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 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 …
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 …
This series of posts is inspired by several chapters from Ernie Chan’s highly recommended book Algorithmic Trading. The book follows Ernie’s first contribution, Quantitative Trading, and focuses on testing and implementing a number of strategies that exploit measurable market inefficiencies. I’m a big fan of Ernie’s work and have used his material as inspiration for a great deal …
Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share and demonstrate the use of the framework I’ve put together to speed the research and development process for a particular type of trading strategy. Comments and critiques regarding the framework and the methodology used are most …
