ta
Technical Analysis Library in Python
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 on this page — 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
ta on PyPI
pip
pip install tauv
uv add tapoetry
poetry add taInstalling 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 | not specified |
| Install friction | high — source build required |
| Runtime dependencies | none |
| Maintenance | actively maintained — 1,016 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 942,654/month — #4,674 on PyPI (30-day window, as of 2026-08-14) |
| Known vulnerabilities | none known (OSV.dev, checked 2026-08-14) |
Evidence: ta-0.11.0.tar.gz
Keywords: technical, analysis
Tags
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