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| _ \_ _|/ \ / \ | | __ _ ___ _ __(_) |_| |__ _ __ ___
| |_) || | / _ \ _____ / _ \ | |/ _` |/ _ \| '__| | __| '_ \| '_ ` _ \
| _ < | |/ ___ \ |_____| / ___ \| | (_| | (_) | | | | |_| | | | | | | | |
|_| \_\|_/_/ \_\ /_/ \_\_|\__, |\___/|_| |_|\__|_| |_|_| |_| |_|
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Goals
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This is an algorithmic trading platform for professional traders. It providers technical analysis of 200+ popular indicators and trading signals. You can program your trading strategies and backtest them against historical data.
Technology stack
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1. Flask for back-end api
Python is known for its ease of use and best ecosystem in scientific field. It really shines when u use Numpy, Pandas and Pytables for large computational work. I see python a perfect fit in financial world.
2. client-side MVC: Backbone.js and Brunch
Node and client-side MVC framework have become popular over the recent years. so i thought of giving it a try ( which is excellent till now. ). Brunch is used for automatic compiling of coffeescript files and serve them in dev mode.
How to Run:
1. start mongodb
2.
$] cd js && brunch watch
$] python manage.py runserver
$] open http://localhost:3000