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 …
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 …
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 …
In the last article, I described an application of the k-means clustering algorithm for classifying candlesticks based on the relative position of their open, high, low and close. This was a simple enough exercise, but now I tackle something more challenging: isolating information that is both useful and practical to real trading. I’ll initially try two …
Candlestick patterns were used to trade the rice market in Japan back in the 1800’s. Steve Nison popularised the idea in the western world and claims that the technique, which is based on the premise that the appearance of certain patterns portend the future direction of the market, is applicable to modern financial markets. Today, …
