Intelligent Alpha Atlas ETF

GPT is designed to be highly dynamic, relying on both AI and human expertise to capitalize on evolving market opportunities. The strategy integrates human insight with AI-powered stock selection to construct a portfolio of global large-cap stocks. Portfolio construction begins with an analyst setting the portfolios parameters, including structure, concentration limits, and thematic inspiration from renowned trading strategies. AI then analyzes datasets to identify four to six major trading trends to choose positions that align with these trends based on key financial metrics such as revenue growth and earnings. Three independent AI models select 20 to 30 stocks to form an equal-weighted portfolio, with no single position exceeding 10%. The selection process combines both quantitative and qualitative analysis. Portfolio rebalancing is done quarterly to ensure compliance. The fund is expected to have a high portfolio turnover rate. Prior to Sept. 29, 2025, the fund name was Intelligent Livermore ETF and traded under the ticker LIVR.

Intelligent Alpha Atlas ETF trades as GPT on NASDAQ. The company is classified in Financial Services / Asset Management and reports in USD.

The current profile places the business in Asset Management. This section is intended to summarize the operating segments, products, geographies, and main revenue lines from official filings.

Detailed operating-segment data is not available for this symbol yet.

Use this area for management strategy, capital allocation priorities, target markets, and measurable goals from the latest annual report or investor presentation.

The app now provides the structure, but exact strategic claims should come from official company documents before being treated as a finished investment thesis.

Intelligent Alpha Atlas ETF can be compared against peers such as Alpha Architect Global Factor Equity ETF, Pacer US Small Cap Cash Cows Growth Leaders ETF, iShares MSCI Belgium ETF, iShares LifePath Target Date 2060 ETF, Harbor Long-Short Equity ETF (LSEQ), TrueShares Structured Outcome (November) ETF.

A complete thesis should compare growth, margins, balance-sheet risk, valuation multiples, and market position against direct competitors.

Current signals to investigate include market capitalization of $22.76M, beta of 0.82, and return on equity of N/A.

This section should be validated with evidence such as durable margins, brand strength, regulation, switching costs, cost advantage, distribution, or technology.

Key risks should include financial leverage, cyclicality, customer concentration, regulatory exposure, currency risk, and execution risk.

GPT currently shows total debt of N/A and beta of 0.82. Missing data should be treated as a research gap, not as low risk.

Production-capacity detail is not available as structured data yet. For industrial, defense, semiconductor, or real-estate companies, this should be reviewed from annual reports and investor presentations.

No structured backlog field is available yet. If the company reports backlog, review the relevant filing section before adding it to the thesis.

Use this section for major contracts, product launches, construction projects, acquisitions, or strategic programs that can materially affect valuation.

No recent SEC-style filings are available for this symbol yet.

Customer concentration is not available as structured data here. Add it from official filings when a company discloses material customers or revenue concentration.

Supplier concentration and critical supply-chain dependencies are not available as structured data here. This should be researched from annual reports and risk disclosures.

Company website: https://iaetfs.com/etf/

For US-listed stocks, verify the thesis against official filings, earnings call transcripts, and company investor relations materials.