pandas-ta
A Comprehensive Python 3 Technical Analysis Library with Pandas Dataframe Extension for Quantitative Researchers, Traders, and Investors.
Decision gist · record as of 2026-08-14
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—verify the license for your use case and be prepared for potential stability issues or slow response to bugs.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later; numba, numpy, pandas, and tqdm must be installed as runtime dependencies.
- Low install friction with a pure-Python wheel distribution.
- However, the package is in Beta status with an aging maintenance signal—last release was 334 days ago—so stability and responsiveness to issues may be limited.
License · maintenance · safety
(unclear) — License treatment is unclear; neither SPDX nor raw license metadata is available in the package record. Verify the actual license terms at the project homepage before use in commercial or proprietary work.
last release 2025-09-14 (334 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 867,325 downloads/mo, #4,857 on PyPI
Alternatives
Verify before relying
pip install pandas-ta
import pandas as pd
import pandas_ta
df = pd.read_csv('price_data.csv')
df.ta.rsi() # Compute RSI indicator- Exact number and names of the 150+ indicators and their computation accuracy
- Whether TA Lib integration is optional or required for the 60 candlestick patterns
- Current maintenance status and responsiveness to bug reports given the 334-day gap since last release
- License terms and any restrictions on commercial use
What it is and 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.
The 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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—verify the license for your use case and be prepared for potential stability issues or slow response to bugs.
Install
pandas-ta on PyPI
Before you install
Low install friction with a pure-Python wheel distribution. However, the package is in Beta status with an aging maintenance signal—last release was 334 days ago—so stability and responsiveness to issues may be limited.
Requires Python 3.12 or later; numba, numpy, pandas, and tqdm must be installed as runtime dependencies.
License in practice
License treatment is unclear; neither SPDX nor raw license metadata is available in the package record. Verify the actual license terms at the project homepage before use in commercial or proprietary work.
Quickstart
pip install pandas-ta
import pandas as pd
import pandas_ta
df = pd.read_csv('price_data.csv')
df.ta.rsi() # Compute RSI indicatorVerify before relying
- Exact number and names of the 150+ indicators and their computation accuracy
- Whether TA Lib integration is optional or required for the 60 candlestick patterns
- Current maintenance status and responsiveness to bug reports given the 334-day gap since last release
- License terms and any restrictions on commercial use
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 4 packagesnumbanumpypandastqdm |
| Maintenance | Aging 334 days since the last release |
| First released | |
| Downloads | 867,325 / month, #4,857 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Financial and Insurance IndustryIntended Audience :: Science/ResearchNatural Language :: EnglishOperating System :: OS IndependentProgramming Language :: Python :: 3.12Topic :: Office/Business :: FinancialTopic :: Office/Business :: Financial :: InvestmentTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Information Analysis |
Evidence: pandas_ta-0.4.71b0-py3-none-any.whl
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