[](https://www.rust-lang.org/)
[](https://github.com/Anexen/pyxirr/blob/master/LICENSE)
[](https://pypi.org/project/pyxirr/)
[](https://pypi.org/project/pyxirr/)
# PyXIRR
Rust-powered collection of financial functions.
PyXIRR stands for "Python XIRR" (for historical reasons), but contains many other financial functions such as IRR, FV, NPV, etc.
Features:
- correct
- supports different day count conventions (e.g. ACT/360, 30E/360, etc.)
- works with different input data types (iterators, numpy arrays, pandas DataFrames)
- no external dependencies
- type annotations
- blazingly fast
# Installation
```
pip install pyxirr
```
> WASM wheels for [pyodide](https://github.com/pyodide/pyodide) are also available,
> but unfortunately are [not supported by PyPI](https://github.com/pypi/warehouse/issues/10416).
> You can find them on the [GitHub Releases](https://github.com/Anexen/pyxirr/releases) page.
# Benchmarks
Rust implementation has been tested against existing [xirr](https://pypi.org/project/xirr/) package
(uses [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html) under the hood)
and the [implementation from the Stack Overflow](https://stackoverflow.com/a/11503492) (pure python).
PyXIRR is much faster than the other implementations.
Powered by [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark) and [plotly.js](https://github.com/plotly/plotly.js).
Live benchmarks are hosted on [Github Pages](https://anexen.github.io/pyxirr/bench).
# Example
```python
from datetime import date
from pyxirr import xirr
dates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]
amounts = [-1000, 750, 500]
# feed columnar data
xirr(dates, amounts)
# feed iterators
xirr(iter(dates), (x / 2 for x in amounts))
# feed an iterable of tuples
xirr(zip(dates, amounts))
# feed a dictionary
xirr(dict(zip(dates, amounts)))
# dates as strings
xirr(['2020-01-01', '2021-01-01'], [-1000, 1200])
```
# Multiple IRR problem
The Multiple IRR problem occurs when the signs of cash flows change more than
once. In this case, we say that the project has non-conventional cash flows.
This leads to situation, where it can have more the one IRR or have no IRR at all.
PyXIRR addresses the Multiple IRR problem as follows:
1. It looks for positive result around 0.1 (the same as Excel with the default guess=0.1).
2. If it can't find a result, it uses several other attempts and selects the lowest IRR to be conservative.
Here is an example illustrating how to identify multiple IRRs:
```python
import numpy as np
import pyxirr
# load cash flow:
cf = pd.read_csv("tests/samples/30-22.csv", names=["date", "amount"])
# check whether the cash flow is conventional:
print(pyxirr.is_conventional_cash_flow(cf["amount"])) # false
# build NPV profile:
# calculate 50 NPV values for different rates
rates = np.linspace(-0.5, 0.5, 50)
# any iterable, any rates, e.g.
# rates = [-0.5, -0.3, -0.1, 0.1, -0.6]
values = pyxirr.xnpv(rates, cf)
# print NPV profile:
# NPV changes sign two times:
# 1) between -0.316 and -0.295
# 2) between -0.03 and -0.01
print("NPV profile:")
for rate, value in zip(rates, values):
print(rate, value)
# plot NPV profile
import pandas as pd
series = pd.Series(values, index=rates)
pd.DataFrame(series[series > -1e6]).assign(zero=0).plot()
# find points where NPV function crosses zero
indexes = pyxirr.zero_crossing_points(values)
print("Zero crossing points:")
for idx in indexes:
print("between", rates[idx], "and", rates[idx+1])
# XIRR has two results:
# -0.31540826742734207
# -0.028668460065441048
for i, idx in enumerate(indexes, start=1):
rate = pyxirr.xirr(cf, guess=rates[idx])
npv = pyxirr.xnpv(rate, cf)
print(f"{i}) {rate}; XNPV = {npv}")
```
# More Examples
### Numpy and Pandas
```python
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}))
# feed Series with DatetimeIndex
xirr(pd.Series(amounts, index=pd.to_datetime(dates)))
# bonus: apply xirr to a DataFrame with DatetimeIndex:
df = pd.DataFrame(
index=pd.date_range("2021", "2022", freq="MS", inclusive="left"),
data={
"one": [-100] + [20] * 11,
"two": [-80] + [19] * 11,
},
)
df.apply(xirr) # Series(index=["one", "two"], data=[5.09623547168478, 8.780801977141174])
```
### Day count conventions
Check out the available options on the [docs/day-count-conventions](https://anexen.github.io/pyxirr/functions.html#day-count-conventions).
