Technical Analysis Library in Python

It is a Technical Analysis library to financial time series datasets (open, close, high, low, volume). You can use it to do feature engineering from financial datasets. It is builded on Python Pandas library.

The library has implemented 32 indicators:

Volume

• Accumulation/Distribution Index (ADI)
• On-Balance Volume (OBV)
• On-Balance Volume mean (OBV mean)
• Chaikin Money Flow (CMF)
• Force Index (FI)
• Ease of Movement (EoM, EMV)
• Volume-price Trend (VPT)
• Negative Volume Index (NVI)

Volatility

• Average True Range (ATR)
• Bollinger Bands (BB)
• Keltner Channel (KC)
• Donchian Channel (DC)

Trend

• Moving Average Convergence Divergence (MACD)
• Average Directional Movement Index (ADX)
• Vortex Indicator (VI)
• Trix (TRIX)
• Mass Index (MI)
• Commodity Channel Index (CCI)
• Detrended Price Oscillator (DPO)
• KST Oscillator (KST)
• Ichimoku Kinkō Hyō (Ichimoku)

Momentum

• Money Flow Index (MFI)
• Relative Strength Index (RSI)
• True strength index (TSI)
• Ultimate Oscillator (UO)
• Stochastic Oscillator (SR)
• Williams %R (WR)
• Awesome Oscillator (AO)

Others

• Daily Return (DR)
• Daily Log Return (DLR)
• Cumulative Return (CR)

Documentation

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

Motivation to use

• English: https://towardsdatascience.com/technical-analysis-library-to-financial-datasets-with-pandas-python-4b2b390d3543
• Spanish: https://medium.com/datos-y-ciencia/biblioteca-de-an%C3%A1lisis-t%C3%A9cnico-sobre-series-temporales-financieras-para-machine-learning-con-cb28f9427d0

How to use (python 3)

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

To use this library you should have a financial time series dataset including “Timestamp”, “Open”, “High”, “Low”, “Close” and “Volume” columns.

You should clean or fill NaN values in your dataset before add technical analysis features.

You can get code examples in examples_to_use folder.

You can visualize the features in this notebook.

Example adding all 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 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(df["Close"], n=20, ndev=2, fillna=True)

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

Deploy to developers

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

Based on:

• https://en.wikipedia.org/wiki/Technical_analysis
• https://pandas.pydata.org
• https://github.com/FreddieWitherden/ta
• https://github.com/femtotrader/pandas_talib

TODO:

• add more technical analysis features.
• use Dask library to parallelize
• use Dash library to visualize features

Credits:

Developed by Darío López Padial (aka Bukosabino) and other contributors: https://github.com/bukosabino/ta/graphs/contributors

Please, let me know about any comment or feedback.

Also, I am a software freelance focused on Data Science using Python tools such as Pandas, Scikit-Learn, Zipline or Catalyst. Don't hesitate to contact with me if you need something related with this library, Technical Analysis, Algo Trading, Machine Learning, etc.