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
Over 80 indicators.
Updated Example Jupyter Notebook under the examples directory.
Abbreviated Indicator names as listed below.
Extended Pandas DataFrame as 'ta'. See examples below.
Parameter names are more consistent.
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.
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', extensionhelp(pd.DataFrame().ta)
# List of all indicators
pd.DataFrame().ta.indicators()
# Help about the log_return indicatorhelp(ta.log_return)
# Help about the log_return indicator as a DataFrame Extensionhelp(pd.DataFrame().ta.log_return)
Technical Analysis Indicators (by Category)
Momentum (17)
Awesome Oscillator: ao
Absolute Price Oscillator: apo
Balance of Power: bop
Commodity Channel Index: cci
Chande Momentum Oscillator: cmo
Coppock Curve: coppock
KST Oscillator: kst
Moving Average Convergence Divergence: macd
Momentum: mom
Percentage Price Oscillator: ppo
Rate of Change: roc
Relative Strength Index: rsi
Stochastic Oscillator: stoch
Trix: trix
True strength index: tsi
Ultimate Oscillator: uo
Williams %R: willr
Moving Average Convergence Divergence (MACD)
Overlap (21)
Double Exponential Moving Average: dema
Exponential Moving Average: ema
Fibonacci's Weighted Moving Average: fwma
High-Low Average: hl2
High-Low-Close Average: hlc3
Commonly known as 'Typical Price' in Technical Analysis literature
Hull Exponential Moving Average: hma
Ichimoku Kinkō Hyō: ichimoku
Use: help(ta.ichimoku). Returns two DataFrames.
Linear Regression: linreg
Midpoint: midpoint
Midprice: midprice
Open-High-Low-Close Average: ohlc4
Pascal's Weighted Moving Average: pwma
William's Moving Average: rma
Simple Moving Average: sma
Symmetric Weighted Moving Average: swma
T3 Moving Average: t3
Triple Exponential Moving Average: tema
Triangular Moving Average: trima
Volume Weighted Average Price: vwap
Volume Weighted Moving Average: vwma
Weighted Moving Average: wma
Zero Lag Moving Average: zlma
Simple Moving Averages (SMA) and Bollinger Bands (BBANDS)
Performance (3)
Use parameter: cumulative=True for cumulative results.
Log Return: log_return
Percent Return: percent_return
Trend Return: trend_return
Percent Return (Cumulative) with Simple Moving Average (SMA)
