What this book covers
Chapter 1, Introduction to R, explains basic commands in R. It starts with the installation of R and its packages and moves on to data types, DataFrames, and loops. This chapter also covers how to write and call functions and how to import data files of various formats into R. This chapter is meant to provide a basic understanding of R.
Chapter 2, Statistical Modeling, talks about the exploratory analysis like common distribution, correlation, measure of central tendencies, outlier detection to better understand the data. It also talks about sampling and standardization/ Normalization of the data which helps in preparing the data for analysis. Further this chapter also deals with hypothesis testing and parameter estimation.
Chapter 3, Econometric and Wavelet Analysis, covers simple and multivariate linear regression models, which are the backbone of every analysis. An explanation of ANOVA and feature selection adds flavor to this chapter. We also build a few models using wavelets analysis.
Chapter 4, Time Series Modeling, in this chapter the author presents the examples to convert data in time series using ts, zoo and xts which works as the base for forecasting models. Then the author talks about various forecasting techniques like AR, ARIMA, GARCH,VGARCH etc. and its execution in R along with examples.
Chapter 5, Algorithmic Trading, contains some live examples from the algorithmic trading domain, including momentum trading and pair trading using various methods. CAPM, multifactor model, and portfolio construction are also covered in this chapter.
Chapter 6, Trading Using Machine Learning, shows how to model a machine learning algorithm using capital market data. This covers supervised and unsupervised algorithms.
Chapter 7, Risk Management, in this chapter the author discusses the techniques to measure market and portfolio risk. He also captures the common methods used for calculation of VAR. He also gives examples of the best practices used in banking domain for measuring credit risk.
Chapter 8, Optimization, in this chapter the author demonstrates examples of optimization techniques like dynamic rebalancing, walk forward testing, grid testing, genetic algorithm in financial domain.
Chapter 9, Derivative Pricing, use cases of R in derivative pricing. It covers vanilla option pricing along with exotic options, bonds pricing, credit spread and credit default swaps. This chapter is complex in nature and require people to have some basic understanding of derivatives.
