Financial Markets and Investment Strategies
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Peer reviewed
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The package provides building blocks for systematic strategies and reports, including exponentially weighted covariance, bootstrap resampling, risk measures, stress testing, regime-conditional statistics, currency hedging and more.
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Quantitative portfolio managers and investors evaluate a strategy by simulating a portfolio that implements it and reporting its risk and return.Using the target weights of a strategy and the prices of its assets, qis simulates the portfolio's net asset value.It then measures the portfolio against a benchmark and renders the result as a factsheet.In addition to the backtesting engine, the package provides building blocks for systematic strategies and reports, including exponentially weighted covariance, bootstrap resampling, risk measures, stress testing, regime-conditional statistics, currency hedging, and return unsmoothing.Signal generation and portfolio construction remain with the researcher.The qis library reconstructs holdings from the target weights and applies explicit accounting and reporting conventions.The qis package is the base layer of a public stack of quantitative finance packages, whose other members are optimalportfolios, trendfollowing, and privateassets.The package depends on NumPy (Harris et al., 2020), SciPy (Virtanen et al., 2020), pandas (McKinney, 2010), statsmodels (Seabold & Perktold, 2010), Matplotlib (Hunter, 2007), seaborn (Waskom, 2021), Numba (Lam et al., 2015), openpyxl, and PyYAML, runs on Python 3.10 to 3.14 and is released under the MIT licence.
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BibTeX
@article{Sepp2026reproducible,
title = {qis: reproducible performance analytics and portfolio backtesting in Python},
author = {Artur Sepp},
journal = {The Journal of Open Source Software},
year = {2026},
doi = {10.21105/joss.11400},
url = {https://doi.org/10.21105/joss.11400}
}FAQ
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