pyfinance
pyfinance is a Python package for investment management and analysis of security returns. It complements existing quantitative-finance packages such as pandas-datareader with a focused, batteries-included statistical toolkit.
Status
- Latest release: 2.0.2
- Python: 3.10, 3.11, 3.12, 3.13, 3.14
- License: MIT
Installation
uv add pyfinance
Or with pip:
pip install pyfinance
Modules
| Module | Description |
|---|---|
pyfinance.returns |
Statistical analysis of financial time series via the CAPM framework. TSeries / TFrame are Pandas subclasses that add performance statistics: annualized return/vol, Sharpe, Sortino, drawdown, capture ratios, alpha/beta, Information Ratio, and so on. |
pyfinance.ols |
Ordinary least-squares regression. OLS, RollingOLS (NumPy-backed), and PandasRollingOLS (Pandas-indexed wrapper). |
pyfinance.options |
Vectorized Black-Scholes-Merton valuation, Greeks, and implied volatility via BSM. Option strategies (Straddle, BullSpread, Butterfly, Condor, variants). |
pyfinance.general |
General-purpose computations: active share, amortization schedules, best-fit distribution, PCA on returns, portfolio simulation, tracking-error optimization, VIF. |
pyfinance.datasets |
A small set of dataset loaders: load_13f (SEC EDGAR), load_industries (Ken French), load_rates (FRED H.15), load_rf (3-month T-bill total-return series). |
pyfinance.utils |
Frequency conversion, rolling-window construction, one-hot encoding, random ticker/weights generation, availability reporting. |
Quick tutorial
TSeries — performance statistics
import numpy as np
import pandas as pd
from pyfinance import TSeries
rng = np.random.default_rng(444)
s = rng.standard_normal(400) / 100 + 0.0008
idx = pd.date_range(start="2016-01-01", periods=len(s))
ts = TSeries(s, index=idx)
ts.anlzd_ret() # annualized geometric return
ts.anlzd_stdev("D") # annualized stdev of returns
ts.max_drawdown() # worst peak-to-trough decline
ts.sharpe_ratio() # Sharpe, annualized
BSM — Black-Scholes-Merton options pricing
from pyfinance.options import BSM
op = BSM(S0=100, K=100, T=1, r=0.04, sigma=0.20)
op.value() # European call value
op.delta() # Greeks
op.implied_vol(10) # implied vol at a target price
# Vectorized across arrays of strikes.
import numpy as np
ops = BSM(S0=100, K=np.arange(100, 110), T=1, r=0.04, sigma=0.20)
ops.value()
OLS — regression
from pyfinance.ols import OLS
# y: 1-d array; x: 2-d array of explanatory variables.
model = OLS(y=y, x=x)
model.beta, model.alpha
model.rsq_adj, model.fstat, model.std_err
datasets.load_13f — SEC EDGAR 13F parser
SEC EDGAR requires a descriptive User-Agent
(policy). Supply one:
from pyfinance import datasets
url = (
"https://www.sec.gov/Archives/edgar/data/1040273/"
"000108514617001787/form13fInfoTable.xml"
)
df = datasets.load_13f(url, user_agent="Jane Doe (jane@example.com)")
Development
This repo is managed with uv:
git clone https://github.com/bsolomon1124/pyfinance
cd pyfinance
uv sync # install pinned runtime + dev deps
uv run pytest # run tests
uv run ruff check # lint
uv run ruff format # format
uv build # build sdist + wheel
Type checking uses ty:
uv run ty check
CI runs these across Python 3.10–3.14 on every push; see
.github/workflows/ci.yml.
Changelog
See CHANGELOG.md. Notably, pyfinance 2.0 dropped
load_factors, load_shiller, and load_retaildata because their
upstream sources had drifted past reasonable repair. See the 2.0
release notes for details and migration guidance.
License
MIT — see LICENSE.txt.
1 maintainer
Metadata
1 maintainer
Release files for pyfinance 2.0.2
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Source distribution (sdist)
| File | Size | Uploaded | |
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| pyfinance-2.0.2.tar.gz | 78.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pyfinance-2.0.2-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 130.7 kB
Release files / pyfinance-2.0.2.tar.gz
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