pandas-ta-classic 0.5.44

Technical Analysis Indicators - Pandas TA Classic is an easy to use Python 3 Pandas Extension with a comprehensive collection of indicators and TA-Lib patterns.

Pandas TA Classic - Technical Analysis Library

Pandas TA Classic is an easy-to-use library that leverages the Pandas package with 192 indicators and utility functions and 62 native candlestick patterns (252 total unique — no TA-Lib required). Many commonly used indicators are included, such as: Simple Moving Average (sma), Moving Average Convergence Divergence (macd), Hull Exponential Moving Average (hma), Bollinger Bands (bbands), On-Balance Volume (obv), Aroon & Aroon Oscillator (aroon), Squeeze (squeeze) and many more.

This is the classic/community maintained version of the popular pandas-ta library.

New to Pandas TA Classic?

Get started quickly with our comprehensive guides:

• Quickstart Guide - Installation, your first indicators, and common workflows
• Tutorials - Step-by-step tutorials for real-world use cases:
• Moving Average Crossover Strategy
• Building Custom Indicator Strategies
• Backtesting with Performance Metrics
• Integrating with VectorBT
• Multi-Timeframe Analysis
• Creating Custom Indicators
• Candlestick Pattern Recognition

Complete documentation: https://xgboosted.github.io/pandas-ta-classic/

Key Features

• 252 Unique Indicators & Patterns: 192 Category indicators + 62 CDL patterns via cdl_pattern() = 252 unique (doji and inside appear in both counts; all work without TA-Lib)
• All-Native Candlestick Patterns: All 62 CDL patterns have native Python implementations — native implementations are used by default; TA-Lib is only a fallback when a native implementation is unavailable
• Optional TA-Lib Acceleration: 34 core indicators (EMA, SMA, RSI, MACD, OBV, ATR, etc.) automatically use TA-Lib when installed; pass talib=False to force native
• Compatibility Scope Is Explicit: Not every TA-Lib/tulipy function has a pandas-ta-classic counterpart. Current mapping includes 67 indicators with TA-Lib counterparts, 71 with tulipy counterparts, and 44 covered by both. Full per-indicator matrix: docs/indicator_support_matrix.rst
• Optional Performance Boost: Install numba for 6–230× speedups on hot-loop indicators (QQE, RSX, HWMA, SSF, PSAR, Supertrend, MCGD)
• Automatic Versioning: Version management via git tags using setuptools-scm
• Modern Package Management: Full support for both uv and pip
• Production Ready: Stable status with comprehensive test coverage
• Active Development: Regular updates with community contributions

Quick Start

Installation

The library supports both modern uv and traditional pip package managers.

Stable Release

Using uv (recommended - faster):

uv pip install pandas-ta-classic

Using pip:

pip install pandas-ta-classic

Latest Version

Using uv:

uv pip install git+https://github.com/xgboosted/pandas-ta-classic

Using pip:

pip install -U git+https://github.com/xgboosted/pandas-ta-classic

Development Installation

Using uv:

# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install with all dependencies
uv pip install -e ".[all]"

# Or install specific dependency groups:
uv pip install -e ".[dev]" # Development tools
uv pip install -e ".[optional]" # Optional features like TA-Lib

Using pip:

# Clone the repository
git clone https://github.com/xgboosted/pandas-ta-classic.git
cd pandas-ta-classic

# Install with all dependencies
pip install -e ".[all]"

# Or install specific dependency groups:
pip install -e ".[dev]" # Development tools
pip install -e ".[optional]" # Optional features like TA-Lib

Basic Usage

import pandas as pd
import pandas_ta_classic as ta

# Load your data
df = pd.read_csv("path/to/symbol.csv")
# OR if you have yfinance installed
df = df.ta.ticker("aapl")

# Calculate indicators
df.ta.sma(length=20, append=True) # Simple Moving Average
df.ta.rsi(append=True) # Relative Strength Index 
df.ta.macd(append=True) # MACD
df.ta.bbands(append=True) # Bollinger Bands

# Or run a strategy with multiple indicators
df.ta.strategy("CommonStrategy") # Runs commonly used indicators

Features

• 180 Technical Indicators & Utilities across 9 categories (Candles, Cycles, Momentum, Overlap, Trend, Volume, etc.)
• 62 Native Candlestick Patterns — all patterns natively implemented, no TA-Lib required
• 240 Total Indicators & Patterns - the most comprehensive Python TA library
• Dynamic Category Discovery - automatically detects all available indicators from the filesystem
• Optional Numba Acceleration - 6–230× speedups via pip install pandas-ta-classic[performance]
• Strategy System with multiprocessing support for bulk indicator processing
• Pandas DataFrame Extension for seamless integration (df.ta.indicator())
• TA Lib Integration - automatically uses TA Lib versions when available
• Vectorbt Integration - compatible with popular backtesting framework
• Custom Indicators - easily create and chain your own indicators

Documentation

Complete documentation is available at: https://xgboosted.github.io/pandas-ta-classic/

Learning Resources

Start Here:

• Quickstart Guide - Get up and running in minutes
• Tutorials - Step-by-step guides for common workflows
• Examples - Jupyter notebooks with real examples

Reference Documentation:

• Usage Guide - Programming conventions and basic usage
• Strategy System - Multiprocessing and bulk indicator processing
• Indicators Reference - Complete list of all 164 indicators & 62 CDL patterns
• DataFrame API - Properties and methods reference
• Performance Metrics - Backtesting and performance analysis

Python Version Support

Pandas TA Classic follows a rolling support policy for the latest stable Python version plus 4 preceding minor versions.

Note: Python version support is dynamically managed via CI/CD workflows. When new Python versions are released, the library automatically updates to support the latest 5 minor versions. Check the CI workflow LATEST_PYTHON_VERSION for the current configuration.

TA-Lib is fully optional. Its effect depends on which indicators you use:

Area	Behaviour without TA-Lib	Behaviour with TA-Lib
CDL patterns (62)	Native Python — always used	Still native — TA-Lib not used for patterns
Core indicators (34)	Native Python	TA-Lib version used by default; pass talib=False to force native

# CDL patterns — always native, no TA-Lib needed
df.ta.cdl_pattern(name="all") # run all 62 patterns
df.ta.cdl_pattern(name="engulfing") # individual pattern

# Core indicators — TA-Lib used if installed (default)
df.ta.ema(length=20) # TA-Lib EMA when available
df.ta.ema(length=20, talib=False) # force native implementation

Installing TA-Lib (optional):

# uv
uv pip install TA-Lib
# pip
pip install TA-Lib

Performance boost: Install numba for 6–230× speedups on computation-heavy indicators:

• Using uv: uv pip install pandas-ta-classic[performance]
• Using pip: pip install pandas-ta-classic[performance]

Contributing

We welcome contributions! Please see our contributing guidelines and issues page.

Reporting Issues

• Check existing issues first
• Provide reproducible code examples
• Include relevant error messages and data samples

Changelog

For detailed information about changes, improvements, and new features, please see the CHANGELOG.md file.

Sources

Original TA-LIB | TradingView | Sierra Chart | MQL5 | FM Labs | Pro Real Code | User 42

Support

If you find this library helpful, please consider:

License

This project is licensed under the MIT License - see the LICENSE file for details.