Technical Analysis Library in Python

Technical Analysis (TA) is an easy to use library that is built upon Python's Pandas library with more than 60 Indicators. These indicators are comminly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close, volume, et al. Many commonly used indicators are included, such as: Moving Average Convergence Divergence (MACD), Hull Exponential Moving Average (HMA), Bollinger Bands (BBANDS), On-Balance Volume (OBV), Aroon Oscillator (AROON) and more.

This version contains both the orignal code branch as well as a newly refactored branch with the option to use Pandas DataFrame Extension mode. All the indicators return a named Series or a DataFrame in uppercase underscore parameter format. For example, MACD(fast=12, slow=26, signal=9) will return a DataFrame with columns: ['MACD_12_26_9', 'MACDH_12_26_9', 'MACDS_12_26_9'].

New Changes

• At 70+ indicators.
• Abbreviated Indicator names as listed below.
• Extended Pandas DataFrame as 'ta'. See examples below.
• Parameter names are more consistent.
• Former indicators still exist and are renamed with '_depreciated' append to it's name. For example, 'average_true_range' is now 'average_true_range_depreciated'.
• Refactoring indicators into categories similar to TA-lib.

What is a Pandas DataFrame Extension?

A Pandas DataFrame Extension, extends a DataFrame allowing one to add more functionality and features to Pandas to suit your needs. As such, it is now easier to run Technical Analysis on existing Financial Time Series without leaving the current DataFrame. This extension by default returns the Indicator result or, inclusively, it can append the result to the existing DataFrame by including the parameter 'append=True' in the method call. See examples below.

Getting Started and Examples

Quick Start using the DataFrame Extension

import pandas as pd
import pandas_ta as ta

# Load data
df = pd.read_csv('symbol.csv', sep=',')

# 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

Module and Indicator Help

import pandas as pd
import pandas_ta as ta

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

# List of all indicators
pd.DataFrame().ta.indicators()

# Help about the log_return indicator
help(ta.log_return)

# Help about the log_return indicator as a DataFrame Extension
help(pd.DataFrame().ta.log_return)

Technical Analysis Indicators (by Category)

Performance (2)

Use parameter: cumulative=True for cumulative results.

• Log Return: log_return
• Percent Return: percent_return

Percent Return (Cumulative) with Simple Moving Average (SMA)

Inspiration

Inspired by Bukosabino: https://github.com/bukosabino/ta

Please leave any comments, feedback, or suggestions.