```python
from pyxirr import DayCount
xirr(dates, amounts, day_count=DayCount.ACT_360)
# parse day count from string
xirr(dates, amounts, day_count="30E/360")
```
### Private equity performance metrics
```python
from pyxirr import pe
pe.pme_plus([-20, 15, 0], index=[100, 115, 130], nav=20)
pe.direct_alpha([-20, 15, 0], index=[100, 115, 130], nav=20)
```
[Docs](https://anexen.github.io/pyxirr/private_equity.html)
### Other financial functions
```python
import pyxirr
# Future Value
pyxirr.fv(0.05/12, 10*12, -100, -100)
# Net Present Value
pyxirr.npv(0, [-40_000, 5_000, 8_000, 12_000, 30_000])
# IRR
pyxirr.irr([-100, 39, 59, 55, 20])
# ... and more! Check out the docs.
```
[Docs](https://anexen.github.io/pyxirr/functions.html)
### Vectorization
PyXIRR supports numpy-like vectorization.
If all input is scalar, returns a scalar float. If any input is array_like,
returns values for each input element. If multiple inputs are
array_like, performs broadcasting and returns values for each element.
```python
import pyxirr
# feed list
pyxirr.fv([0.05/12, 0.06/12], 10*12, -100, -100)
pyxirr.fv([0.05/12, 0.06/12], [10*12, 9*12], [-100, -200], -100)
# feed numpy array
import numpy as np
rates = np.array([0.05, 0.06, 0.07])/12
pyxirr.fv(rates, 10*12, -100, -100)
# feed any iterable!
pyxirr.fv(
np.linspace(0.01, 0.2, 10),
(x + 1 for x in range(10)),
range(-100, -1100, -100),
tuple(range(-100, -200, -10))
)
# 2d, 3d, 4d, and more!
rates = [[[[[[0.01], [0.02]]]]]]
pyxirr.fv(rates, 10*12, -100, -100)
```
# API reference
See the [docs](https://anexen.github.io/pyxirr)
# Roadmap
- [x] Implement all functions from [numpy-financial](https://numpy.org/numpy-financial/latest/index.html)
- [x] Improve docs, add more tests
- [x] Type hints
- [x] Vectorized versions of numpy-financial functions.
- [ ] Compile library for rust/javascript/python
# 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](https://github.com/PyO3/pyo3/issues/341), [#771](https://github.com/PyO3/pyo3/issues/771).
If you are using `pyenv`, make sure you have the shared library installed (check for `${PYENV_ROOT}/versions/<version>/lib/libpython3.so` file).
```bash
$ PYTHON_CONFIGURE_OPTS="--enable-shared" pyenv install <version>
```
Install dev-requirements
```bash
$ pip install -r dev-requirements.txt
```
### Building
```bash
$ maturin develop
```
### Testing
```bash
$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo test
```
### Benchmarks
```bash
$ pip install -r bench-requirements.txt
$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo +nightly bench
```
# Building and distribution
This library uses [maturin](https://github.com/PyO3/maturin) to build and distribute python wheels.
