Tokenized Private Market Fund Asset Valuation Engine
MAS initiatives to strengthen Singapore's asset management hub place heavy emphasis on private asset tokenization and automated NAV calculation. Build an autonomous valuation agent that continuously ingests illiquid asset performance metrics and updates fund NAVs. Achieve reliable valuation outputs with verifiable mathematical consistency.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated private market tokenized asset valuation and NAV pipeline for Singapore fund managers.
How work is evaluated
Evaluates NAV calculation precision and adherence to VCC reporting schema.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
nav_math_check
Verifies that token price exactly matches formula output.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
valuation_precision
Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement automated Net Asset Value (NAV) pricing functions for illiquid tokenized assets
Ingest private equity quarterly cash flow files and apply discounted cash flow (DCF) models
Validate schema outputs against Singapore Variable Capital Company (VCC) reporting standards
Deploy automated audit trail logging for real-time fund manager inspection
Reference links and supporting material
Synthetic financial holding statements and cash flow projections for Singapore Variable Capital Companies (VCCs).
How this agent runs
Evaluates NAV calculation precision and adherence to VCC reporting schema.
Challenge input
JSON portfolio holding summary
Pydantic
Standard validation library for structural financial models.
Evaluated output
JSON computed NAV and token price
- Verifies that token price exactly matches formula output.
- Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01
- Benchmark: FundVal-Math-Bench
- Valuation Precision target: 0.0001
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Zed · Required
- Ray Tune · Optional
- Google ADK · Optional
Evaluation contract
- nav_math_check · Weight 1
- valuation_precision · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.