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Explore the research behind our trading, plus some just-for-fun stuff....

Posted on Apr 12, 2017 by
2,260 Views

This is the final post in our 3-part Back to Basics series. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 2: How to Succeed at Algorithmic Trading We've also compiled this series into an eBook which you can download for free here. Nearly all research related...

Posted on Mar 20, 2017 by
2,155 Views

This is the second post in our 3-part Back to Basics series on successful algorithmic trading. You may be interested in checking out the other posts in this series: Part 1: An Introduction to Algorithmic Trading Part 3: Backtesting in Algorithmic Trading There is a lot of information about algorithmic and quantitative trading in the public domain today. The type of...

Posted on Feb 04, 2017 by
6,289 Views

This is the first post in our 3-part Back to Basics series which serve as an introduction to algorithmic trading. You may be interested in checking out the other posts in this series: Part 2: How to Succeed at Algorithmic Trading Part 3: Backtesting in Algorithmic Trading This is the first in a series of posts in which we will change gears...

Posted on Jan 03, 2017 by
6,496 Views

What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? That's the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including some Python code for its...

Posted on Oct 31, 2016 by
9,321 Views

This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together and I drew on some of his previously published work as well as other sources like Quantstart.com. UPDATE 03/01/16: Please note that the Python code below has been updated with a more accurate algorithm for calculating Hurst Exponent. Mean-reverting time...

Posted on Oct 14, 2016 by
4,171 Views

This post comes to you from Dr Tom Starke, a good friend of Robot Wealth. Tom is a physicist, quant developer and experienced algo trader with keen interests in machine learning and quantum computing. I am thrilled that Tom is sharing his knowledge and expertise with the Robot Wealth community. Over to you, Tom. Unlike most other businesses, algorithmic trading...

Posted on Aug 09, 2016 by