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
- Intermediate/Advanced R Programming
- Mixed R and Python Application Development
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 :
- Introductory Statistics with R, 2nd Edition - Peter Dalgaard
- A Beginner's Guide to R - Alain Zuur, Elena Ieno, Erik Meesters
- R in a Nutshell - Joseph Adler
- The R Book - Michael J. Crawley
- Discovering Statistics Using R - Andy Field, Jeremy Miles, Zoe Field
- The Art of R Programming: A Tour of Statistical Software Design - Norman Matlof
- R in Action: Data Analysis and Graphics with R - Robert Kabacoff
- R for Data Science - Garrett Grolemund, Hadley Wickham
- Hands-On Programming with R: Write Your Own Functions and Simulations - Garrett Grolemund
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:
- Introductory Time Series with R - Paul Cowpertwait, Andrew Metcalfe
- Multivariate Time Series Analysis: with R and Financial Applications - Ruey S. Tsay
- Practical Time Series Forecasting with R: A Hands-On Guide [2nd Edition] (Practical Analytics) - Galit Shmueli, Kenneth C. Lichtendahl Jr
- Time Series Analysis: With Applications in R (Springer Texts in Statistics) - Jonathan D. Cryer, Kung-Sik Chan
- Nonlinear Time Series Analysis with R - Ray Huffaker, Marco Bittelli, Rodolfo Rosa
- Displaying Time Series, Spatial, and Space-Time Data with R - Oscar Perpinan Lamigueiro
- An Introduction to Applied Multivariate Analysis with R - Brian Everitt, Torsten Hothorn
- R Cookbook - Paul Teetor
- Machine Learning with R, 2nd Edition - Brett Lantz
- R Machine Learning By Example - Raghav Bali, Dipanjan Sarkar
- Mastering Machine Learning with R - Second Edition - Cory Lesmeister
- Machine Learning with R - Abhijit Ghatak
- Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles - Giuseppe Ciaburro and Balaji Venkateswaran
- Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R - Bertram K. C. Chan
- Financial Risk Modelling and Portfolio Optimization with R 2nd Ed. - Bernhard Pfaff
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.
- Python for R Users - Ajay Ohri
- Data Analytics for Data Science, Big Data & Machine Learning: A Practical Step-By-Step Guide & Exam Preparation using Hadoop, Python, and R - B. Charles Henry
- Statistical Application Development with R and Python - Second Edition: Develop applications using data processing, statistical models, and CART - Prabhanjan Narayanachar Tattar
- Practical Data Science Cookbook - Second Edition: Data pre-processing, analysis and visualization using R and Python - Prabhanjan Tattar, Tony Ojeda, Sean Patrick Murphy
- Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics) - Thomas W. Miller
- Data science : fondamentaux et études de cas : Machine learning avec Python et R (French) - Eric Biernat, Michel Lutz, Yann LeCun
- Modeling Techniques in Predictive Analytics with Python and R: A Guide to Data Science - Thomas W. Miller
- Extending R - John M. Chambers
- Bayesian Models for Astrophysical Data: Using R, JAGS, Python, and Stan - Joseph M. Hilbe, Rafael S. de Souza, Emille E. O. Ishida
