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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.

Workflow AutomationHosted by Vera
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What you are building

Build an automated private market tokenized asset valuation and NAV pipeline for Singapore fund managers.

How work is evaluated

Evaluation

Evaluates NAV calculation precision and adherence to VCC reporting schema.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

nav_math_check

Verifies that token price exactly matches formula output.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

valuation_precision

Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • 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

Resources

Dataset notes

Synthetic financial holding statements and cash flow projections for Singapore Variable Capital Companies (VCCs).

Challenge setup

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

Evaluation checks
  • Verifies that token price exactly matches formula output.
  • Absolute precision error on token NAV calculation. • target: 0.0001 • range: 0-0.01
Configured evaluation evidence
  • Benchmark: FundVal-Math-Bench
  • Valuation Precision target: 0.0001
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Zed · Required
  • Ray Tune · Optional
  • Google ADK · Optional

Evaluation contract

  • nav_math_check · Weight 1
  • valuation_precision · Weight 1

Metrics

  • valuation_precision · Target 0.0001

Benchmarks

  • FundVal-Math-Bench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON portfolio holding summary

    Output: JSON computed NAV and token price

    Execution harness: Python

Evaluation tools

  • Pydantic · Data Schema Enforcement

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Tokenized Private Market Fund Asset Valuation Engine