pystatsfinance 0.1.1

Financial and quantitative statistical computing for Python

PyStatsFinance

Financial and quantitative statistical computing for Python.

Status: 0.1.0 — first module shipped. Return-series performance metrics are available now; more quantitative-finance methods are on the roadmap below.

PyStatsFinance is part of the open-core PyStatistics family:

Package	Layer
pystatistics	Fundamental, general statistics
pystatsbio	Biotech / pharma statistics
pystatsclinical	Clinical-trial / clinical-research statistics
pystatsgenomic	Genomics / computational-biology statistics
pystatsfinance	Financial / quantitative statistics
pystatsinsurance	Actuarial / insurance statistics

Like its siblings, it builds on pystatistics for the general statistical layer and adds methods specific to quantitative finance.

What's available now (0.1.0)

Return-series risk and performance metrics:

from pystatsfinance import performance

returns = [0.01, -0.02, 0.03, -0.01, 0.02]

performance.sharpe_ratio(returns)              # annualized Sharpe ratio
performance.annualized_volatility(returns)     # annualized volatility
performance.max_drawdown(returns)              # worst peak-to-trough decline

sharpe_ratio annualizes mean(excess) / std(excess) (sample standard deviation; per-period risk-free rate). max_drawdown works on returns or a price series (are_prices=True) and reports the decline magnitude with its peak and trough indices. All three fail loud on empty/non-finite input, and the Sharpe ratio is raised — not returned as inf/nan — when volatility is zero.

Roadmap (candidates, not commitments)

• Value-at-Risk and Conditional VaR (historical and parametric).
• Extended performance ratios (Sortino, Calmar, Information) and drawdown analytics.
• GARCH-family volatility and portfolio optimization.

Installation

pip install pystatsfinance

License

MIT © Hai-Shuo. Part of the SGCX open-core ecosystem.