Welcome to Technical Analysis Library in Python’s documentation!

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.

Installation (python >= v3.6)

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

Examples

Example adding all features:

import pandas as pd
from ta import add_all_ta_features
from ta.utils import dropna

# Load datas
df = pd.read_csv('ta/tests/data/datas.csv', sep=',')

# Clean NaN values
df = dropna(df)

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

Example adding a particular feature:

import pandas as pd
from ta.utils import dropna
from ta.volatility import BollingerBands


# Load datas
df = pd.read_csv('ta/tests/data/datas.csv', sep=',')

# Clean NaN values
df = dropna(df)

# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)

# Add Bollinger Bands features
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()

# Add Bollinger Band high indicator
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()

# Add Bollinger Band low indicator
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()

Indices and tables