Quant DataLake

A governed data foundation for investment systems.

Quant DataLake brings source authority, point-in-time history, stable data contracts and controlled access into one foundation for research, portfolio, risk and AI workflows.

It is infrastructure beneath the operating stack - not another terminal. OneBook provides the decision and workflow experience built on top of it.

Discuss your data architecture See the architecture

Licensed sources
Internal systems
Private files

QuantJourney DataLake authority · history · contracts · controls

OneBook Data Gateway Backtester Private environments

The operating problem

Investment teams do not need another disconnected data destination.

They need a layer that keeps source choice, historical state, entitlement and distribution rules intact as data moves into research, portfolio decisions, risk review and automation.

Quant DataLake is designed as that shared layer: it makes the underlying data usable across the stack without making every team rebuild its own integrations and controls.

The foundation

Four properties that should travel with every dataset.

Source authority

Keep provider, licence, credential and source-selection decisions explicit instead of scattering them through notebooks and applications.

Point-in-time data

Make dates, revisions, identifiers and historical state available to research and review workflows in a consistent form.

Contracts and lineage

Give downstream systems stable data contracts and a path back to the source, transformation and context behind an output.

Controlled distribution

Serve the same governed data through approved product surfaces, programmatic routes and private deployment boundaries.

Where it fits

One shared data layer. Different controlled ways to use it.

Inputs

Market and reference sources Filings and fundamentals Portfolio and operational systems Private firm data

Quant DataLake

Controlled data foundation source authority · point-in-time history · contracts · audit context

Product surfaces

OneBook decision workflows API, SDK and approved MCP Backtester research runs Private deployment integrations

The same data foundation can support a human workflow, an internal application and a controlled AI tool without treating each as a separate data estate.

What it supports

Data infrastructure for the work around an investment decision.

Research and analytics

Market, fundamental and reference data for repeatable research rather than one-off extracts.

Investment operations

Instrument, portfolio and operational context that can be reconciled with the systems of record already in use.

Risk and reporting

Defined inputs, snapshots and data contracts behind reports, scenario work and oversight.

AI and automation

Permissioned, auditable access for internal tools and approved agent workflows.

Built for the real estate you already have

Add a controlled data foundation without forcing a wholesale platform replacement.

Quant DataLake is intended to sit alongside existing IBOR, PMS, OMS, risk, reporting and research systems. The aim is to create a clearer data boundary and a usable shared layer - not to make a fund discard the systems that already hold operational authority.

Discuss a private deployment