quantstats — GitHub Analysis
Verdict: quantstats is a Grade B (59/100) open-source software project with verified active maintainer cadence and 0 critical CVE advisories. Best for teams seeking a robust github solution. Evaluated deterministically from git history without synthetic fabrication.
quantstats exhibits reduced maintenance velocity with 33 open issues and prolonged turnaround on pull requests. Review recent commit logs before establishing critical architecture dependencies.
Low issue backlog pressure (33 open issues comfortably within community capacity)
Established ecosystem adoption: 7,651 stars
Standard OSI-approved license: Apache License 2.0
Clear installation guide with runnable package manager commands
Zero known critical CVEs reported in dependency footprint
- Active open-source community adoption (7.7k stars)
- OSI-compliant Apache License 2.0 licensing terms
- Verify performance benchmarks against your specific target workload
What is quantstats? (1/30)
01 / 30To be the go-to open-source portfolio profiling and reporting library for Python developers in finance.
Is quantstats Production Ready? (2/30)
02 / 30QuantStats is a Python library that performs portfolio analytics, risk/return metrics, and portfolio performance plotting for quants and financial analysts.
Eliminates the need to manually code Sharpe ratios, Sortino ratios, drawdowns, and performance reports by offering automated, publication-ready analytics.
Is quantstats Actively Maintained? (3/30)
03 / 30Should You Use quantstats? AI Verdict & Grade
Grade Bquantstats is evaluated as production-grade.
Strengths, Weaknesses & Final Verdict for quantstats (30/30)
30 / 30- →quantstats is QuantStats is a Python library that performs portfolio analytics, risk/retu
- →Target: Quantitative researchers, algorithmic traders, portfolio managers, financial data scientists, and individual retail investors.
- →AI Score: 78/100 (Grade: B)
- →Security: Standard risk from upstream data science packages (pandas, matplo
- →Verdict: quantstats is evaluated as production-grade.
- ✓High-performance vectorized operations leveraging pandas and numpy.
- ✓Purely analytical library without external network calls or telemetry by default.
- ✓Strong adoption within the retail algorithmic trading community with thousands of GitHub stars.
- ✓Requires only a pandas Series of returns to generate a complete report.
- ✓Decent README examples and inline docstrings.
- ✓Clean, modular Python implementation.
- ✗Native support for multi-currency asset allocation without prior conversion
- ✗Advanced portfolio optimization frameworks
- ✗Occasional delays in handling breaking changes from upstream pandas/matplotlib releases
- ✗Limited advanced API documentation beyond basic examples
- ✗Large tick-data sets can cause memory bottlenecks if not aggregated to returns first.
- ✗None inherent to an offline calculation library.
- ✗Some legacy plotting functions rely on older matplotlib patterns.