An actively managed ETF: aims for twice the daily return of the Magnificent Seven stocks, using swaps and futures. Because it resets daily, longer-term returns can differ a lot from twice.
MAGX, the Roundhill Daily 2X Long Magnificent Seven ETF, is a $66 million ETF whose main story is the AI compute core. Over the past year it returned +13.9%, against +16.8% for broad US ETFs. Figures to October 2, 2026; past performance, not a recommendation.
The narrative it carries
This is its only narrative.
The AI compute core →
The chips, memory and data centers AI runs on, and the big technology companies that build and buy them.
The band is where this narrative’s level sits among the 22 this week. QUIET means less talked about, not falling.
| # | ETF | SHARE | ASSETS | 1 YEAR | COST |
|---|---|---|---|---|---|
| 1 | VGT | 23.8% | $147.56bn | +36.0% | 0.09% |
| 2 | XLK | 20.5% | $127.30bn | +40.4% | 0.08% |
| 3 | SMH | 12.1% | $74.88bn | +87.0% | 0.35% |
| 4 | SOXX | 7.8% | $48.43bn | +109.7% | 0.33% |
| 5 | IYW | 4.3% | $26.53bn | +36.0% | 0.37% |
| 391 other ETFs | 31.5% |
Share is each ETF’s slice of the capital carrying this narrative. 1 year is market price total return; cost is the expense ratio. See all 396 ETFs on the narrative page →
FRI OCT 2MARKET PRICE TOTAL RETURN
| 1M | 3M | YTD | 1Y | 3Y* | 5Y* | |
|---|---|---|---|---|---|---|
| MAGX | +10.7% | +20.8% | +10.3% | +13.9% | – | – |
| The AI compute coreits narrative | +9.5% | +8.7% | +47.8% | +50.2% | +41.6% | +24.5% |
| The marketbroad US ETFs | +0.8% | +2.6% | +14.6% | +16.8% | +22.9% | +12.9% |
1M, 3M, 1Y, 3Y, 5Y: one month to five years. YTD: year to date. * A year, annualised. A dash means the ETF has not traded for the whole period. Since it started trading on February 29, 2024: +42.9% a year.
$10,000 invested February 29, 2024
Year by year
Total return in each calendar year; this year is year to date. A year appears only if MAGX traded for all of it.
Total return on the market price, with each distribution reinvested on its ex-date. Not the NAV return the issuer publishes; the two differ when the ETF trades above or below the value of its holdings. The narrative is the asset-weighted return of the unlevered ETFs that carry it. The market is the asset-weighted return of broad US index ETFs. Past performance is no guarantee of future results.
FRI OCT 2MARKET PRICE TOTAL RETURN
How closely it moves
MAGX moves closely with the market. When the market has moved 1%, MAGX has tended to move 2.81%.
MAGX moves closely with the AI compute core. When the AI compute core has moved 1%, MAGX has tended to move 1.62%.
Weekly returns over 135 weeks. The narrative is measured without MAGX in it, so it is not compared with itself.
ETFs it moves most and least like
Correlation of weekly returns over the last 3 years, among unlevered ETFs StoryVector covers.
Where it sits in The AI compute core
Each dot is one of 77 unlevered ETFs whose main narrative is the AI compute core, placed by its return (up) and how much it swung (right) over the last year. MAGX is levered or inverse, so it is shown against them but not counted among them.
Risk
Cost against its peers
MAGX is levered or inverse, so it is shown against the unlevered ETFs but not ranked among them.
How it is built
Seeks 2x the daily return of its exposure, and gets its exposure through swaps. It carries the narrative but does not count toward Share of Narrative, because only unlevered long exposure does.
Narrative exposure
Common questions
What story is MAGX selling?
MAGX’s main story is the AI compute core, which covers the chips, memory and data centers AI runs on, and the big technology companies that build and buy them.
Which ETFs compete with MAGX?
The ETFs whose weekly returns move most like MAGX’s over the past 3 years are MAGS (1.00), YMAG (0.97) and QBIG (0.97).
How has MAGX performed against its story?
Over the past year MAGX returned +13.9%, the ETFs carrying the AI compute core +50.2% and broad US ETFs +16.8%, to October 2, 2026. Its weekly returns have a correlation of 0.82 with the story. Past performance only.
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