Pandas TA Classic - Technical Analysis Library

!Example Chart

Pandas TA Classic is an easy-to-use library that leverages the Pandas package with 224 indicators and utility functions and 62 native candlestick patterns (284 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:

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

Key Features

• 284 Unique Indicators & Patterns: 224 Category indicators + 62 CDL patterns via cdl_pattern() = 284 unique (doji and inside appear in both counts; all CDL patterns use native Python — no TA-Lib required)
• All-Native Candlestick Patterns: All 62 CDL patterns have native Python implementations — TA-Lib is never used for CDL patterns
• Optional TA-Lib Acceleration: Core indicators (EMA, SMA, RSI, MACD, OBV, ATR, etc.) use native implementations by default; pass talib=True to use TA-Lib
• Compatibility Scope Is Explicit: Not every TA-Lib/tulipy function has a pandas-ta-classic counterpart. See the full per‑indicator matrix for current coverage: 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 including property-based testing (Hypothesis)
• 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 core dependencies (excludes the platform-fragile
data/backtest extras — install those explicitly if needed)
uv pip install -e ".[all]"
Or install specific dependency groups:
uv pip install -e ".[dev]" # Development tools
uv pip install -e ".[optional]" # Optional runtime features
uv pip install -e ".[oracle]" # Oracle parity lib: TA-Lib
uv pip install -e ".[data]" # Data sources: yfinance, alpha-vantage
uv pip install -e ".[backtest]" # Backtesting: backtesting, vectorbt, backtrader

Using pip:

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

Install with all core dependencies (excludes the platform-fragile
data/backtest extras — install those explicitly if needed)
pip install -e ".[all]"
Or install specific dependency groups:
pip install -e ".[dev]" # Development tools
pip install -e ".[optional]" # Optional runtime features
pip install -e ".[oracle]" # Oracle parity lib: TA-Lib
pip install -e ".[data]" # Data sources: yfinance, alpha-vantage
pip install -e ".[backtest]" # Backtesting: backtesting, vectorbt, backtrader

Basic Usage

import pandas as pd
import pandas_ta_classic as ta
Load your data
df = pd.read_csv("path/to/symbol.csv")
OR fetch OHLCV with yfinance directly (df.ta.ticker() is deprecated —
see examples/fetch_market_data.py):
import yfinance as yf
df = yf.download("AAPL", period="1y")
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
Fluent API chaining (v0.6+)
df.ta.chain().sma(20).ta.rsi(14).ta.macd().ta.bbands(20)
Or run a strategy with multiple indicators
df.ta.strategy("CommonStrategy") # Runs commonly used indicators

Features

• 224 Technical Indicators & Utilities across 10 categories (Candles, Cycles, Math, Momentum, Overlap, Trend, Volume, etc.)
• 62 Native Candlestick Patterns — all patterns natively implemented, no TA-Lib required
• 284 Unique Indicators & Patterns - 224 category indicators plus 62 CDL patterns via cdl_pattern()
• 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
• Fluent API Chaining: `df.ta.chain().sma(20).ta.rsi(14).ta.macd().ta.bbands(20) — chain multiple indicators in a single expression
• Pandas DataFrame Extension for seamless integration (df.ta.indicator())
• TA-Lib Integration (dual-role) - (1) acceleration backend: core indicators use native implementations by default; pass talib=True to use TA-Lib's C implementation. (2) oracle: test_oracle_talib.py verifies parity against TA-Lib
• tulipy Integration (frozen oracle only) - test_oracle_tulipy.py verifies native output against a committed golden snapshot of tulipy's output (tests/fixtures/tulipy_oracle.json); tulipy itself is no longer installed at test time, only to regenerate the snapshot; never used as a computation backend
• Backtesting.py Integration — bridge function and runnable SMA crossover example in examples/backtesting_py_strategy.py
• backtrader Integration — precompute-then-feed pattern with dynamic PandasData subclass; runnable example in examples/backtrader_strategy.py
• Vectorbt Integration - compatible with popular backtesting framework
• Custom Indicators - easily create and chain your own indicators

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.

| Library | Role | Effect when installed | |---------|------|-----------------------| | TA-Lib | Acceleration backend + live oracle | Core indicators — native by default, opt-in via talib=True; also used live in test_oracle_talib.py for parity checks | | tulipy | Frozen oracle only | Not a computation backend and not installed at test time; test_oracle_tulipy.py compares against a committed golden snapshot of tulipy's output. tulipy is only needed to regenerate that snapshot (CPython <3.12) |

| Area | Behaviour without TA-Lib | Behaviour with TA-Lib | |------|--------------------------|----------------------| | CDL patterns (62) | Native Python — always used | Still native — TA-Lib never used for patterns | | Core indicators (59) | Native Python (default) | TA-Lib available via talib=True |

# 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 — native by default
df.ta.ema(length=20) # native implementation
df.ta.ema(length=20, talib=True) # use TA-Lib

# uv
uv pip install pandas-ta-classic[oracle] # installs TA-Lib (the live oracle)
uv pip install TA-Lib # TA-Lib only (also enables acceleration backend)
pip
pip install pandas-ta-classic[oracle] # installs TA-Lib (the live oracle)
pip install TA-Lib # TA-Lib only (also enables acceleration backend)
tulipy is only needed to regenerate the frozen oracle snapshot (CPython <3.12):
pip install tulipy && python tests/fixtures/generate_tulipy_oracle.py

Note: test_oracle_talib.py skips automatically (@unittest.skipUnless) when TA-Lib is not installed. test_oracle_tulipy.py runs on every Python version against the committed tulipy_oracle.json snapshot (skips only if that fixture is missing) — it does not require tulipy installed. Neither is required for normal use. Installing TA-Lib additionally enables C-library acceleration for core indicators via talib=True.

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:

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