ta
Technical Analysis Library in Python
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Source distribution (ta-0.11.0.tar.gz) suggests a build environment may be needed at install time; verify build tool availability before installation.
- 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 · maintenance · safety
The MIT License (MIT) (permissive) — MIT License (permissive) places no restrictions on commercial or private use, modification, or redistribution, provided the license and copyright notice are retained.
last release 2023-11-02 (1016 days) · last repo commit 2026-03-18 · 5,141 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 942,654 downloads/mo, #4,674 on PyPI
Alternatives
Verify before relying
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)- 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.
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 on it.
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
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.
Source distribution (ta-0.11.0.tar.gz) suggests a build environment may be needed at install time; verify build tool availability before installation.
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)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 |
| Classifiers | Intended Audience :: Financial and Insurance IndustryLicense :: OSI Approved :: MIT LicenseProgramming Language :: Python :: 3.6Programming Language :: Python :: 3.7 |
Evidence: ta-0.11.0.tar.gz
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “financial time series features”
- taComputes technical analysis indicators (volume, volatility, trend)…
- tsfreshAutomatically extracts hundreds of time-series features from sampled…
- QuandlQuandl provides a Python client for accessing financial and economic…
Give your agent the search over MCP, or paste the wish link into any chat.
More Information Analysis packages
A drop-in replacement for Python's standard `re` module that adds advanced regex features like nested sets, fuzzy matching, lookaround in conditionals, and full Unicode case-folding while maintaining backward compatibility.
pyarrow provides Python bindings to Apache Arrow's C++ libraries for efficient columnar data processing, serialization, and interoperability with pandas, NumPy, and other Python ecosystem tools.
NetworkX provides data structures and algorithms for creating, analyzing, and manipulating graphs and networks, supporting everything from simple undirected graphs to complex directed and weighted networks.
Connects Python applications to Snowflake data warehouses using the DB API 2.0 specification, enabling SQL queries, data transfers, and warehouse operations.
ContourPy calculates contours of 2D quadrilateral grids using C++11 algorithms wrapped in Python, offering serial and multithreaded implementations without requiring Matplotlib as a dependency.
Snowpark Python provides APIs to query and process data directly in Snowflake without moving data to your local system, with support for both native Snowpark and pandas-compatible interfaces.
Install it if you use Snowflake and want to process data without moving it to your application layer.
See also ft-pandas-ta · technical · TA-Lib · stockstats · pandas-ta-classic · pandas-ta · smartmoneyconcepts · investor-agent · vollib · py-vollib