The Austrian Quant: My Machine Learning Trading Algorithm Outperformed SP500 For 10 Years

Permanent Portfolio Fund on Quantopian : January 1, 2006 until June 2, 2017



Dutch Golden Age. Origins of Tulip Mania, The first investment bubble. 17th Century.
Source: Jan Claesz Rietschoof [Public domain], via Wikimedia Commons

Broadly speaking, I generally spend most of my time thinking about two things, technology and investing. More specifically, I often ask myself what is something useful I can build with software (or occasionally hardware) and what is something useful which I should invest in. Algorithmic trading is a nice integration of these two schools and I have been spending some time understanding this field. This is an intriguing field and I learnt some interesting things which I decided to share.


Practical Machine Learning


For my summer internship, my project involves using machine learning to help small businesses with funding. I learned a lot about machine learning in the process, so I gave a talk about it to some of my co-workers and shared the slides online:

I also shared the code on my Github. The following is an essay version of the talk.


Before I say anything, I want to show you a video from the 2017 WWDC Apple conference, WWDC is the annual conference which Apple hosts and is one of the most important events in the tech calendar for showcasing the top technology applications that will be used in the near future.

So the machine learning supercut gives some context to how I think society generally views machine learning. On the one hand, it’s a technology which has a lot of potential and will drastically change aspects of our society. Conversely, because it has so much potential people have a tendency to over promise and over-advertise the things which machine learning is capable of doing and often turn it into a marketing gimmick and annoying buzzword. For a high level, non-technical summary of what machine learning is about and what the future of technology in general, I recommend Homo Sapiens by Yuval Noah Harari.

I take a more middle-ground approach and say that you should judge it on the merits of what you can actually build with machine learning, but first, you have to understand what machine learning is.

(Guage audience level) How many of you: has never coded before… used ML in a small side project … Studied ML at a Master’s or Ph.D. Level, written or helped write a paper about ML etc.)I’ve tried to structure my talk in such a way that non-technical people will find it interesting and the more technical, ML-experienced people may some new, interesting concepts.

The reason my talk is called practical machine learning is because I consider myself a very pragmatic, practical person and whenever I learn something, the first thing I ask myself is how can I apply what I’ve learned and put it into practice. Hopefully, after today’s talk, you will hopefully be able to apply what you’ve learned and build actual ML projects. Alright, so let’s get started

What is Machine Learning?

My talk was greatly inspired by 2 tutorials which I did, the website is awesome and the guy who runs it Harrison Kinley is a very good teacher.

  • ML is a software program that can learn from given inputs and give you the desired output, without explicitly teaching it what you want the out output to be.
  • Google gets approximately .00000003% 1)I forget the exact number, but I’m just repeating what Scott Galloway said better each time you use it
  • In the past, people used to say that the fundamental difference between humans and computers was that humans get better at a task with more information/ “experience” which is something computer can’t do. ML changes that by allowing algorithms/ programs to become more accurate with more information.
    • Also, speaks to a pattern of whenever people say “Computers will never be able to do X because they need a certain skill that only humans have”. It’s usually just a matter of time before computers acquire those skills.
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  • References   [ + ]

    1. I forget the exact number, but I’m just repeating what Scott Galloway said