All episodes of Chat With Traders x Quantopian mini-series are available here.

Throughout this series, which has been a window into the workflow of professional quant trading firms, we’ve encouraged you to submit questions and requests for further clarification. So, in this episode, being the final installment, Delaney answers as many of these questions as possible (within 80-minutes).

Sponsored by DataCamp:

• Once you commit to learning how to program—specifically for data science and quant research, your next best step: Visit DataCamp.com to start any course, free!

Topics of discussion:

• Resource recommendations and thoughts on programming.
• Considerations for constructing factor models and using classic models.
• Machine learning and it’s potential impact on traders and markets.
• Measuring the goodness of a strategy and live trading expectations.
• Future updates for the Quantopian platform.

Links and resources mentioned:

• Frank J. Fabozzi [Amazon]
• Dessislava A. Pachamanova [Amazon]
• Quantitative Investment Analysis [Amazon]
• Big data: The next frontier for innovation… [McKinsey]
• Pyfolio – Portfolio and risk analytics in Python [Github]
• Machine Learning on Quantopian [Quantopian forum]
• Backtesting ‘Driven to Distraction’ by Rob Reider [Quantopian forum]
• How Accurate is Our Slippage Model [Quantopian post]
• Position Concentration Risk [Quantopian lecture]
• How to Get an Allocation [YouTube]

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• For a quick and easy way to support this free podcast, please write an honest review in iTunes. It’ll take you two minutes, and it helps massively—thank you.