VertiMax is the research infrastructure quantitative teams use to source and clean market data, test strategies without fooling themselves, and manage risk with the rigor their investors expect — not a pile of one-off scripts.
Systematic investing fails most often not from a bad idea, but from bad plumbing — a dataset that shifted under you, a backtest that flattered you, a risk number nobody can reproduce. We build the plumbing.
One clean, versioned view of every market you trade. Point-in-time correction, corporate actions handled, timestamps you can actually trust — so the number you researched is the number you traded.
point-in-timeA backtester that refuses to flatter you. Survivorship-aware and cost-aware, it is built to answer the only question that matters: would this have worked, after you could have traded it?
cost-awareEx-ante and ex-post risk in the same pane. Factor exposures, stress scenarios, and attribution you can defend in front of an investment committee — not a number you hand-waved past.
attributionEach market has its own idiosyncrasies — settlement, corporate actions, microsecond timestamps. We normalize them into a single research-ready format so your pipeline doesn't branch per asset class.
| Asset class | Data | Point-in-time | History | Notes |
|---|---|---|---|---|
| Equities | OHLCV · fundamentals · refs | native | deep | corporate actions & survivorship handled at source |
| Futures | continuous & per-contract | native | deep | roll calendars you can audit, not a black box |
| FX | spot · forwards | native | deep | fixing-aligned timestamps for executable research |
| Options | chains · greeks · IV | native | broad | surface construction included |
| Crypto | spot · perp funding | native | broad | exchange-level resolution, funding carried through |
| Fixed income | curves · credit | native | broad | curve & carry analytics |
| Alternative | your own signals | ingest | — | bring-your-own-data with the same governance |
Research is never a single run — it's an argument with your own history. The platform keeps every experiment reproducible, every dataset versioned, every number traceable back to its inputs.
Signals decay; the question is the slope. What we measure when we measure factor decay, and how it should set your rebalance frequency.
Read → 2026-07-02It's a research bias. Why point-in-time data is the difference between a strategy and a survivorship artifact.
Read → 2026-05-14Multiple testing, overfitting, and the quiet pressure to show a line that goes up. How we keep our own honest.
Read →We spent years patching together vendor data, an in-house backtester, and a risk model that never agreed. VertiMax is the first stack where the same dataset, the same cost model, and the same attribution feed everything.
The point-in-time data alone changed how we build. We stopped discovering, six months into a strategy, that half our history was backfilled. The research finally matches what we could have actually traded.
Priya Raman spent a decade running research infrastructure inside systematic asset managers, watching the same tragedy repeat: a team's best ideas built on data that had quietly shifted, backtests that flattered, and risk numbers nobody could reproduce a month later.
She founded VertiMax in 2019 on a simple conviction — that the tooling for rigorous quantitative research should be as disciplined as the strategies it produces. No black boxes. No survivorship. Every number traceable to its inputs.
We'd rather show you the platform running on your own data than tell you about it. Tell us what you trade, and we'll show you the research record you could have.