vecviz(charts, narrative)
= valuation, risk, timing

1. Scan every covered ticker in one table: vector metrics for the full universe on a single page.
2. Build your own screen: filter and sort the VV Data Table by V-Score, VNA price return, EUB and EDB returns, 99-day return, and equity liquidity. Percentile returns use the 21-day (~1 month) forward horizon.
3. Narrow to one sector or industry and compare its names side by side, no exporting.
4. Read the probability bands around any price: see how likely a level is for a ticker at each of six horizons, from 1 day to 1 year forward.
5. See the vector structure behind a target price: what the VNA target is actually built from, event by event.
6. Compare a name’s downside VaR to its upside OaR, then check whether today’s bound is normal for that name against its own history.
7. Find diversifiers: spot names whose narrative and long-term chart shape genuinely differ from your core holding, not just names with a low trailing correlation.
8. Open the model portfolios and compare MVO weights across the strategy variants and volatility caps: see what the optimizers actually hold.
9. Break a V-Score into the criteria that produced it on the spider chart.
10. Try all of it with no key at all: the public demo covers ten tickers across every page.

Open the demo dashboards →

Click any prompt to copy it, fill in the blanks, and paste it into an agent connected to the VecViz MCP server. You describe the question; the agent picks the tools.

Get a free key for your agent →