Technical Analysis Library in Python

It is a Technical Analysis library useful to do feature engineering from financial time series datasets (Open, Close, High, Low, Volume). It is built on Pandas and Numpy.

The library has implemented 42 indicators:


  • Money Flow Index (MFI)

  • 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)

  • Volume Weighted Average Price (VWAP)


  • Average True Range (ATR)

  • Bollinger Bands (BB)

  • Keltner Channel (KC)

  • Donchian Channel (DC)

  • Ulcer Index (UI)


  • Simple Moving Average (SMA)

  • Exponential Moving Average (EMA)

  • Weighted Moving Average (WMA)

  • 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)

  • Parabolic Stop And Reverse (Parabolic SAR)

  • Schaff Trend Cycle (STC)


  • Relative Strength Index (RSI)

  • Stochastic RSI (SRSI)

  • True strength index (TSI)

  • Ultimate Oscillator (UO)

  • Stochastic Oscillator (SR)

  • Williams %R (WR)

  • Awesome Oscillator (AO)

  • Kaufman’s Adaptive Moving Average (KAMA)

  • Rate of Change (ROC)

  • Percentage Price Oscillator (PPO)

  • Percentage Volume Oscillator (PVO)


  • Daily Return (DR)

  • Daily Log Return (DLR)

  • Cumulative Return (CR)

How to use (Python 3)

$ pip install --upgrade 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 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 all ta features
df = add_all_ta_features(
    df, open="Open", high="High", low="Low", close="Close", volume="Volume_BTC")

Example adding 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()

# Add Width Size Bollinger Bands
df['bb_bbw'] = indicator_bb.bollinger_wband()

# Add Percentage Bollinger Bands
df['bb_bbp'] = indicator_bb.bollinger_pband()

Deploy and develop (for developers)

$ git clone
$ cd ta
$ pip install -r requirements-play.txt
$ make test