# 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 2022-12-18**
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:
```python
# 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
```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.
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"description": "# QTALIB: Quantitative Technical Analysis Library\n\n<p align=\"center\">\n <img src =\"https://img.shields.io/badge/version-0.0.1-blueviolet.svg\"/>\n <img src =\"https://img.shields.io/badge/platform-windows|linux|macos-yellow.svg\"/>\n <img src =\"https://img.shields.io/badge/python-3.8-blue.svg\" />\n <img src =\"https://img.shields.io/github/workflow/status/vnpy/vnpy/Python%20application/master\"/>\n <img src =\"https://img.shields.io/badge/license-JXW-orange\"/>\n</p>\n\n**Latest update on 2022-12-18**\n\nTechnical indicators implemented in Cython/C. This is supposed to be a\nfaster technical analysis library with perfect integration to Python.\n\n## Available technical indicators\n\n* Simple Moving Average (SMA)\n\n* Exponential Moving Average (EMA)\n\n* Moving Average Convergence Divergence (MACD) \n\n* Moving Standard Deviation function (MSTD) \n\n* Relative Strength Index (RSI)\n\n* True Range (TR)\n\n* Absolute True Range (ATR)\n\n* (Parabolic) Stop and Reverse (SAR)\n\n* Super Trend (ST)\n\n* Time Segmented Volume (TSV)\n\n* On Balance Volume (OBV)\n\n* Cyclicality (CLC)\n\n## Installation\n\nYou may run the folllowing command to install QTalib immediately:\n\n```python\n# Virtual environment is recommended (python 3.8 or above is supported)\n>> conda create -n qtalib python=3.8\n>> conda activate qtalib\n\n# (Recommend) Install latest version from github \n>> pip install git+https://github.com/josephchenhk/qtalib@main\n\n# Alternatively, install stable version from pip (currently version 0.0.2)\n>> pip install qtalib\n```\n\n## Usage\n\n```python\nimport numpy as np\nimport qtalib.indicators as ta\n\nvalues = np.array([12.0, 14.0, 64.0, 32.0, 53.0])\n\n# Simple Moving Average\n# [30. 36.66666667 49.66666667]\nprint(ta.SMA(values, 3))\n\n# Exponential Moving Average\n# [12. 13.33333333 42.28571429 36.8 45.16129032]\nprint(ta.EMA(values, 3))\n```\n\n## Contributing\n* Fork it (https://github.com/josephchenhk/qtalib/fork)\n* Study how it's implemented.\n* Create your feature branch (git checkout -b my-new-feature).\n* Use [flake8](https://pypi.org/project/flake8/) to ensure your code format\ncomplies with PEP8.\n* Commit your changes (git commit -am 'Add some feature').\n* Push to the branch (git push origin my-new-feature).\n* Create a new Pull Request.\n",
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