Model Research
Compare candidate signals across horizons, universes, and market regimes with repeatable evaluation runs.
Quant research infrastructure
AI-driven financial research infrastructure
Research, validate, and monitor systematic signals through a controlled pipeline for model search, diagnostics, and portfolio construction.
/Cross-sectional signals
/Out-of-sample validation
/Portfolio diagnostics
/Production monitoring
What we build
Systems for disciplined research execution.
FrankVector is organized around repeatable research processes: controlled inputs, explicit validation, and auditable outputs.
Signal Generation
Convert market and fundamental inputs into ranked outputs, diagnostics, and portfolio-ready factor files.
Research Operations
Run batch workloads, track model state, and separate exploration from production monitoring.
Execution path
From raw observations to monitored portfolio inputs.
Each stage has a clear boundary: inputs, transforms, decision logic, validation evidence, and downstream monitoring.
01
Ingest
02
Features
03
Labels
04
Search
05
Validate
06
Allocate
07
Monitor
Operating principles
Built for systematic research, not presentation dashboards.
Rank-based modelling for cross-sectional signal selection
Out-of-sample validation before portfolio construction
Batch-oriented search for overnight and distributed workloads
Separate boundaries for research, generation, and monitoring
Discuss research infrastructure.
Scope the research pipeline, validation process, and operating model before adding dashboards, APIs, or client-facing tools.