Design an ETF from revenue-weighted exposure, and see its market before you file.
Type the rules and the fund builds as you type. Every fund already listed on the theme, who already holds the names, how advisors have adopted its ETFs, and how it holds up on diversification and capacity checks.
One thesis, from theme to file
One real thesis, Data Center & Next-Gen Infrastructure at 50% of revenue, through all six steps. Every figure is live, as of 2026-11-05.
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01ExploreCrowded
40 funds already compete on the theme, 26 of them launched in the last two years
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02Build your thesis71 companies
earn 50%+ of their revenue in your thesis, and 180 earn 25%+
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03Compare1st of 23
stacked against the 22 closest ETFs on the theme, by revenue in it
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04Demand46%
of RIAs already own at least one of its companies
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05Check$1.1B
capacity before liquidity binds; each check names the fix
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06Hand over4 pages
the assessment, the rules written out and the file, ready for your index provider
- White-space map
- Theme pairings
- Launch tracker
- Flows by theme
- Revenue clauses
- Exclusions
- Liquidity and float screens
- Seven weighting schemes
- Name caps
- Strata Score rules
- Pairs well
- Competitor purity and overlap
- Names no fund holds
- N-CEN fund facts
- Theses side by side
- 13F holders and flows
- Advisor book overlap
- Advisor adoption
- RIC and UCITS checks
- Capacity
- Risk vs benchmarks
- Custom themes
- Four-page report
- Constituent file
Launched faster, closed sooner
Thematic funds are launched faster than any other category and survive less often. A launch costs six figures before the fund lists and a few hundred thousand a year to run, so a fund charging 50 bps needs tens of millions in assets to cover its costs.
The decision is still made the old way: an analyst screens by sector code or keyword, hand-builds a list, and an index provider turns it into an index.
Six ways a launch fails before listing
Each one has a reading or a screen in Strata Thesis that shows it first.
The theme is not a fund
Too few companies earn real revenue from it, so the fund fills with names at 5 to 10% exposure.
An incumbent is already pure
A listed fund with $100M or more already delivers most of its holdings’ revenue in the theme.
The shelf is full
Five or more funds with $100M or more already compete on the theme, and at least one already delivers it.
The names are the names everyone owns
The basket overlaps an incumbent, QQQ or the average advisor book so heavily that advisors already hold it.
Advisors are not adopting it
Advisors are trimming the constituents, the theme’s ETFs are losing holders, or adopters leave within a year.
It cannot scale, or breaks a limit
Liquidity caps the fund below the seed plan, or the weights break the RIC or UCITS diversification limits.
Advisor adoption of thematic ETFs, 2023 to 2026
The 13F books of a matched panel of 4,137 RIAs above $100M, followed quarter to quarter, with every ETF read through to its holdings. Inside the platform: demand by theme, what adopters had in common, and the adoption curve of each of 360 thematic funds.
Observed, not causal: what advisors held, never a forecast.
What the usual tools leave out
Each one answers part of the launch decision. None of them answers it.
| What you use today | What it gives you | What it cannot tell you |
|---|---|---|
| Terminal industry-code screens | Companies by industry code or keyword; revenue by subindustry, assembled into a theme by hand | Whether the theme is a fund, or what the funds already listed on it really hold |
| Thematic relevance scoresfrom data vendors | A 0 to 100 score per company on the vendor’s own list of themes | A revenue share, your theme, or who owns the names |
| ETF analytics dashboards | Flows, market share and 13F holders for funds that exist, built for sales teams | Anything about a fund that does not exist yet |
| White-label platform review | Whether the fund can be operated: liquidity, replicability, licensing | Whether anyone wants it |
| Strata Thesis | Revenue by theme for every company with the reasons, every listed fund read the same way, who owns the names, how advisors adopted the theme’s funds, and the basket as rules, in one place | What it will return. Nothing here is simulated |
Strata Thesis sits before your index provider: scope the theme here, then hand the rules and the constituent file to the index provider you work with.
