pandas-ta

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

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.

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

pip install pandas-ta

uv add pandas-ta

poetry add pandas-ta

Installing pandas-ta

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.

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 indicator

Requires Python 3.12 or later; numba, numpy, pandas, and tqdm must be installed as runtime dependencies.

Verify 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

Evidence: pandas_ta-0.4.71b0-py3-none-any.whl

Keywords: ai, dataframe extension, finance, indicators, library, machine learning, pandas, ta, technical analysis, trading

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