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 31 indicators:

Volume

Accumulation/Distribution Index (ADI)
On-Balance Volume (OBV)
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)
Kaufman's Adaptive Moving Average (KAMA)

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

How to use (python 3.7)

$ 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 for developers

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

Based on:

TODO:

add more technical analysis features.
use Dask library to parallelize the implementation
use Dash library to visualize features
automated tests for indicators.
class based style code

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, Backtrader, Zipline or Catalyst. Don't hesitate to contact me if you need something related with this library, Python, Technical Analysis, AlgoTrading, Machine Learning, etc.

pegahcarter/ta