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'].

Quick Start using the DataFrame Extension

import pandas as pd
import ta as ta

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

# Process and append your indicators to the 'df'
df.ta.ema(length=8, append=True)
df.ta.ema(length=21, append=True)
df.ta.sma(length=50, append=True)
df.ta.sma(length=200, append=True)
df.ta.kc(append=True)

df.ta.macd(fast=8, slow=21, signal=9, append=True)
df.ta.rsi(length=14, append=True)

df.ta.obv(append=True)
df.ta.log_return(cumulative=True, append=True)

# New Columns with results
df.columns

# Take a peek
df.tail()

# vv Continue Post Processing vv

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.

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: copc
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 (19)

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.
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
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

Simple Moving Averages (SMA) and Bollinger Bands (BBANDS)

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)

Statistics (9)

Kurtosis: kurtosis
Mean Absolute Deviation: mad
Mean: mean
- Alias of sma
Median: median
Quantile: quantile
Skew: skew
Standard Deviation: stdev
Variance: variance
Z Score: zscore

Z Score

Trend (6)

Average Directional Movement Index: adx
Aroon Oscillator: aroon
Decreasing: decreasing
Detrended Price Oscillator: dpo
Increasing: increasing
Vortex Indicator: vortex

Average Directional Movement Index (ADX)

Volatility (8)

Acceleration Bands: accbands
Average True Range: atr
Bollinger Bands: bbands
Donchian Channel: donchain
Keltner Channel: kc
Mass Index: massi
Normalized Average True Range: natr
True Range: true_range

Average True Range (ATR)

Volume (10)

Accumulation/Distribution Index: ad
Accumulation/Distribution Oscillator: adosc
Chaikin Money Flow: cmf
Elder's Force Index: efi
Ease of Movement: eom
Money Flow Index: mfi
Negative Volume Index: nvi
On-Balance Volume: obv
Price-Volume: pvol
Price Volume Trend: pvt

On-Balance Volume (OBV)

Documentation

https://technical-analysis-library-in-python.readthedocs.io/en/latest/

Motivation

Python 3 Installation

$ virtualenv -p python3 virtualenvironment
$ source virtualenvironment/bin/activate
$ pip install ta

To use this library you will need a financial time series dataset including “Open”, “High”, “Low”, “Close” and “Volume” columns. A “Timestamp” or "Date" column is not required, but is typically included anyhow. It is preferred that the original columns are lowercase, however it will do it's best to find the intended column. The majority of Technical Analysis Indicators use price or volume.

You should clean or fill NaN values in your dataset before adding technical analysis indicators.

You can get code examples in examples_to_use folder.

You can visualize the features in this notebook.

Getting Started and Examples

Module and Indicator Help

import pandas as pd
from ta import ta

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

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

# Help about the OBV indicator
help(ta.obv)

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

Calling an Indicator

# Load data
spy = pd.read_csv('SPY.csv', sep=',')

# Typical Call
spy_ema50 = ta.ema(spy['close'], length=50)

# Extended Call
spy_ema50 = spy.ta.ema(length=50)

# Extended Call with appending to the 'spy' DataFrame and returning the result
# By default, appending is False
spy_ema50 = spy.ta.ema(length=50, append=True)
# Notice as 'spy_ema50' has been appended to 'spy' DataFrame
spy.columns

Additional ways of calling an Indicator

# You can also use the 'kind' parameter.  Below are equivalent calls.
spy_ema50 = spy.ta(kind='ema', length=50)
spy_ema50 = spy.ta(kind='Ema', length=50)

# Using a non-default series as an input.
# For example instead of having 'ema' using the default 'close' column, have it use the 'open' column instead
spy_ema50_open = spy.ta.ema(close='open', length=50)

Legacy Examples

Example adding all features

import pandas as pd
from ta import *

# Load data
df = pd.read_csv('your-file.csv', sep=',')

# Clean NaN values
df = utils.dropna(df)

# Add ta features filling NaN values
df = add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC", fillna=True)

Example adding individual features

import pandas as pd
from ta import *

# Load datas
df = pd.read_csv('your-file.csv', sep=',')

# Clean NaN values
df = utils.dropna(df)

# Add bollinger band high indicator filling NaN values
df['bb_high_indicator'] = bollinger_hband_indicator_depreciated(df["Close"], n=20, ndev=2, fillna=True)

# Add bollinger band low indicator filling NaN values
df['bb_low_indicator'] = bollinger_lband_indicator_depreciated(df["Close"], n=20, ndev=2, fillna=True)

Developer Edition

$ git clone https://github.com/bukosabino/ta.git
$ cd ta
$ pip install -r requirements.txt
$ cd dev
$ python bollinger_band_features_example.py

Inspiration:

TODO:

Add more technical analysis features.
Incorporate dask library to parallelize

Credits:

Developed by Bukosabino at Lecrin Technologies - http://lecrintech.com

Expanded and Extended (via Pandas) by Kevin Johnson - https://github.com/twopirllc

Please leave any comments, feedback, or suggestions.

gp-slick-coder / ta Goto Github PK

ta's Introduction