EigenPoint is a systematic trading engine built on proprietary mathematics that generates its algorithm for market prediction — licensed as core technology to funds and platforms that need it to run under their own name.
EigenPoint doesn't take positions on its own behalf. It's licensed as infrastructure — the licensee's regulatory entity holds the deployment, the capital, and the client relationship.
Licensees don't inherit a research project — they integrate a production system. The delivery interface, data requirements, and onboarding cadence are documented upfront, so bringing EigenPoint into an existing book is a technical integration, not an open-ended collaboration.
Prospective licensees can evaluate live and historical performance before committing to a deployment. Specifics of the underlying models are shared only after an NDA and licensing agreement are in place.
The research behind EigenPoint is treated as its core asset — protected, not publicized.
Developed in-house over multiple market cycles, the underlying models aren't published, licensed for reuse, or explained in sales conversations. Prospective licensees see how the algorithm performs — not how it's built.
Integration happens at the output layer — a defined feed of prediction and sizing signals. Track record and live performance are what get evaluated; the research itself stays out of the room.
Extend an existing book with a strategy engine built on proprietary mathematics, delivered as a signal feed you can run against your own risk parameters.
Run a systematic sleeve alongside discretionary allocations, with sizing logic that adjusts to volatility automatically rather than requiring active oversight.
Embed the engine behind your own product surface via API, with your entity as the regulated party of record for anything client-facing.
EigenPoint is delivered as core technology, not as an advisory or brokerage relationship. Deployment, client relationships, and regulatory standing remain with the licensee at every step.
EigenPoint licenses the engine's predictive output to the licensee's entity. The underlying mathematics stays proprietary; the licensee determines how that output is deployed, under what mandate, and to which end clients.
Because the licensee's entity holds the regulatory relationship for any given deployment, licensing terms and integration details are confirmed directly and vary by jurisdiction and use case.
Share a few details about your entity and intended deployment, and we'll follow up to walk through licensing terms and integration.