nanobook Python bindings

Rust extension plus pure-Python research helpers. Build with uv sync --group dev from this directory.

Monte Carlo scenarios (Rust-backed, parity-safe)

nanobook.scenarios provides terminal price distributions for stress testing and forecasting. When the extension is built with the scenarios feature (default in wheels), int/None seeds delegate to Rust: NumPy PCG64 draws feed Rust math so results match the nanotrade/calc reference at tight tolerance. A pure-Python fallback remains for random.Random seeds and environments without numpy.

import nanobook

res = nanobook.monte_carlo_stock_valuation(
    "XYZ",
    74.0,
    version="advanced",
    n_paths=200,
    seed=42,
    gp_growth_mean=0.16,
    multiple_mean=22.0,
    macro_shock_mean=-0.03,
)
print(res)  # MonteCarloResult(..., median_price=86.36)
print(res.median_price, res.implied_median_annual_return)

paths = res.to_price_paths(4, method="linear")
# See examples/scenario_backtest.py for feeding paths into backtest_weights.

Regenerate frozen parity fixtures:

cd ../../nanotrade && uv run python ../nanobook/python/scripts/generate_scenarios_parity.py

Run scenario tests:

uv run pytest tests/test_scenarios*.py tests/property/test_prop_scenarios.py tests/reference/test_ref_scenarios.py -q

Project links

Data verified by PyPI on Jul 25, 2026
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  • Released:
Newer release available (0.18.0)

1 maintainer

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Avatar for ricardofrantz from gravatar.com ricardofrantz

Credits

Author: Ricardo Frantz

GitHub Statistics

Data verified by PyPI on Jul 25, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

MIT
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Requires

Python >=3.11

Tags

orderbook trading matching-engine backtesting finance

Classifiers

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