Just wanted to check in with a book recommendation – Python for Finance by Yves Hilpisch, which covers programming for basic financial modeling and algorithmic trading.
Resources are listed below and kudos to O’Reilly and The Python Quants group (founded by Yves Hilpisch) for providing these great resources for those of us researching this topic!
Python for Finance by Yves Hilpisch – O’Reilly
Book Code Examples – Github
The Python Quants Group
Found a forum post on Quantopian with an aggregate of recent strategies which I found interesting; specifically, the strategies (PEAD) focusing on time periods around earnings news since it is now earnings season.
Many of the strategies are based on academic papers, which are also worth a read for additional background on the topic. Again, kudos to Quantopian and all the great work they are doing to promote algorithmic trading!
I recently started researching algorithmic trading and got interested in the Quantopian platform, which includes a full development research, learning and development environment.
I have been using the platform for market research with the following tools:
1. Lectures & Tutorials – Lessons are presented with iPython notebooks
2. Extensive Data API’s – Utilized for backtesting
3. Development Environment – Develop and test algorithms
It is an impressive platform, and one that I highly recommend!
Please check back for more updates as I post more news on my progress.
For anyone looking to get up and running on Node.js, I recommend the Heroku tutorial which provides a quick guide on deployment and a good template for future use.
Granted, it does take some basic programming knowledge to get all the steps but otherwise, the tutorial is easy to complete. I especially enjoy the minimalist template with intuitive defaults and great styling out of the box.
Great job, Heroku and please keep up the good work!