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Python library providing performance, risk, and distribution metrics for quantitative finance workflows

Project description

Python NumPy Pandas Quant Finance Research Grade

Research-Grade Performance & Risk Metrics
Statistically consistent analytics for quantitative finance

sigmaquant is a lightweight Python library providing performance, risk, and distribution metrics for quantitative finance workflows.

📦 Installation (PyPI Package)


Install the package from PyPI:

pip install sigma-quant

📘Documentation


This README is intentionally high-level.

For the complete API reference, mathematical definitions, statistical conventions, and usage examples, please refer to the official documentation:

https://quant-kit.readthedocs.io/en/stable/index.html

ASCII Report

Generate a fast portfolio performance report using ascii_report. If the input series represents PnL, set kind="pnl" and optionally specify the currency.

import sigmaquant

report = sigmaquant.ascii_report(
    returns=portfolio_returns,
    frequency="D",
    strategy_name="My Portfolio",
    kind="simple",
)

print(report)

# Optional exports
df = report.to_dataframe()
data = report.to_dict()

🪪 License


MIT © 2025 — Developed with ❤️ by Lorenzo Santarsieri & Tommaso Grandi

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