One classification, read three ways
The unit is a reported revenue segment of a company, from its filing, split across one to four themes with a reason written down. Boundaries are enforced in code, every company is audited by a second independent review, and each release is numbered, dated and frozen.
The Builder
Which companies qualify at any revenue threshold, every segment and why it was classified that way, and the basket the rules produce.
Competition
3,200 funds read through their latest holdings: purity on any theme, overlap with the basket, and the names no fund holds.
Demand
Advisors and hedge funds above $100M, matched quarter to quarter: who holds the names, whether they added or trimmed, and which thematic ETFs they adopted.
The five steps in full, with the boundary rules and confidence bands →
Custom themes, for research
A theme the taxonomy does not have yet gets a first basket in about a minute, with the definition in front of you, so you can size it and see what is already listed. A theme for an actual index build is a separate, full classification through StrataNova’s model, on request.
Name a specific theme. The model checks it is specific enough to judge a revenue segment against, says when a taxonomy theme already covers it, and drafts a definition in the house style: what counts, what does not, and where adjacent businesses belong. You edit it before anything is scored.
Up to 180 investable candidates. For each, the model reads the revenue segments as StrataNova classified them and returns a fraction per segment; the arithmetic is done in code. Companies the model expects but the segments do not show come back flagged for you to decide.
Estimates are shown as bands, never precise shares: Core 50%+, Significant 25 to 50%, Moderate 10 to 25%. The Estimate tag follows the theme into the Builder, the report and the file.
Observed, never simulated
Nothing simulated
No hypothetical index performance, no rating and no recommendation to launch. The product shows what exists today and labels anything that looks backward.
Each firm’s work is its own
Theses, custom themes and requests are held in your organization, encrypted at rest with keys derived per organization, and never visible to another firm.
Refreshed nightly, dated everywhere
Prices, holdings and fund data refresh every night; 13F ownership and advisor adoption are quarterly as of the latest filing period. Every page prints the release and the dates it rests on.
Questions issuers ask
Is this AI tagging companies with themes?
The classification reads revenue segments from filings and records a reason for every line; the model is one input to that. The product is the classification applied to companies, to 3,200 funds’ holdings and to 13F books at once, with a rules engine, diversification and capacity checks, and advisor demand on top. The only in-app AI estimate is the custom theme, and it is labeled and shown in bands.
We already have a terminal and a revenue dataset.
Industry-code revenue data maps revenue to subindustries; a theme is assembled by hand, and no fund white space or advisor demand comes with it. Terminal screens match codes or text. Neither says whether a fund that does not exist yet would be pure, crowded or owned.
Where does this fit with our index provider?
Before them. Use Strata Thesis to scope a theme, see what is already listed and who already holds the names, and settle the rules. The rules and the constituent file then go to your index provider, who builds, calculates and maintains the index. Strata Thesis does not calculate or license indexes.
How is this different from an ETF analytics dashboard?
Those track funds that exist, for sales and distribution. Strata Thesis classifies the companies, so it can describe a fund that does not exist yet and compare it with everything that does.
Where do the advisor adoption figures come from?
13F filings of a matched panel of 4,137 RIAs above $100M from mid-2023, followed quarter to quarter, with each ETF read through to its holdings near each quarter end where N-PORT filings exist. They describe what advisors held and when they opened or closed a position. They do not say why, and they are not a forecast.
Does it cover non-US names?
Yes. Segments come from SEC, EDINET, TWSE, DART and ESEF filings and from Chinese company reports. A thesis can be built on any listing, US listings only, or US plus OTC, and liquidity is measured on the line that would actually trade.
What about a theme you do not have?
Describe it, review the definition the model drafts, and get a banded estimate in about a minute, for research. A theme for an actual index build is a separate classification through StrataNova’s model, with the definition agreed in writing.
Who sees our theses?
Your organization only. Work is encrypted at rest with keys derived per organization. A request for a custom theme build is the only thing that leaves your organization, and you send it.
Thirty minutes on live data
A theme with room, the fund built as rules, the funds already listed on it, and who holds the names. The same demo every time, so you see the product rather than a pitch built around you.
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