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

Posted on Oct 14, 2016 by Kris Longmore
2 comments.
4,081 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 Kris Longmore
23 comments.
1,638 Views

It would be great if machine learning were as simple as just feeding data to an out-of-the box implementation of some learning algorithm, then standing back and admiring the predictive utility of the output. As anyone who has dabbled in this area will confirm, it is never that simple. We have features to engineer and transform (no trivial task -...

Posted on Jul 20, 2016 by Kris Longmore
5 comments.
429 Views

Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using machine learning in algorithmic trading systems. Here you can download the presentation Many thanks to all who attended and particularly for the engaging questions. I thoroughly enjoyed myself! In particular, thanks to Andrien Juric for oraganising the event and Sharon Lu...

Posted on Jun 21, 2016 by Kris Longmore
No Comments.
3,391 Views

If there's one thing I've done a lot of over the last few years, reading would be it. I've devoted a great deal of time to devouring any material that I thought might give me an edge in my trading - textbooks, academic papers, blog articles, training courses, lecture notes, conference presentations...anything and everything I could get my hands on. I was...

Posted on May 10, 2016 by Kris Longmore
43 comments.
7,366 Views

My first post on using machine learning for trading took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. We looked at various methods to identify predictive features including Maximal Information Coefficient (MIC), Recursive Feature Elimination (RFE), algorithms with built-in feature selection, selection via exhaustive search...

Posted on Apr 14, 2016 by Kris Longmore
5 comments.
1,631 Views

Disclaimer: I am not posting this at the behest of the developers of Zorro, nor do I receive any form of payment or commission for this post. I felt that I should relay this experience because it was an example of customer service that went way above and beyond the call of duty in terms of its promptness and professionalism. Credit where...

Posted on Mar 04, 2016 by Kris Longmore
71 comments.
19,167 Views

Updates 2019: In this first Machine Learning for Trading post, we've added a section on feature selection using the Boruta package, equity curves of a simple trading system, and some Lite-C code that generates the training data. Don't forget to download the code and data used throughout the Machine Learning for Trading series. Way back in the day when I...

Posted on Feb 04, 2016 by Kris Longmore
22 comments.
8,439 Views

Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models' predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let's research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional...