FinanceDatabase — GitHub Analysis
Verdict: FinanceDatabase 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.
FinanceDatabase exhibits reduced maintenance velocity with 4 open issues and prolonged turnaround on pull requests. Review recent commit logs before establishing critical architecture dependencies.
Low issue backlog pressure (4 open issues comfortably within community capacity)
Established ecosystem adoption: 9,304 stars
Standard OSI-approved license: MIT License
Clear installation guide with runnable package manager commands
Zero known critical CVEs reported in dependency footprint
- Active open-source community adoption (9.3k stars)
- OSI-compliant MIT License licensing terms
- Verify performance benchmarks against your specific target workload
What is FinanceDatabase? (1/30)
01 / 30To be the definitive offline reference database for financial symbols in Python.
Is FinanceDatabase Production Ready? (2/30)
02 / 30FinanceDatabase is an open-source Python database containing over 300,000 financial symbols covering Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies, and Money Markets.
Eliminates the friction of gathering scattered, unstructured financial ticker symbols and reference data across multiple web sources.
Is FinanceDatabase Actively Maintained? (3/30)
03 / 30Should You Use FinanceDatabase? AI Verdict & Grade
Grade BFinanceDatabase is evaluated as production-grade.
Strengths, Weaknesses & Final Verdict for FinanceDatabase (30/30)
30 / 30- →FinanceDatabase is FinanceDatabase is an open-source Python database containing over 300,000 f
- →Target: Quantitative analysts, financial developers, data scientists, algorithmic traders, and finance students.
- →AI Score: 89/100 (Grade: B)
- →Security: Low, relies primarily on pandas.
- →Verdict: FinanceDatabase is evaluated as production-grade.
- ✓Fast local queries since data is packaged locally.
- ✓High, as it is a read-only static dataset with no active server components.
- ✓Strong engagement on GitHub with over 8k stars.
- ✓Simple pip installation and straightforward pandas-based query syntax.
- ✓Clear and concise README examples.
- ✓Clean, modular Python code.
- ✗Real-time streaming prices
- ✗Historical tick data
- ✗Reliance on periodic manual or automated dataset refreshes
- ✗Advanced custom filtering examples
- ✗Memory intensive if all 300k+ records are loaded simultaneously into pandas without filtering.
- ✗None significant.
- ✗Minimal.