```bash
$ docker run --rm -v $(pwd):/io ghcr.io/pyo3/maturin build --release --manylinux 2010 --strip
$ maturin upload target/wheels/pyxirr-${version}*
```
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"description": "[](https://www.rust-lang.org/)\n[](https://github.com/Anexen/pyxirr/blob/master/LICENSE)\n[](https://pypi.org/project/pyxirr/)\n[](https://pypi.org/project/pyxirr/)\n\n# PyXIRR\n\nRust-powered collection of financial functions.\n\nPyXIRR stands for \"Python XIRR\" (for historical reasons), but contains many other financial functions such as IRR, FV, NPV, etc.\n\nFeatures:\n\n- correct\n- supports different day count conventions (e.g. ACT/360, 30E/360, etc.)\n- works with different input data types (iterators, numpy arrays, pandas DataFrames)\n- no external dependencies\n- type annotations\n- blazingly fast\n\n# Installation\n\n```\npip install pyxirr\n```\n\n> WASM wheels for [pyodide](https://github.com/pyodide/pyodide) are also available,\n> but unfortunately are [not supported by PyPI](https://github.com/pypi/warehouse/issues/10416).\n> You can find them on the [GitHub Releases](https://github.com/Anexen/pyxirr/releases) page.\n\n# Benchmarks\n\nRust implementation has been tested against existing [xirr](https://pypi.org/project/xirr/) package\n(uses [scipy.optimize](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.newton.html) under the hood)\nand the [implementation from the Stack Overflow](https://stackoverflow.com/a/11503492) (pure python).\n\n\n\nPyXIRR is much faster than the other implementations.\n\nPowered by [github-action-benchmark](https://github.com/benchmark-action/github-action-benchmark) and [plotly.js](https://github.com/plotly/plotly.js).\n\nLive benchmarks are hosted on [Github Pages](https://anexen.github.io/pyxirr/bench).\n\n# Example\n\n```python\nfrom datetime import date\nfrom pyxirr import xirr\n\ndates = [date(2020, 1, 1), date(2021, 1, 1), date(2022, 1, 1)]\namounts = [-1000, 750, 500]\n\n# feed columnar data\nxirr(dates, amounts)\n# feed iterators\nxirr(iter(dates), (x / 2 for x in amounts))\n# feed an iterable of tuples\nxirr(zip(dates, amounts))\n# feed a dictionary\nxirr(dict(zip(dates, amounts)))\n# dates as strings\nxirr(['2020-01-01', '2021-01-01'], [-1000, 1200])\n```\n\n# Multiple IRR problem\n\nThe Multiple IRR problem occurs when the signs of cash flows change more than\nonce. In this case, we say that the project has non-conventional cash flows.\nThis leads to situation, where it can have more the one IRR or have no IRR at all.\n\nPyXIRR addresses the Multiple IRR problem as follows:\n\n1. It looks for positive result around 0.1 (the same as Excel with the default guess=0.1).\n2. If it can't find a result, it uses several other attempts and selects the lowest IRR to be conservative.\n\nHere is an example illustrating how to identify multiple IRRs:\n\n```python\nimport numpy as np\nimport pyxirr\n\n# load cash flow:\ncf = pd.read_csv(\"tests/samples/30-22.csv\", names=[\"date\", \"amount\"])\n# check whether the cash flow is conventional:\nprint(pyxirr.is_conventional_cash_flow(cf[\"amount\"])) # false\n\n# build NPV profile:\n# calculate 50 NPV values for different rates\nrates = np.linspace(-0.5, 0.5, 50)\n# any iterable, any rates, e.g.