Running financial models with code is relatively easy provided that one has some previous programming and math/statistics experience.
That said, I have been working through the examples in the Python for Finance book by Packt Publishing and am sharing examples for pricing covered calls and simulating stock returns as listed below.
Source code of my updated examples are available on my Github repo.
Chapter 9, Example 15: Covered Stock Option Call
Chapter 11, Example 12: Simulating Stock Returns with Lognormal Distribution
I used part my summer to study up for my current course (statistical methods) at HES so am working thru the class textbook, OpenIntro Statistics.
The textbook is open-source, which means lots of useful updates/contributions and material is readily available for free. It was designed as a template for instructors to use for teaching their own courses.
That said, the topics has come in handy not only for class but in my study of algorithmic trading and financial markets.
I am spending the summer off between work and school semesters to learn more about algorithmic trading so started working through examples in Python for Finance by Packt Publishing.
The book focuses on financial models and examples are algorithms implementing those models. There are easy instructions for installing Python and the examples run as scripts.
Having programming experience and some knowledge of financial markets, I was able to get up and running so would recommend the book for anyone interested in learning more about this topic.
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
One of the unique and awesome features of Quantopian has been their lecture series which focuses on teaching specific topics on quantitative finance for building algorithms on their platform.
The lectures are updated on a regular basis and developed in collaboration with top-tier universities so the curriculum is on par with classes being taught in academia.
I am working through the lectures and been enjoying them so please keep up the great work, Quantopian!
I have been following the Chat with Traders podcast for ideas and particularly enjoyed their series with Quantopian.
The series gets into the details on alpha factors, portfolio optimization and machine learning which are all core concepts in algorithmic trading.
Kudos to Aaron for this interview and Chat with Traders, please keep up the great work!