v1.0.0 — Initial Public Release

QuantScenarioBench's first stable release: a JAX-native library for generating reproducible stochastic market scenarios, with three v1 market models and a built-in path to the Hugging Face Hub.

Highlights

• Common state-space interface — MarketModel, Scenario, TimeGrid, and Metadata give every model the same shape, so you can swap BlackScholes for Heston or RoughBergomi without touching simulation or export code.
• Three validated models
  • Black-Scholes (GBM) — checked against the closed-form pricing formula
  • Heston (stochastic volatility) — checked against semi-closed-form (Gil-Pelaez) pricing
  • Rough Bergomi (non-Markovian rough volatility) — checked against statistical skew-monotonicity properties
• Reproducibility by construction — identical (model, time_grid, n_paths, seed) produce bit-identical paths on the same backend; every batch carries full provenance metadata.
• Export & publish — export_parquet() for a fixed 12-column schema, and publish_to_hub() to push straight to the Hugging Face Hub with an auto-generated dataset card.
• Extensible by design — implement MarketModel._drift / _diffusion / initial_state and a conformance test suite verifies your model behaves correctly, with no changes to the rest of the library.

Pre-built benchmark samples

Three lightweight, quick-loading sample datasets are live under the QuantScenarioBench org, generated with library_version=1.0.0:

• qsb-black-scholes
• qsb-heston
• qsb-rough-bergomi

For research-scale datasets, generate your own with simulate() and export with export_parquet() / publish_to_hub() — see the README for details.

Installation

pip install quantscenariobench

Changelog

See CHANGELOG.md for the full list of changes.