ta

library0.11.0pypypi✓ verified 22d ago

The `ta` library (Technical Analysis Library in Python, also known as `python-ta`) is a Python library designed for feature engineering on financial time series datasets. It provides a comprehensive collection of over 150 technical indicators and candlestick pattern recognition functions, built entirely on the Pandas library. Version 0.11.0 is the current release, and the project maintains an active, albeit intermittent, release cadence with new features and bug fixes.

data ai-ml

pip install ta

github ↗package ↗

INSTALL

IMPORT

SIG · TA

datapythonv0.11.0

Install

8.8s avg

Import

928ms

Disk

166MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.11.0 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.954s · 166.3MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 8.8s · import 0.902s · 159MB

166MB installed

● package 166MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

add_all_ta_features

✓ from ta import add_all_ta_features

✗ import ta

While `import ta` works, `add_all_ta_features` is a common entry point to apply many indicators easily, or import specific indicator classes from `ta.momentum`, `ta.trend`, etc.

dropna

✓ from ta.utils import dropna

Utility function for cleaning NaN values from DataFrames.

RSIIndicator

✓ from ta.momentum import RSIIndicator

Example of importing a specific indicator class. Most indicators are available under submodules like `ta.momentum`, `ta.trend`, `ta.volatility`, etc.

This quickstart demonstrates how to load a DataFrame with typical OHLCV (Open, High, Low, Close, Volume) data, clean potential NaN values, and then apply all available technical analysis features using `add_all_ta_features`. The resulting DataFrame will include numerous new columns corresponding to various indicators.

import pandas as pd from ta import add_all_ta_features from ta.utils import dropna # Example DataFrame (replace with your actual data) data = { 'Open': [100, 102, 101, 105, 103, 106, 108, 107, 109, 110], 'High': [103, 104, 105, 106, 107, 108, 110, 110, 112, 113], 'Low': [98, 100, 99, 102, 100, 103, 105, 104, 106, 107], 'Close': [102, 101, 104, 103, 106, 107, 109, 108, 111, 112], 'Volume': [1000, 1200, 1100, 1500, 1300, 1400, 1600, 1550, 1700, 1800] } df = pd.DataFrame(data) # Ensure data has required columns (Open, High, Low, Close, Volume) # Clean NaN values (optional, but recommended if your data has them) df_cleaned = dropna(df) # Add all technical analysis features df_features = add_all_ta_features( df_cleaned, open="Open", high="High", low="Low", close="Close", volume="Volume" ) print(df_features.head())

Debug

Known issues

gotchaThis library (`ta` by bukobasabino) is *not* the `TA-Lib` Python wrapper (which uses `import talib`). Despite similar names and functionality, they are distinct projects with different APIs and installation requirements. This `ta` library is pure Python and easier to install, while `TA-Lib` is a Cython wrapper around a C library.

fix

Be clear about which library you intend to use. If you need the `TA-Lib` wrapper for performance or specific `TA-Lib` functions, install `TA-Lib` (often `pip install TA-Lib` after installing the C library). If you prefer a pure Python solution, use this `ta` library.

affects: All versions

gotchaThe library expects financial time series data with 'Open', 'High', 'Low', 'Close', and 'Volume' columns. Many indicators will produce NaN values for an initial 'lookback' period. It is crucial to handle or clean NaN values in your input DataFrame, either by using `ta.utils.dropna` or other Pandas methods, before applying indicators.

fix

Always preprocess your DataFrame to handle missing data before passing it to `ta` functions. Use `df = ta.utils.dropna(df)` or `df = df.dropna()` as appropriate.

affects: All versions

gotchaWhile the `ta` library is robust, for extremely large datasets or highly performance-critical real-time applications, the `TA-Lib` Python wrapper (often imported as `talib`) may offer superior performance due to its C-compiled core. The `ta` library prioritizes ease of installation and pure Python implementation.

fix

Evaluate your project's performance requirements. If absolute speed is paramount and you are comfortable with the more complex installation of the underlying C `TA-Lib` library, consider using `TA-Lib` (the wrapper) instead of `ta`.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'ta'

The `ta` library is not installed in the current Python environment or is not accessible on the Python path.

fix

Install the library using pip: `pip install ta`

KeyError: 'high'

The input DataFrame is missing one or more required column names ('open', 'high', 'low', 'close', 'volume') expected by `ta` functions like `add_all_ta_features`.

fix

Ensure your DataFrame columns are correctly named (case-sensitive) or rename them to match the expected lowercase format before passing to `ta` functions. Example: `df.rename(columns={'Open': 'open', 'High': 'high', 'Low': 'low', 'Close': 'close', 'Volume': 'volume'}, inplace=True)`

TypeError: missing 1 required positional argument: 'close'

An individual `ta` indicator function was called without providing the necessary pandas Series as a keyword argument (e.g., `close=df['close']`).

fix

Pass the required pandas Series to the indicator function using the correct keyword argument. Example: `ta.momentum.rsi(close=df['close'], window=14, fillna=True)`

TypeError: 'module' object is not callable

The `ta` module itself was attempted to be called as a function, rather than accessing its specific attributes (functions, submodules, or classes).

fix

Access specific functions or attributes from the `ta` module. For example, use `ta.add_all_ta_features(...)` or `ta.volatility.bollinger_hband(...)` instead of `ta(...)`.

Upgrade

Version history

0.11.0latest on PyPI · released Nov 2, 2023

Audit

Dependencies

pandasrequiredCore data structure for time series processing and integration.

numpyrequiredNumerical operations and array handling, underlying dependency for pandas.

Agent activity

19 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/bukosabino/ta ↗

packagehttps://pypi.org/project/ta/ ↗