Metadata-Version: 2.1
Name: qtalib
Version: 0.0.1
Summary: QTALIB: Quantitative Technical Analysis Library
Home-page: UNKNOWN
Author: josephchen
Author-email: josephchenhk@gmail.com
License: JXW
Description: # QTALIB: Quantitative Technical Analysis Library
        
        <p align="center">
            <img src ="https://img.shields.io/badge/version-0.0.1-blueviolet.svg"/>
            <img src ="https://img.shields.io/badge/platform-windows|linux|macos-yellow.svg"/>
            <img src ="https://img.shields.io/badge/python-3.8-blue.svg" />
            <img src ="https://img.shields.io/github/workflow/status/vnpy/vnpy/Python%20application/master"/>
            <img src ="https://img.shields.io/badge/license-JXW-orange"/>
        </p>
        
        **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:
        
        ```python
        # 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
        
        ```python
        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](https://pypi.org/project/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.
Keywords: Quantitative Trading,Technical Analysis,QTaLib
Platform: any
Description-Content-Type: text/markdown
