This tutorial is aimed at helping anyone with Quantopian, so that means you! If you're lost/confused/curious about something, ask questions!
The tutorials come in both video and text-based versions. The video tutorials are posted on the YouTube channel first, then I write the written-versions to release on PythonProgramming.net, so you should be able to consume the content in whatever method you prefer.
Source code is posted along with the written-versions of the code, and they eventually wind up on the PythonProgramming Github as well.
The series can be found here: Finance with Python, Zipline, and Quantopian Tutorials
The initial batch of this series is fully released, which covers many of the basics of marrying Python, Quantopian, and general Algorithmic trading. I will add more strategies in time, based on requests and suggestions.
Table of Contents:
- Introduction to Quantopian and Zipline
- Basics of a Strategy
- Back-testing a Strategy
- Accessing Fundamental Data via Quantopian
- Back-Testing our strategy
- Using Schedule Function to rebalance portfolio
- Shorting Companies
- Quantopian's Fetcher (fetch_csv)
- Trading Logic with fetched csv signals
- Shorting based on sentiment signals from fetch_csv
- Paper Trading a Strategy
- Understanding the goals of Funds
- Building a Machine Learning framework
- Creating Machine Learning Classifier Feature Sets
- Creating our Machine Learning Classifiers
- Testing our Machine Learning Strategy
- Understanding Leverage
- Pipeline API introduction
Questions? Comments? Suggestions? Requests? Let me know!