Pandas TA Classic Documentation
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 unique total; cdl_doji and cdl_inside are counted in both sets). All CDL patterns are natively implemented — no TA-Lib required. This is the community maintained version of the popular pandas-ta library.
Note
The library features dynamic configuration management:
Category Discovery: Indicators are automatically detected from the package structure
Version Management: Python version support (the latest stable plus the prior 4 versions, currently 3.11–3.15) follows
LATEST_PYTHON_VERSIONin the CI workflow, which sets the test matrix and, at each release, the PyPI classifiers andrequires-pythonBoth ensure the library metadata stays in sync with actual capabilities
Quick Start
Pandas TA Classic is simple to use — import the library and start calculating indicators directly on your DataFrame:
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)
df.ta.rsi(append=True)
df.ta.macd(append=True)
Note
New to Pandas TA Classic? Check out our Quickstart guide for a comprehensive introduction, or explore the DataFrame Properties and Methods guide for detailed programming conventions.
Table of Contents
Getting Started:
- Installation
- Quickstart
- Tutorials
- Table of Contents
- Tutorial 1: Creating a Moving Average Crossover Strategy
- Tutorial 2: Building a Custom Indicator Strategy
- Tutorial 3: Backtesting with Performance Metrics
- Tutorial 4: Integrating with backtesting.py
- Tutorial 5: Integrating with backtrader
- Tutorial 6: Integrating with VectorBT
- Tutorial 7: Multi-Timeframe Analysis
- Tutorial 8: Creating Custom Indicators
- Tutorial 9: Candlestick Pattern Recognition
- Tutorial 10: Integrating with manifoldbt
- Next Steps
- Additional Resources
User Guide:
Additional Resources:
- Testing
- Unit Tests
- Extension API Tests
- Accessor API Tests
- Oracle / Comparison Tests
- Native Indicator Tests
- Regression Tests
- Edge-Case Tests
- API Consistency Map
- Integration / E2E Tests
- Fluent API Tests
- Strategy Tests
- Custom / Plugin Tests
- Property-Based Tests
- JIT Parity Tests
- Utility Tests
- Repository Guards
- Running All Tests
- Fixture Files
- GitHub Repository
- Examples
- Changelog