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Books on and Related To Quantitative Finance Part 4 - Statistics using R and Python

This list arose as a result of research involved in the development of a series of fairly advance Python, R and C++ courses for those working in the area of Quantitative Finance. Quantitative finance is a technical subject that encompasses many technical disciplines and areas of applicabipity. These include e.g. financial markets, time series analysis, risk management, financial engineering, statistical data analysis and machine learning. The following list of books, though by no means exhaustive represents an attempt to pull together those books that may be useful, and includes also some books that are more in the nature of "light reading" for when all that maths, computing and theory becomes too much. Some of the books cover the basic essentials in a given are whilst other are specialised references, and most are somewhere in between. The list is split up over several sections each made up of sub-sections covering particular areas of interest. The areas covered on this page - Part 4 - are concerned with the statistical Package R, its associated programming language and how they are used in Quantitative Finance. Also some useful books concerning the combined use of Python and R are included.
Part - 1 Is concerned with historical, career and interview preparation aspects of Quantitative Finance.
Part - 2 Is concerned with Quantitative Trading, High Frequency Trading and Time Series Analysis
Part - 3 Is concerned with C++ and Python programming as it is used in Quantitative Finance
Part - 5 will be concerned with Java Programming and how it is used in Quantitative Finance
Part - 6 will be concerned with statistics and machine learning and how they can be applied in Quantitative Finance
Part - 7 will be concerned with numerical analytical an modeling methods used in Quantitative Finance
Part - 8 will be concerned with Quantitative Financial aspects of Derivatives
Part - 9 will be concerned with Volatility and Portfolio management aspect of Quantitative Finance
Part - 10 will be concerned with advanced programming and machine learning technologies and frameworks such as Matlab and CUDA
Part - 11 will be concerned with spreadsheets, data mining and data visualisation as they apply to Quantitative Finance

Introductory R Programming

R is an advanced statistical programming framework widely used in systematic quant funts and investment banks. When developing an advanced Python programming course for computerised trading application developers working in the City (of London) the author of that course, and this list, also developed a hybrid Python + R module, as well as several R programming modules. These modules were developed via ITBS's sister company First Technology Transfer (FTT). If there is sufficient interest then FTT can run a series of evening workshops in the city covering R programming and mixed Python + R programming

Because R includes not only a very wide selection of statistical libraries and data visualisation tool but also has an inbuilt programming language it is a very powerful tool that can be used to both study the methods and assumptions underlying quantitative trading, but also to develop "full blown" quantiative applications. A good understanding of statistics is important in practicing the art of quantitative finance and learning statistics via the use of R is a good way to refresh or devlop statistical data analysis skillw.

Getting Started with R

These following books provide a good foundation to R and how to use it effectives for statistical data analysis :

Intermediate/Advanced R Programming

The books listed in this section are concerned with advanced uses and applications of R that are relevant to "quant finance". The list includes books dealing with the fields of time series analysis and machine learning:

Mixed R and Python Application Development

Various Python libraries have been developed for interfacing with R. The combination of R and Python is a powerful one. The following books provide an introduction to mixed Python and R application development. Also in this list are books which provide a compare and contrast description of Python and R and describe techniques for converting R code into Python code and vice versa. You mau wonder why I have include a book on Bayesian Models for Astrophysical data in this section. The answer is, actually quite simple. Astrophysicists have to do complex searching, pattern matching and modeling on vast amounts of observational data using the most effective tools at their disposal. Having run a number of advanced Python programming courses for astronomers and astrophysicists, on behalf of FTT, I have been most impressed by the inventiveness and originality of their approaches to handling complex observational data. Many would make outstanding "Quants", but have chosen the "higher vocation of science" instead. Quants could do worse than learn from the various techniques they have developed, quite a few of which are described in this book.