Billed as “a training resource for ... students and professionals,” this title is a sophisticated manual on financial data manipulation, statistics, and R programming. It is by no means for the beginner or the faint of heart--a decent background in statistics and financial analysis is required to understand this text.

The first few chapters are brief introductions to “analytical thinking” (six throwaway pages), the R language, financial statistics, and financial securities. They are relatively superficial and can be ignored.

The meat follows: chapter 5 starts to look at dataset composition and risk, chapter 6 discusses time series, and then the next three chapters contain detailed discussions of the statistical themes of Sharpe ratios, mean variance, and cluster analysis as applied via the use of R in the financial realm.

The market is addressed next. Chapter 10 is on gauging market sentiment, and chapter 11 is on simulating trading--here is where I had hoped the authors would focus more attention on competing strategies and their implementation and verification. The remaining chapters close out the themes, focusing on prediction and options strategies, which are open to debate in terms of efficacy and potential. A useful appendix discusses probability distributions and statistical analysis. The index is decent and the references useful.

The chapters are long on detailed exposition and short on debating points of view. If you don’t agree with the authorial approaches to, say, simulating trading strategies, or to how strategies are chosen, you won’t find background material to help. Nor will you get background in R, which would help you execute your own strategies; for that, one should refer to R for data science [1], which is a better overall introduction to R. Other competing titles [2,3] are useful complements to the text reviewed here.

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