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xgboosted 821038e1e1 docs: refresh indicator counts and modernize TA-Lib install docs

Update all stale indicator counts (193→224, 253→284) across README,
index, docs/index.rst, quickstart.md, and indicators.rst. Remove
unverifiable hardcoded numbers (59 core indicators, ~45 indicators,
89/98/66 counterparts) in favor of generic descriptions or links to
the live matrix. Add tulipy to optional deps list. Pin ta-lib>=0.6.8
in pyproject.toml and document binary wheel availability in install
docs — pre-0.6.5 lacks wheels and requires manual C library setup.
Pin tulipy>=0.4.0. Remove duplicate Compatibility Matrix section.
Replace stale Windows wheel workaround with single cross-platform
install command.

2026-06-28 21:28:05 +10:00

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layout, title

layout	title
default	Pandas TA Classic - Technical Analysis Library

Pandas TA Classic

Pandas Technical Analysis (Pandas TA Classic) is an easy to use library that leverages the Pandas package with 224 indicators and utility functions plus 62 native candlestick patterns (284 total unique — no TA-Lib required). This is the community maintained version of the popular pandas-ta library.

Features

224 indicators and utility functions across 10 categories
62 native candlestick patterns — all natively implemented, no TA-Lib required
284 total unique indicators and patterns
Dynamic category discovery - automatically detects available indicators
Optional TA-Lib Acceleration: Core indicators auto-use TA-Lib when installed; pass talib=False to force native
Optional Oracle Libraries: TA-Lib (acceleration backend + oracle) and tulipy (oracle only) are optional and skip gracefully when not installed
Supports both standalone and DataFrame extension usage
Fluent API chaining: df.ta.chain().sma(20).ta.rsi(14).ta.macd()
Multiprocessing support via Strategy method
Custom strategies and indicator chaining
Performance metrics (BETA)

Quick Installation

Supports both modern uv and traditional pip:

Using uv (recommended):

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

Quick Start

import pandas as pd
import pandas_ta_classic as ta

# Load your data
df = pd.read_csv("path/to/symbol.csv")

# Calculate indicators
df.ta.sma(length=20, append=True)  # Simple Moving Average
df.ta.rsi(length=14, append=True)  # RSI
df.ta.macd(append=True)            # MACD

# Fluent API chaining
df.ta.chain().sma(20).ta.rsi(14).ta.macd().ta.bbands(20)

# Or use strategies for bulk processing
df.ta.strategy("CommonStrategy")

Documentation

For detailed documentation, examples, and the complete list of indicators, please visit our GitHub repository.

Categories of Indicators

Candles (67): Candle-focused indicators, including 62 native CDL patterns (no TA-Lib required)
Momentum (53): RSI, MACD, Stochastic, etc.
Overlap (46): Moving averages, Bollinger Bands, etc.
Trend (26): ADX, Aroon, Parabolic SAR, etc.
Volume (20): OBV, Money Flow, etc.
Volatility (18): ATR, Bollinger Bands, Chandelier Exit, etc.
Statistics (14): Z-Score, Standard Deviation, etc.
Performance (3): Returns, Drawdown analysis
Cycles (8): Hilbert Transform and cycle detection
Math (28): Element-wise math operators and transforms

Support

License

This project is licensed under the MIT License.

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