Screen 19,000+ names for your thesis in seconds
Find the names that fit your idea — deleveraging, margin-inflecting, buyback-heavy — before the trade is crowded.
The problem
Idea generation dies in spreadsheets. By the time you've pulled fundamentals for a few hundred names, you've biased your search to what you already know — and missed the thesis hiding in the long tail.
How Valuein does it
- 1
Express the thesis as a screen
screen_universe filters the full universe on standardized fundamentals and pre-computed factor ranks — combine growth, margin, leverage, and value conditions in one call.
- 2
Rank, don't just filter
get_financial_ratios exposes the cross-sectional factor scores so you sort by composite rank, not arbitrary cutoffs.
- 3
Hand the shortlist to deeper work
The screen_and_shortlist SOP hands each survivor to the equity_research_brief — so a universe screen becomes a stack of IC-ready briefs without manual hand-off.
In practice
You ask
Screen for profitable companies under 2x net debt/EBITDA, gross margin expanding YoY, trading below peer median EV/EBIT.
Valuein returns
47 names match; ranked by composite quality+value score. Top 10 returned with the factor breakdown and a link to spread each.
The outcome
You surface the whole opportunity set — including names you'd never have thought to pull — and go from idea to shortlist in one step.
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Frequently asked
How big is the universe?
19,000+ active and delisted US entities on Pro and Institutional (the free tier covers the S&P 500). Screens run across the whole set, not a sample.
Can I screen on point-in-time data?
Yes — combine screening with the PIT universe so a historical screen reflects only what was knowable on that date, for honest backtests of the screen itself.
Related use cases
Do this with your own data — free.
The free S&P 500 tier needs no credit card. One token works across MCP, the Python SDK, and the Workspace.