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ta-lib/ta-lib-python

11,954CythonAudience · dataComplexity · 3/5Setup · moderate

TLDR

TA-Lib for Python is a fast Cython wrapper that gives Python and Pandas users access to over 150 financial technical analysis indicators, MACD, RSI, Bollinger Bands, candlestick patterns, running 2-4x faster than the original bindings.

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  root((TA-Lib Python))
    What it does
      150 plus indicators
      Candlestick patterns
      Faster than original
    Indicators
      MACD RSI
      Bollinger Bands
      Stochastic
    Data Compatibility
      Pandas DataFrames
      Polars DataFrames
      NumPy arrays
    Installation
      C library first
      Then pip install
      Pre-built wheels
    Platforms
      Linux macOS Windows
      x86 and ARM
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Things people build with this

USE CASE 1

Calculate RSI, MACD, Bollinger Bands, and 150+ other technical indicators on stock price data stored in Pandas or Polars DataFrames.

USE CASE 2

Detect candlestick patterns in OHLC price data for algorithmic trading strategy development.

USE CASE 3

Speed up existing TA-Lib calculations by switching to this Cython-based wrapper that runs 2-4x faster than the official Python bindings.

USE CASE 4

Install pre-built binary wheels for Linux, macOS, or Windows on both x86 and ARM without needing to compile the C library from source.

Tech stack

PythonCythonCPandasPolars

Getting it running

Difficulty · moderate Time to first run · 30min

Requires installing the TA-Lib C library separately on your OS before pip-installing the Python wrapper, pre-built wheels available for many platforms.

In plain English

TA-Lib for Python is a wrapper that makes a well-known financial analysis library, TA-Lib, available from Python code. TA-Lib is a C library that traders and quantitative developers use to calculate technical indicators from price data. Things like MACD, RSI, Bollinger Bands, and the Stochastic oscillator are all in there, along with over 150 other indicators and a set of candlestick pattern recognition functions. This Python package gives you access to all of that from a Python script or notebook. The wrapper is built with Cython rather than the older SWIG binding approach that TA-Lib once used officially. The practical difference is that this version is easier to install and runs the calculations two to four times faster than the original Python bindings. It also works with Pandas DataFrames and Polars DataFrames, which are the data structures most Python data analysts already use. Installation has two parts. First, you need to install the underlying TA-Lib C library on your operating system. On a Mac this is one Homebrew command, on Windows there is an installer package, on Linux you download and compile from source. After that, you install the Python wrapper with pip. The README covers common error messages that appear when the C library is not found and explains how to point the installer at a custom installation location using environment variables. Starting with version 0.6.5, the project publishes pre-built binary wheels for many platform and Python version combinations, including Linux, macOS, and Windows on both x86 and ARM processors. If a matching wheel is available for your setup, installation skips the compile step entirely. The project tracks three parallel version branches because the upstream C library changed its name in version 0.6 and that change affected how the Python wrapper links to it. The README explains which Python package version corresponds to which version of the underlying C library, so you can match them correctly.

Copy-paste prompts

Prompt 1
Using ta-lib-python with Pandas, show me how to calculate RSI, MACD, and Bollinger Bands for a stock's daily closing prices stored in a DataFrame column called close.
Prompt 2
I want to scan OHLCV data for bullish candlestick patterns like Hammer or Morning Star using TA-Lib. Show me how to loop through the data and flag detected patterns.
Prompt 3
How do I install the TA-Lib C library on Ubuntu Linux and then install the ta-lib Python wrapper? What are the most common errors if the C library is not found?
Prompt 4
Show me how to build a simple moving average crossover strategy using ta-lib-python and Pandas that generates buy and sell signals when the 20-day SMA crosses the 50-day SMA.
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