{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/4"}],"enrichment":{"capability":"Backtest trading strategies on Taiwan stock market data by writing simple pandas-based logic, with historical data for 2000 stocks and detailed performance analysis.","skillfed_tags":["backtesting","quantitative-trading","taiwan-stocks"],"use_cases":["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."],"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\u2014boolean conditions on rolling windows, resampling logic, and position signals\u2014making it accessible to traders without deep programming expertise.\n\nThe 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.","worth_installing":"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."},"id":"finlab","links":{"html":"https://skillfed.io/packages/finlab","md":"https://skillfed.io/packages/finlab.md","pypi":"https://pypi.org/project/finlab/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-02","license_spdx":"GPL-3.0-or-later","license_treatment":"copyleft","name":"finlab","python_support":"supports_current","summary":"Analyzing stock has never been easier."},"popularity":{"monthly_downloads":77351,"position":14533,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.0.17"}