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 pandas python library.

The library has implemented 25 indicators and 46 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)

Others

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

How to use

> virtualenv -p python3 virtualenvironment
> source virtualenvironment/bin/activate
> pip3 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 Nans values in your dataset before add technical analysis features.

You can get code examples in examples_to_use folder.

If you don't know any feature you can view this notebook.

Note: To execute the notebook you will need install 'matplotlib' and 'jupyter lab'. So:

> pip3 install matplotlib==2.1.2
> pip3 install jupyterlab==0.31.12
> jupyter lab

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 Nans 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 Nans values
df['bb_high_indicator'] = bollinger_hband_indicator(df["Close"], n=20, ndev=2, fillna=True)

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

Deploy to developers

> pip3 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:

• generate online documentation (https://readthedocs.org/ , http://www.sphinx-doc.org/en/master/)
• add ta features
• more documentation

Credits:

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

We are glad to receive any contribution, idea or feedback.