---
id: ta
version: "0.11.0"
license: The MIT License (MIT)
license_treatment: permissive
maintenance: active
---

# ta — Technical Analysis Library in Python

License: permissive · Maintenance: active · Downloads: 942.7K/mo

## What it is and what it does

ta is a technical analysis library that computes financial indicators from time series data (open, close, high, low, volume). It implements 43 indicators across three categories: volume-based (Money Flow Index, On-Balance Volume, Chaikin Money Flow), volatility (Bollinger Bands, Keltner Channel, Average True Range), and trend (moving averages, MACD). The library is designed for feature engineering in quantitative trading and financial analysis pipelines.

The package is stable and moderately popular (top 5000 PyPI packages by downloads), with an active repository and no known security vulnerabilities. However, the source distribution format suggests potential friction at install time. It carries no license restrictions (MIT) but offers limited recent maintenance signals.

Use it for:

- Build feature sets for machine learning models trained on historical price data.
- Calculate Bollinger Bands and other volatility indicators for algorithmic trading signals.
- Compute moving averages and MACD for trend-following strategies.
- Generate volume-weighted indicators like Money Flow Index for momentum analysis.
- Backtest trading strategies that rely on technical indicator thresholds.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Computes technical analysis indicators (volume, volatility, trend) from financial time series data for feature engineering in trading and financial analysis workflows.

Yes, with conditions. Install if you need a straightforward technical indicator library for financial feature engineering and can tolerate high install friction (source distribution, potential build requirements). The MIT license is permissive, the codebase is stable, and there are no known vulnerabilities. However, verify Python version compatibility and build requirements before committing to production use.

## Install

pip install ta
uv add ta
poetry add ta

## Installing ta

Before you install: High install friction: the package distributes as a source tarball (ta-0.11.0.tar.gz) with no runtime dependencies listed, suggesting a compiled extension or build requirement. Last release was over a year ago; repository is active but the gap between releases may indicate maintenance is minimal or reactive rather than proactive.

License in practice: MIT License (permissive) places no restrictions on commercial or private use, modification, or redistribution, provided the license and copyright notice are retained.

Quickstart:

from ta.trend import sma_indicator
from ta.volatility import bollinger_hband, bollinger_lband

# Assumes financial time series data is available
sma_result = sma_indicator(close_prices, window=14)
bb_high = bollinger_hband(close_prices, window=20, window_dev=2)

Source distribution (ta-0.11.0.tar.gz) suggests a build environment may be needed at install time; verify build tool availability before installation.

Verify before relying:

- Whether the package requires a C compiler or other build tools to install from source.
- Current Python version support (classifiers list 3.6 and 3.7, both EOL; actual support unclear).
- Whether the library depends on external packages like Pandas and Numpy at runtime or build time.

## Package facts

- License: The MIT License (MIT) (permissive)
- Python support: unspecified
- Install friction: high
- Maintenance: active
- Downloads: 942.7K/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

technical analysis indicators, financial time series features, trading signal calculation, MACD bollinger bands ATR, stock price analysis library, feature engineering finance, financial-analysis, time-series, trading

[View on SkillFed](https://skillfed.io/packages/ta) · [View on PyPI](https://pypi.org/project/ta/)