I posted a little while ago about a Coursera class I took which covered financial analysis done with Python. The course was called Computational Investing, Part I. I find the topic interesting so I figured I would highlight what I enjoyed the most in a short series of posts.
The course, offered by Tucker Balch, an associate professor at Georgia Tech, covered various topics of portfolio management including several drawn from: Active Portfolio Management: A Quantitative Approach for Producing Superior Returns and Controlling Risk by Richard Grinold, Ronald Kahn.
Active Portfolio Management, by Grinold and Khan
We can breakdown the course in two distinct portions:
- Lectures and theory :: The lectures covered a number of topics relating to instruments and portfolio valuations like:
- Market mechanics
- What is a company really worth
- Capital Asset Pricing Model (CAPM)
- Risk and Sharpe ratio
- and more.
- Practical work :: On the practical side, several homework was given where we explored computational techniques using Python, Pandas and QSTK:
- Intro to Python/Pandas
- Manipulating data with Numpy
- Manipulating market data with QSTK
- and more.
The lectures were well adapted for a beginner audience. Someone who understands the concepts could easily skip or fast forward through some of the lectures. I would sill consider the course useful as long as you are getting what you need from the practical homework.
Some students clearly did not have enough background with scripting and coding and struggled with the assignments. Yet they still benefited from the lectures and know more now about market mechanics, portfolios and risk.
Overall, I was very satisfied with the material presented in the course. Not perfect in any way but easy to adapt depending on your current situation and skill level. The lectures were interesting and provided some background information and more than enough leads for the curious mind to follow and research in depth. One of the hardest thing to do when learning something new is motivate yourself to write useless code for the sole purpose of learning and practicing. This class provided the motivation required since the homework assignments forced me to code on deadline.
It is following the basic topics of the course that I embarked on my mission to dig deeper into Python, Numpy, QSTK and analysis of financial data using these tools.
In the next post, I will explore QSTK’s basic functionality.
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Next on topic (this is work in progress, most posts will show up in the weeks to come):