pyxirr 0.4.0

Rust-powered collection of financial functions.

PyXIRR

Rust-powered collection of financial functions.

Features:

• correct
• blazingly fast
• works with iterators
• works with unordered input
• no external dependencies

Installation

pip install pyxirr

Benchmarks

Rust implementation has been tested against existing xirr package (uses scipy.optimize under the hood) and the implementation from the Stack Overflow (pure python).

Implementation	Sample size	Execution time
pyxirr (Rust)	100	45.89 us
xirr (scipy)	100	790.76 us
pure Python	100	14.37 ms
pyxirr (Rust)	1000	404.03 us
xirr (scipy)	1000	3.47 ms
pure Python	1000	35.97 ms
pyxirr (Rust)	10000	3.58 ms
xirr (scipy)	10000	28.04 ms
pure Python	10000	24.23 s

PyXIRR is ~10-20x faster than other solutions!

Examples

from datetime import date
from pyxirr import xirr

dates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]
amounts = [-1000, 1000, 1000]

# feed columnar data
xirr(dates, amounts)
# feed iterators
xirr(iter(dates), (x for x in amounts))
# feed an iterable of tuples
xirr(zip(dates, amounts))
# feed a dictionary
xirr(dict(zip(dates, amounts)))

Numpy and Pandas support

import numpy as np
import pandas as pd

# feed numpy array
xirr(np.array([dates, amounts]))
xirr(np.array(dates), np.array(amounts))
# feed DataFrame (columns names doesn't matter; ordering matters)
xirr(pd.DataFrame({"a": dates, "b": amounts}))

API reference

Let's define type annotations:

# `None` if the calculation fails to converge or result is not finite (`inf` or `nan`)
FiniteOrNone = Optional[float]

DateLike = Union[datetime.date, datetime.datetime, numpy.datetime64, pandas.Timestamp]
Amount = Union[int, float, Decimal]
Payment = Tuple[DateLike, Amount]

DateLikeArray = Iterable[DateLike]
AmountArray = Iterable[Amount]
CashFlowTable = Iterable[Payment]
CashFlowDict = Dict[DateLike, Amount]

Exceptions

• InvalidPaymentsError. Occurs if either:
  • the amounts and dates arrays (AmountArray, DateLikeArray) are of different lengths
  • the given arrays do not contain at least one negative and at least one positive value

XIRR

# raises: InvalidPaymentsError
def xirr(
    dates: Union[CashFlowTable, CashFlowDict, DateLikeArray],
    amounts: Optional[AmountArray] = None,
    guess: Optional[float] = None,
) -> FiniteOrNone

XNPV

# raises: InvalidPaymentsError
def xnpv(
    rate: float,
    dates: Union[CashFlowTable, CashFlowDict, DateLikeArray],
    amounts: Optional[AmountArray] = None,
) -> FiniteOrNone

IRR

# raises: InvalidPaymentsError
def irr(amounts: AmountArray, guess: Optional[float] = None) -> FiniteOrNone

NPV

# raises: InvalidPaymentsError
def npv(rate: float, amounts: AmountArray) -> FiniteOrNone

Roadmap

• NumPy support
• XIRR
• XNPV
• FV
• PV
• NPV
• IRR
• MIRR

Development

Running tests with pyo3 is a bit tricky. In short, you need to compile your tests without extension-module feature to avoid linking errors. See the following issues for the details: #341, #771.

If you are using pyenv, make sure you have the shared library installed (check for ${PYENV_ROOT}/versions/<version>/lib/libpython3.so file).

$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <version>

Install dev-requirements

$ pip install -r dev-requirements.txt

Building

$ maturin develop

Testing

$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.8.6/lib cargo test --no-default-features

Building and distribution

This library uses maturin to build and distribute python wheels.

$ docker run --rm -v $(pwd):/io konstin2/maturin build --release --manylinux 2010 --strip
$ maturin upload target/wheels/pyxirr-${version}*