This post summarises the key lessons of the academic literature that has been published on pairs trading. The key themes are highlighted at the end of the page. Pair Trading Literature Review Gatev, Goetzmann, Rouwenhorst – “Pairs Trading: Performance of a Relative Value Arbitrage Strategy” https://papers.ssrn.com/sol3/papers.cfm?abstract_id=141615 This is the first meaningful academic paper on pair …
Way back in November 2007, literally weeks after SPX put in its pre-GFC all-time high, Friedman, Hastie and Tibshirani published their Graphical Lasso algorithm for estimation of the sparse inverse covariance matrix. Are you suggesting that Friedman and his titans of statistical learning somehow caused the GFC by publishing their Graphical Lasso algorithm? Not at …
Deep Learning for Trading Part 4: Fighting Overfitting is the fourth in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Deep Learning for Trading Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques …
This is the third in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful signals …
This is the second in a multi-part series in which we explore and compare various deep learning tools and techniques for market forecasting using Keras and TensorFlow. In Part 1, we introduced Keras and discussed some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful …
This is the first in a multi-part series where we explore and compare various deep learning trading tools and techniques for market forecasting using Keras and TensorFlow. In this post, we introduce Keras and discuss some of the major obstacles to using deep learning techniques in trading systems, including a warning about attempting to extract meaningful …
This article is a departure from the quantitative research that usually appears on the Robot Wealth blog. Until recently, I was working as a machine learning consultant to financial services organizations and trading firms in Australia and the Asia Pacific region. A few months ago, I left that world behind to join an ex-client’s proprietary …
This article is adapted from one of the units of Advanced Algorithmic Trading. If you like what you see, check out the entire curriculum here. Find out what Robot Wealth is all about here. If you’re interested in using artificial neural networks (ANNs) for algorithmic trading, but don’t know where to start, then this article …
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 …
