This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together. I drew on some of his earlier work as well as other resources, including Quantstart.com. UPDATE 03/01/16: The Python code below has been updated with a more accurate algorithm for calculating the Hurst Exponent. …
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 …
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 …
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 …
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 …
Introduction My first post on using machine learning for financial prediction 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. I looked at various methods to identify predictive features including Maximal Information Coefficient (MIC), Recursive Feature Elimination (RFE), algorithms with …
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 …
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. 2020: I’ve updated the original post with some new thinking about data-mining, refreshed the code, updated the data and …
Recently, I wrote about using mean-reversion time series models to analyze financial data and build trading strategies based on their predictions. Continuing our exploration of time series analysis and modelling, let’s turn our attention to the autoregressive and conditionally heteroskedastic family of models. Specifically, we’ll look into the Autoregressive Integrated Moving Average (ARIMA) and Generalized …
In the first Mean Reversion and Cointegration post, I explored mean reversion of individual financial time series using techniques such as the Augmented Dickey-Fuller test, the Hurst exponent and the Ornstein-Uhlenbeck equation for a mean reverting stochastic process. I also presented a simple linear mean reversion strategy as a proof of concept. In this post, I’ll …
