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 26 indicators and 49 features:

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)

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)

Others

• Daily Return (DR)
• 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

> pip install -r requirements.txt

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

Credits:

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

Please, let us know about any comment or feedback.