Pandas TA - A Technical Analysis Library in Python 3

Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), 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.

Note: TA Lib must be installed to use all the Candlestick Patterns. pip install TA-Lib. If TA Lib is not installed, then only the builtin Candlestick Patterns will be available.

Table of contents

• Features
• Installation
  • Stable
  • Latest Version
  • Cutting Edge
• Quick Start
• Help
• Issues and Contributions
• Programming Conventions
  • Standard
  • Pandas TA DataFrame Extension
  • Pandas TA Strategy
• Pandas TA Strategies
  • Types of Strategies
  • Multiprocessing
• DataFrame Properties
• DataFrame Methods
• Indicators by Category
  • Candles
  • Cycles
  • Momentum
  • Overlap
  • Performance
  • Statistics
  • Trend
  • Utility
  • Volatility
  • Volume
• Performance Metrics
• Changes
  • General
  • Breaking Indicators
  • New Indicators
  • Updated Indicators
• Sources
• Support

Features

• Has 130+ indicators and utility functions.
  • BETA Also Pandas TA will run TA Lib's version, this includes TA Lib's 63 Chart Patterns.
• Indicators in Python are tightly correlated with the de facto TA Lib if they share common indicators.
• If TA Lib is also installed, TA Lib computations are enabled by default but can be disabled disabled per indicator by using the argument talib=False.
  • For instance to disable TA Lib calculation for stdev: ta.stdev(df["close"], length=30, talib=False).
• NEW! Include External Custom Indicators independent of the builtin Pandas TA indicators. For more information, see import_dir documentation under /pandas_ta/custom.py.
• Example Jupyter Notebook with vectorbt Portfolio Backtesting with Pandas TA's ta.tsignals method.
• Have the need for speed? By using the DataFrame strategy method, you get multiprocessing for free! Conditions permitting.
• Easily add prefixes or suffixes or both to columns names. Useful for Custom Chained Strategies.
• Example Jupyter Notebooks under the examples directory, including how to create Custom Strategies using the new Strategy Class
• Potential Data Leaks: dpo and ichimoku. See indicator list below for details. Set lookahead=False to disable.

Under Development

Pandas TA checks if the user has some common trading packages installed including but not limited to: TA Lib, Vector BT, YFinance ... Much of which is experimental and likely to break until it stabilizes more.

• If TA Lib installed, existing indicators will eventually get a TA Lib version.
• Easy Downloading of ohlcv data using yfinance. See help(ta.ticker) and help(ta.yf) and examples below.
• Some Common Performance Metrics

Installation

Stable

The pip version is the last stable release. Version: 0.3.14b

$ pip install pandas_ta

Latest Version

Best choice! Version: 0.3.14b

• Includes all fixes and updates between pypi and what is covered in this README.

$ pip install -U git+https://github.com/twopirllc/pandas-ta

Cutting Edge

This is the Development Version which could have bugs and other undesireable side effects. Use at own risk!

$ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development

Quick Start

import pandas as pd
import pandas_ta as ta

df = pd.DataFrame() # Empty DataFrame

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

# VWAP requires the DataFrame index to be a DatetimeIndex.
# Replace "datetime" with the appropriate column from your DataFrame
df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True)

# Calculate Returns and append to the df DataFrame
df.ta.log_return(cumulative=True, append=True)
df.ta.percent_return(cumulative=True, append=True)

# New Columns with results
df.columns

# Take a peek
df.tail()

# vv Continue Post Processing vv

Help

Some indicator arguments have been reordered for consistency. Use help(ta.indicator_name) for more information or make a Pull Request to improve documentation.

import pandas as pd
import pandas_ta as ta

# Create a DataFrame so 'ta' can be used.
df = pd.DataFrame()

# Help about this, 'ta', extension
help(df.ta)

# List of all indicators
df.ta.indicators()

# Help about an indicator such as bbands
help(ta.bbands)

Issues and Contributions

Thanks for using Pandas TA!

• Comments and Feedback

  • Have you read this document?
  • Are you running the latest version?
    • $ pip install -U git+https://github.com/twopirllc/pandas-ta
  • Have you tried the Examples?
    • Did they help?
    • What is missing?
    • Could you help improve them?
  • Did you know you can easily build Custom Strategies with the Strategy Class?
  • Documentation could always be improved. Can you help contribute?

• Bugs, Indicators or Feature Requests

  • First, search the Closed Issues before you Open a new Issue; it may have already been solved.
  • Please be as detailed as possible with reproducible code, links if any, applicable screenshots, errors, logs, and data samples. You will be asked again if you provide nothing.
    • You want a new indicator not currently listed.
    • You want an alternate version of an existing indicator.
    • The indicator does not match another website, library, broker platform, language, et al.
      • Do you have correlation analysis to back your claim?
      • Can you contribute?
  • You will be asked to fill out an Issue even if you email my personally.

Contributors

Thank you for your contributions!

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