Machine Learning in Algorithmic Trading Systems: Opportunities and Pitfalls
Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using
Last night it was my pleasure to present at the Tyro Fintech Hub in Sydney on the topic of using
If there’s one thing I’ve done a lot of over the last few years, reading would be it. I’ve devoted
Introduction My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods
Recently, I wrote about using mean-reversion time series models to analyze financial data and build trading strategies based on their
In the first Mean Reversion and Cointegration post, I explored mean reversion of individual financial time series using techniques such
This series of posts is inspired by several chapters from Ernie Chan’s highly recommended book Algorithmic Trading. The book follows Ernie’s
Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share
In the last article, I described an application of the k-means clustering algorithm for classifying candlesticks based on the relative position
Candlestick patterns were used to trade the rice market in Japan back in the 1800’s. Steve Nison popularised the idea
This post builds on work done by jcl over at his blog, The Financial Hacker. He proposes the Cold Blood
In the first part of this article, I described a procedure for empirically testing whether a trading strategy has predictive
Picture this: A developer has coded up a brilliant strategy, taking great care not to over-optimize. There is no look-ahead