finlab
Analyzing stock has never been easier.
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you are backtesting Taiwan stock strategies and accept GPL-3.0-or-later licensing. The package is actively maintained, supports modern Python versions, and has no known vulnerabilities. Medium install friction from 10 dependencies is typical for data-science workflows. Not suitable if you require proprietary or closed-source derivative work.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; data retrieval depends on finlab's remote database access.
- Medium install friction due to 10 runtime dependencies including numpy, pandas, scipy, and cryptography.
- Package is actively maintained with recent release history and supports modern Python versions (3.9–3.14) across multiple platforms via precompiled wheels.
License · maintenance · safety
GPL-3.0-or-later (copyleft) — Licensed under GPL-3.0-or-later (copyleft). Any derivative work or distribution must also be open-source under a compatible GPL license; proprietary use or closed-source modifications are not permitted.
last release 2026-08-02 (12 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 77,351 downloads/mo, #14,533 on PyPI
Alternatives
Verify before relying
from finlab import data, backtest
close = data.get('price:收盤價')
position = close >= close.rolling(300).max()
report = backtest.sim(position, resample='M')
report.display()- Whether the 2000 stocks dataset covers all Taiwan-listed companies or a specific subset.
- Exact historical data depth and date range available beyond 'past ten years' mentioned in description.
- Performance characteristics and typical runtime for backtesting 2000 stocks as claimed.
- Whether qlib machine-learning integration is built-in or requires separate installation.
What it is and what it does
Finlab is a backtesting framework designed for Taiwan stock market analysis. It provides one-line access to historical price and fundamental data for 2000 stocks, integrates seamlessly with pandas for strategy definition, and executes multi-stock backtests with detailed performance reports. Strategies are written using familiar pandas syntax—boolean conditions on rolling windows, resampling logic, and position signals—making it accessible to traders without deep programming expertise.
The package combines data retrieval, strategy expression, and backtesting into a unified workflow. It handles multi-frequency data alignment automatically and uses Cython-optimized computation to run backtests on large stock universes in seconds. It also supports machine-learning strategy development via qlib integration. The 10 runtime dependencies (requests, numpy, pandas, pyarrow, lz4, tqdm, jinja2, ipython, scipy, cryptography) provide data fetching, numerical computation, and reporting capabilities.
Use it for
- Test a mean-reversion strategy on 2000 Taiwan stocks over a decade of historical data in minutes.
- Develop and iterate on technical-indicator-based trading rules using pandas rolling-window operations.
- Generate detailed backtest reports with performance metrics to compare strategy variants.
- Prototype machine-learning trading strategies using qlib within the finlab framework.
- Analyze historical stock performance and identify patterns across Taiwan's equity market.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you are backtesting Taiwan stock strategies and accept GPL-3.0-or-later licensing. The package is actively maintained, supports modern Python versions, and has no known vulnerabilities. Medium install friction from 10 dependencies is typical for data-science workflows. Not suitable if you require proprietary or closed-source derivative work.
Install
finlab on PyPI
Before you install
Medium install friction due to 10 runtime dependencies including numpy, pandas, scipy, and cryptography. Package is actively maintained with recent release history and supports modern Python versions (3.9–3.14) across multiple platforms via precompiled wheels.
Requires Python 3.9 or later; data retrieval depends on finlab's remote database access.
License in practice
Licensed under GPL-3.0-or-later (copyleft). Any derivative work or distribution must also be open-source under a compatible GPL license; proprietary use or closed-source modifications are not permitted.
Quickstart
from finlab import data, backtest
close = data.get('price:收盤價')
position = close >= close.rolling(300).max()
report = backtest.sim(position, resample='M')
report.display()Verify before relying
- Whether the 2000 stocks dataset covers all Taiwan-listed companies or a specific subset.
- Exact historical data depth and date range available beyond 'past ten years' mentioned in description.
- Performance characteristics and typical runtime for backtesting 2000 stocks as claimed.
- Whether qlib machine-learning integration is built-in or requires separate installation.
Package facts
| License | GPL-3.0-or-later copyleft |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 10 packagesrequestsnumpypandaspyarrowlz4tqdmjinja2ipythonscipycryptography |
| Maintenance | Actively maintained 12 days since the last release |
| First released | |
| Downloads | 77,351 / month, #14,533 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9 |
Evidence: finlab-2.0.17-cp310-cp310-macosx_10_15_universal2.whl; finlab-2.0.17-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; finlab-2.0.17-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; finlab-2.0.17-cp310-cp310-win_amd64.whl; finlab-2.0.17-cp311-cp311-macosx_10_15_universal2.whl; finlab-2.0.17-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; finlab-2.0.17-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; finlab-2.0.17-cp311-cp311-win_amd64.whl; finlab-2.0.17-cp312-cp312-macosx_10_15_universal2.whl; finlab-2.0.17-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; finlab-2.0.17-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; finlab-2.0.17-cp312-cp312-win_amd64.whl; finlab-2.0.17-cp313-cp313-macosx_10_15_universal2.whl; finlab-2.0.17-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; finlab-2.0.17-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; finlab-2.0.17-cp313-cp313-win_amd64.whl; finlab-2.0.17-cp314-cp314-macosx_10_15_universal2.whl; finlab-2.0.17-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; finlab-2.0.17-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl; finlab-2.0.17-cp314-cp314-win_amd64.whl
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