $18.6B in T2 ETF ATS flow against the FINRA 16's $20.3B. The overnight share of Tier 2 ETF ATS flow is 47.8%. This is not a niche venue with growing share. It is a venue at structural parity with the entire daytime institutional dark-pool complex for an entire product category.
(c) Tier 1 (mega-cap broad indexes) lags but still registers at 30%.
For Tier 1 ETFs (SPY, QQQ, QQQM, VOO, IWM, SMH, SOXX, GLD, SLV, EWY), the 16 daytime dark pools still lead by more than 2-to-1: $33.2B daytime vs $14.0B overnight. But the overnight share is 29.7% - nearly one in three. The structural pattern matches the per-symbol findings in Section 06 (Random Forest): the overnight venues are not a tactical sliver hosted alongside daytime activity. They are the dominant venue for one entire product category (Tier 2) and a meaningful peer in the other (Tier 1). The implication for ETF flow desks is that overnight ATS routing and benchmarking are no longer separable problems from daytime workflow.
/ 09 · SECTION
Thesis
What this analysis means for institutional desks, beyond the headline record.
- 01
The overnight ETF tape is a tactical venue, not an allocation venue.
Two-thirds of notional was in 2x and 3x products. Issuer HHI of 2,619 is at the DOJ “highly concentrated” threshold, and the concentration is not in the Vanguards and BlackRocks. It is in Direxion, ProShares, and Roundhill — the leveraged and single-stock-thematic issuers.
- 02
Catalyst nights amplify existing names. They do not recruit new ones.
The Random Forest is unambiguous: 88.5% of the predictive signal for overnight notional is the ETF's own habitual overnight activity. Building a franchise on the overnight tape on normal nights is what captures the catalyst nights. Issuers absent from the overnight tape during quiet periods will not appear when the trade ignites.
- 03
Levered and single-stock ETFs are a separate liquidity universe.
SOXL, MUU, TQQQ, DRAM, KORU — they trade lit and on overnight ATSes. They do not print in UBS, Morgan Stanley, JPMorgan, or any of the 14 other institutional dark pools FINRA covers. This is a structural distinction by product type, not just by time of day. Workflow desks routing levered ETF flow cannot rely on conventional dark-pool sourcing — the inventory is not there.
- 04
The overnight ATSes are no longer a niche. In Tier 2 ETFs they are at venue parity with the daytime dark-pool complex.
Same 4-week window, head to head: 3 overnight venues processed $32.6B in ETF notional vs the 16 institutional dark pools FINRA publishes at $53.4B. 38% of all tracked institutional ATS ETF flow now goes overnight, and that is a floor. In the Tier 2 category specifically (leveraged, inverse, single-stock, niche), our 3 overnight venues moved $18.6B against the FINRA 16's $20.3B - 47.8% overnight share. The overnight session has crossed the threshold where it can be modeled separately from daytime; for ETF flow desks, routing and benchmarking are no longer separable problems.
/ 10 · SECTION
Subscriber data download
Every analysis on this page in a 10-sheet Excel workbook. Bring the data into your own models.
ETF Overnight Record · June 23, 2026 — Full Dataset
Cover, Topline stats, all 419 ETFs (with cluster assignment, RVOL, leverage, AUM, family), Issuer breakdown with HHI, ETF-type rollup, K-Means cluster profiles with exemplars, Random Forest feature importance, Ward correlation groups, FINRA cross-reference table, and a Glossary. Same source data we used to write this report.
Download requires an active Sapinover session (any tier including free trial). Anonymous requests are redirected to the subscribe page.
/ 11 · SECTION
Methodology
Sources, model specs, and the caveats baked into each result.
Data sources
- BlueOcean Master Historical parquet — proprietary 3-venue pipeline (BlueOcean, Bruce, Moon), GitHub Release
pipeline-data - ETF enrichment file (1,137 ETFs with family, category, AUM, leverage, expense ratio) — yfinance + bespoke leverage parser
- FINRA Rule 4552 ATS Weekly file — 16 institutional dark pools across a 28-day window 2026-04-06 to 2026-05-03 (4 contiguous weeks, the most recent stretch with both Tier 1 and Tier 2 published; FINRA's 5/4 and 5/11 weeks are T1-only and excluded for cleanliness)
Models
- K-Means clustering — silhouette-optimized k across [3..7] on standardized log-feature space (log-notional, log-volume, log-AUM, log-habitual, leverage factor, inverse flag, RVOL)
- Random Forest regression — 300 trees, max_depth=8, min_samples_leaf=5, target = log10(overnight notional). Feature attribution only; in-sample R² 0.81
- Hierarchical correlation tree — Ward linkage on (1 - Pearson) distance matrix over 60-session log-notional panel of the top-30 ETFs by 6/23 notional. Cut at k=5
- Head-to-head venue comparison — same 4-week window (4/6-5/3), ETF flow only, our 3 overnight ATSes vs FINRA 16 daytime ATSes. BlueOcean ETF flow taken directly from the pipeline parquet (full per-symbol per-day coverage); Bruce + Moon ETF flow extracted from venue_overview top-100 per-day data (partial coverage); FINRA ETF flow filtered to the 1,137-symbol ETF universe and summed across all 16 institutional dark pools, T1 + T2 combined. Tier breakdown uses FINRA's NMS Tier_ID classification per symbol.
Caveats
- ETF subset is BlueOcean only; Bruce + Moon ETF-level detail is not in this analysis
- Random Forest R² is in-sample. Out-of-sample fit would be lower. The analysis is for feature attribution, not forecasting
- K-Means silhouette of 0.33 is moderate — cluster boundaries are real but soft
- FINRA window is 4 weeks 4/6 - 5/3 (most recent T1+T2-complete stretch). May weeks 5/4 and 5/11 are T1-only in FINRA's publication and excluded from the head-to-head for cleanliness
- BlueOcean ETF flow is authoritative (full pipeline parquet, every symbol every session). Bruce + Moon ETF flow is from venue_overview top-100 per-day data with partial date coverage (~15 of 20 April sessions), so the 37.9% overall and 47.8% T2 overnight share are FLOORS - the true share is higher
- FINRA does not publish OCEA, Bruce, or Moon, so we use our proprietary pipeline data for the overnight side. The comparison is genuinely apples-to-apples on the ETF universe (1,137 tickers from etf_enriched) and the 4-week window
- All notional figures are dollar volume, not share-count
SAPINOVER LLC · 2026-06-24 · NOT INVESTMENT ADVICE · INFORMATIONAL ONLY
This report is for informational purposes only. Past trading patterns do not predict future activity. All numbers are derived from public market structure data and the proprietary Sapinover 3-venue overnight ATS pipeline. The report contains no investment recommendations, ratings, or implied views on securities. See disclaimer.