{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/3"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"},{"label":"Financial","url":"https://skillfed.io/packages/category/office-business-financial"},{"label":"Investment","url":"https://skillfed.io/packages/category/office-business-financial-investment"}],"enrichment":{"capability":"Provides over 150 technical analysis indicators and 60 candlestick patterns for financial data, optimized with numba and numpy, and integrated as a pandas DataFrame extension.","skillfed_tags":["technical-analysis","quantitative-finance","trading-indicators"],"use_cases":["Compute RSI, MACD, Bollinger Bands, and other standard indicators on historical price data for backtesting trading strategies.","Recognize candlestick patterns in OHLC data to identify potential entry and exit signals in quantitative trading systems.","Build feature sets for machine learning models trained on technical indicators derived from financial time series.","Analyze market data in bulk using pandas DataFrames with vectorized indicator calculations for research workflows.","Extend existing pandas-based financial data pipelines with a comprehensive suite of pre-built technical analysis functions."],"what_it_does":"Pandas TA is a technical analysis library for Python that extends pandas DataFrames with financial indicators and candlestick pattern recognition. It wraps over 150 indicators and utilities designed for quantitative researchers, traders, and investors, leveraging numba for performance and numpy for numerical accuracy. The library integrates directly with pandas workflows, allowing you to compute indicators on OHLC data with minimal boilerplate.\n\nThe package targets financial data analysis and trading strategy development. It depends on numba, numpy, pandas, and tqdm for core functionality. Note that the library is in Beta status and has not been updated for 334 days, so it may lack recent bug fixes or feature improvements. The license terms are not clearly documented in the package metadata, which could be a concern for commercial deployments.","worth_installing":"Yes, if you need a comprehensive suite of technical indicators for financial data analysis and can work with Python 3.12+. The low install friction and large indicator library make it attractive for trading research and backtesting. However, the Beta status, 334-day maintenance gap, and unclear license terms warrant caution\u2014verify the license for your use case and be prepared for potential stability issues or slow response to bugs."},"id":"pandas-ta","links":{"html":"https://skillfed.io/packages/pandas-ta","md":"https://skillfed.io/packages/pandas-ta.md","pypi":"https://pypi.org/project/pandas-ta/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2025-09-14","license_spdx":null,"license_treatment":"unclear","name":"pandas-ta","python_support":"supports_current","summary":"A Comprehensive Python 3 Technical Analysis Library with Pandas Dataframe Extension for Quantitative Researchers, Traders, and Investors."},"popularity":{"monthly_downloads":867325,"position":4857,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.4.71b0"}