Quantopian on Github: Open-Source Tools

In addition to the powerful platform that Quantopian provides, they have open-sourced some of their tools on Github which means they are able to be cloned for personal use and experimentation.

Their approach to algorithmic trading is very refreshing given the Wall St culture to keep all intellectual property (IP) secret – and rightfully so due to the competitive nature of the markets.

That said, Quantopian is approaching the markets as a crowd-sourced hedge fund and leverages the IP of many to construct profitable algorithmic strategies.

As a result, they are following the culture of many Silicon Valley to open-source their most common tools and social coding to improve upon them.

Chat with Traders #75: Dan Aisen (IEX Stock Exchange)

I enjoyed listening to one of archived Episode #75 with Dan Aisen, who is co-founder of the IEX Stock Exchange.

IEX is a newer stock exchange which introduces features to minimize structural arbitrage tactics used by some High-Frequency Trading (HFT) firms and protect institutional investors.

During the interview, Dan goes into depth about market structure, HFT tactics and dark pool exchanges where IEX started out before being granted status as a stock exchange.

Dan also outlines his work in a detailed Quantopian blog post.

Kudos to Aaron for this interview and Chat with Traders, please keep up the great work!

Python for Finance: Covered Call Options & Simulated Stock Returns

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

OpenIntro Statistics: Summer Study for HES, Statistics E-100

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.

Python for Finance by Packt Publishing

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.

Python for Finance by Yves Hilpisch

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

Quantopian Lecture Series

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!