QTALIB: Quantitative Technical Analysis Library

Latest update on 2022-12-18

Technical indicators implemented in Cython/C. This is supposed to be a faster technical analysis library with perfect integration to Python.

Available technical indicators

• Simple Moving Average (SMA)

• Exponential Moving Average (EMA)

• Moving Average Convergence Divergence (MACD)

• Moving Standard Deviation function (MSTD)

• Relative Strength Index (RSI)

• True Range (TR)

• Absolute True Range (ATR)

• (Parabolic) Stop and Reverse (SAR)

• Super Trend (ST)

• Time Segmented Volume (TSV)

• On Balance Volume (OBV)

• Cyclicality (CLC)

Installation

You may run the folllowing command to install QTalib immediately:

# Virtual environment is recommended (python 3.8 or above is supported)
>> conda create -n qtalib python=3.8
>> conda activate qtalib

# (Recommend) Install latest version from github 
>> pip install git+https://github.com/josephchenhk/qtalib@main

# Alternatively, install stable version from pip (currently version 0.0.2)
>> pip install qtalib

Usage

import numpy as np
import qtalib.indicators as ta

values = np.array([12.0, 14.0, 64.0, 32.0, 53.0])

# Simple Moving Average
# [30.         36.66666667 49.66666667]
print(ta.SMA(values, 3))

# Exponential Moving Average
# [12.         13.33333333 42.28571429 36.8        45.16129032]
print(ta.EMA(values, 3))

Contributing

• Fork it (https://github.com/josephchenhk/qtalib/fork)
• Study how it's implemented.
• Create your feature branch (git checkout -b my-new-feature).
• Use flake8 to ensure your code format complies with PEP8.
• Commit your changes (git commit -am 'Add some feature').
• Push to the branch (git push origin my-new-feature).
• Create a new Pull Request.