QTALIB: Quantitative Technical Analysis Library
Project description
QTALIB: Quantitative Technical Analysis Library
Latest update on 2022-08-10
Available technical indicators
-
Simple Moving Average (SMA)
-
Exponential Moving Average (EMA)
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
# Install stable version from pip (currently version 0.0.1)
>> pip install qtalib
# Alternatively, install latest version from github
>> pip install git+https://github.com/josephchenhk/qtalib@master
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.
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