\n# rates = [-0.5, -0.3, -0.1, 0.1, -0.6]\nvalues = pyxirr.xnpv(rates, cf)\n\n# print NPV profile:\n# NPV changes sign two times:\n# 1) between -0.316 and -0.295\n# 2) between -0.03 and -0.01\nprint(\"NPV profile:\")\nfor rate, value in zip(rates, values):\n print(rate, value)\n\n# plot NPV profile\nimport pandas as pd\nseries = pd.Series(values, index=rates)\npd.DataFrame(series[series > -1e6]).assign(zero=0).plot()\n\n# find points where NPV function crosses zero\nindexes = pyxirr.zero_crossing_points(values)\n\nprint(\"Zero crossing points:\")\nfor idx in indexes:\n print(\"between\", rates[idx], \"and\", rates[idx+1])\n\n# XIRR has two results:\n# -0.31540826742734207\n# -0.028668460065441048\nfor i, idx in enumerate(indexes, start=1):\n rate = pyxirr.xirr(cf, guess=rates[idx])\n npv = pyxirr.xnpv(rate, cf)\n print(f\"{i}) {rate}; XNPV = {npv}\")\n```\n\n# More Examples\n\n### Numpy and Pandas\n\n```python\nimport numpy as np\nimport pandas as pd\n\n# feed numpy array\nxirr(np.array([dates, amounts]))\nxirr(np.array(dates), np.array(amounts))\n\n# feed DataFrame (columns names doesn't matter; ordering matters)\nxirr(pd.DataFrame({\"a\": dates, \"b\": amounts}))\n\n# feed Series with DatetimeIndex\nxirr(pd.Series(amounts, index=pd.to_datetime(dates)))\n\n# bonus: apply xirr to a DataFrame with DatetimeIndex:\ndf = pd.DataFrame(\n index=pd.date_range(\"2021\", \"2022\", freq=\"MS\", inclusive=\"left\"),\n data={\n \"one\": [-100] + [20] * 11,\n \"two\": [-80] + [19] * 11,\n },\n)\ndf.apply(xirr) # Series(index=[\"one\", \"two\"], data=[5.09623547168478, 8.780801977141174])\n```\n\n### Day count conventions\n\nCheck out the available options on the [docs/day-count-conventions](https://anexen.github.io/pyxirr/functions.html#day-count-conventions).\n\n```python\nfrom pyxirr import DayCount\n\nxirr(dates, amounts, day_count=DayCount.ACT_360)\n\n# parse day count from string\nxirr(dates, amounts, day_count=\"30E/360\")\n```\n\n### Private equity performance metrics\n\n```python\nfrom pyxirr import pe\n\npe.pme_plus([-20, 15, 0], index=[100, 115, 130], nav=20)\n\npe.direct_alpha([-20, 15, 0], index=[100, 115, 130], nav=20)\n```\n\n[Docs](https://anexen.github.io/pyxirr/private_equity.html)\n\n### Other financial functions\n\n```python\nimport pyxirr\n\n# Future Value\npyxirr.fv(0.05/12, 10*12, -100, -100)\n\n# Net Present Value\npyxirr.npv(0, [-40_000, 5_000, 8_000, 12_000, 30_000])\n\n# IRR\npyxirr.irr([-100, 39, 59, 55, 20])\n\n# ... and more! Check out the docs.\n```\n\n[Docs](https://anexen.github.io/pyxirr/functions.html)\n\n### Vectorization\n\nPyXIRR supports numpy-like vectorization.\n\nIf all input is scalar, returns a scalar float. If any input is array_like,\nreturns values for each input element. 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In short, you need to compile your tests without `extension-module` feature to avoid linking errors.\nSee the following issues for the details: [#341](https://github.com/PyO3/pyo3/issues/341), [#771](https://github.com/PyO3/pyo3/issues/771).\n\nIf you are using `pyenv`, make sure you have the shared library installed (check for `${PYENV_ROOT}/versions/<version>/lib/libpython3.so` file).\n\n```bash\n$ PYTHON_CONFIGURE_OPTS=\"--enable-shared\" pyenv install <version>\n```\n\nInstall dev-requirements\n\n```bash\n$ pip install -r dev-requirements.txt\n```\n\n### Building\n\n```bash\n$ maturin develop\n```\n\n### Testing\n\n```bash\n$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo test\n```\n\n### Benchmarks\n\n```bash\n$ pip install -r bench-requirements.txt\n$ LD_LIBRARY_PATH=${PYENV_ROOT}/versions/3.10.8/lib cargo +nightly bench\n```\n\n# Building and distribution\n\nThis library uses [maturin](https://github.com/PyO3/maturin) to build and distribute python wheels.\n\n```bash\n$ docker run --rm -v $(pwd):/io ghcr.io/pyo3/maturin build --release --manylinux 2010 --strip\n$ maturin upload target/wheels/pyxirr-${version}*\n```\n\n",
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