I've been a bit busy lately with a few big things, however, I wanted to stop by and mention a fantastic book for those who have been following along the R examples. Anyone who's followed my blog knows that I'm big on practical books with examples. There are also three main open source tools I've discussed with regards to prototyping trading systems: Weka, Python, and R. Of the three tools mentioned, I've been able to recommend Witten and Frank's book on Data Mining for Weka, and Stephen Marsland's book on Machine Learning as the Python bible for hands on Machine Learning. Well now, I can thankfully complete the trinity, with Luis Torgo's new book, 'Data Mining with R, Learning with Case Studies.'
Both R novices and experts will find this a great reference for Data Mining. The opening chapter has a useful intro to get you started on R (Factors, Vectors, and Data Frames, as well as other useful objects are covered with examples). Additional chapters cover both classification and regression type prediction schemes.
The most useful chapter to readers here, however, is the chapter on 'Predicting Stock Market Returns.' Many of the readers who have been looking for example scripts on some of the topics I've covered, will find them here. Not only is gathering and processing data (CSV, quantmod and yahoo finance, and MySQL) well covered, but various prediction and evaluation schemes (cross validation, sliding and growing windows, PerformanceAnalytics package) are discussed along with access to the author's code. Many topics I haven't discussed yet are available here as well, including MARS (Multivariate Adaptive Regression Splines), SVMs, and various validation techniques along with handy tabulation of results. Having read a previous draft, I'm still working into the examples, and welcome any feedback and thoughts I can address.
The book can be accessed via the amazon book showcase on the right and instructions for R code access are available in the book.
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