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Summary
Because of its easy learning curve and broad extensibility Python has found its way into the realm of algorithmic trading at Quantopian. In this episode we spoke with Scott Sanderson about what algorithmic trading is, how it differs from high frequency trading, and how they leverage Python for empowering everyone to try their hand at it.
Brief Introduction
- Hello and welcome to Podcast.__init__, the podcast about Python and the people who make it great.
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- We are recording today on December 16th, 2015 and your hosts as usual are Tobias Macey and Chris Patti
- Today we are interviewing Scott Sanderson on Algorithmic Trading
Interview with Scott Sanderson
- Introductions
- How did you get introduced to Python? – Chris
- Can you explain what algorithmic trading is and how it differs from high frequency trading? – Tobias
- What kinds of algorithms and libraries are commonly leveraged for algorithmic trading? – Tobias
- Quantopian aims to make algorithmic trading accessible to everyone. What do people need to know in order to get started? Is it necessary to have a background in mathematics or data analysis? – Tobias
- Does the Quantopian platform build in any safe guards to prevent user’s algorithms from spiraling out of control and creating or contributing to a market crash? – Chris
- How is Python used within Quantopian and when do you leverage other languages? – Tobias
- What Pypi packages does Quantopian leverage in its platform? – Chris
- How do the financial returns compare between algorithmic vs human trading on the stock market? – Tobias
- Can you speak about any trends you see in the trading algorithms people are creating for the Quantopian platform? – Chris
Picks
- Tobias
- Chris
- Scott
Keep In Touch
Links
- QGrid
- SlickGrid
- Jupyter Hub
- Light Table
- CodeMirror
- Cython
- PyData NYC Talk by Scott
- Blaze
- Dask
- Theano
- TensorFlow
- Zipline
- Pyfolio
- PGContents
- SQLAlchemy
- Gevent
- quantopian.com/lectures
The intro and outro music is from Requiem for a Fish The Freak Fandango Orchestra / CC BY-SA
Activity