A stronger strategy starts
with a deliberate universe.
Asset screenerResearch what an asset holds, how it is structured, and which history is available. Bring that context into every model you build.
Interactive example · sample data
Workspace / Diversified coreA considered universe.
Start with what an asset actually holds.
More screening filters
| Asset | Exposure | Structure |
|---|---|---|
| VTIVanguard Morningstar Total Stock Market ETF | U.S. equities | RIC |
| VXUSVanguard Total International Stock ETF | International equities | RIC |
| BNDVanguard Total Bond Market ETF | U.S. bonds | RIC |
| GLDSPDR Gold Shares | Physical gold | Grantor trust |
Explore locally. Nothing here creates a model, executes a run, or places a trade.
Open the asset screenerLook beyond the ticker.
Search and filter the supported catalog across the attributes that affect an allocation decision. A missing value remains a missing value: inspect coverage and evidence before treating a field as fact.
Backfills
Extend the history.
Keep the assumptions.
When an asset has limited observed history, define a backfill from a supported source. Specify the transformation, review the splice, and keep that synthetic period distinguishable from observed data.
The source may behave differently from the target asset. A longer window is useful only when you understand what the extension assumes.
A longer view, with a visible boundary.
Illustrative asset history · no returns shown
- Assumption
- Proxy source and transformation
- Review
- Splice date, source, limitations
Backfilled history is synthetic. It is not the asset’s observed performance.
From your universe to a model.
Use the symbols you have researched when defining a model’s universe. The model build validates the referenced assets and inputs; the screener provides context for your selection. Research and model construction remain explicit steps.
Explore allocation models →