"Algorithmic Trading is an insightful book on quantitativetrading written by a seasoned practitioner. What sets this bookapart from many others in the space is the emphasis on realexamples as opposed to just theory. Concepts are not onlydescribed, they are brought to life with actual trading strategies,which give the reader insight into how and why each strategy wasdeveloped, how it was implemented, and even how it was coded. Thisbook is a valuable resource for anyone looking to create their ownsystematic trading strategies and those involved in managerselection, where the knowledge contained in this book will lead toa more informed and nuanced conversation with managers."
—DAREN SMITH, CFA, CAIA, FSA, Managing Director, ManagerSelection & Portfolio Construction, University of Toronto AssetManagement
"Using an excellent selection of mean reversion and momentumstrategies, Ernie explains the rationale behind each one, shows howto test it, how to improve it, and discusses implementation issues.His book is a careful, detailed exposition of the scientific methodapplied to strategy development. For serious retail traders, I knowof no other book that provides this range of examples and level ofdetail. His discussions of how regime changes affect strategies,and of risk management, are invaluable bonuses."
—Roger Hunter, Mathematician and AlgorithmicTrader
This text aims to overcome several common obstacles in teaching financial modeling. First, most texts do not provide students with enough information to allow them to implement models from start to finish. In this book, we walk through each step in relatively more detail and show intermediate R output to help students make sure they are implementing the analyses correctly. Second, most books deal with sanitized or clean data that have been organized to suit a particular analysis. Consequently, many students do not know how to deal with real-world data or know how to apply simple data manipulation techniques to get the real-world data into a usable form. This book will expose students to the notion of data checking and make them aware of problems that exist when using real-world data. Third, most classes or texts use expensive commercial software or toolboxes. In this text, we use R to analyze financial data and implement models. R and the accompanying packages used in the text are freely available; therefore, any code or models we implement do not require any additional expenditure on the part of the student.
Demonstrating rigorous techniques applied to real-world data, this text covers a wide spectrum of timely and practical issues in financial modeling, including return and risk measurement, portfolio management, options pricing, and fixed income analysis.
Written by one of the leading experts on the topic, An Introduction to Analysis of Financial Data with R explores basic concepts of visualization of financial data. Through a fundamental balance between theory and applications, the book supplies readers with an accessible approach to financial econometric models and their applications to real-world empirical research.
The author supplies a hands-on introduction to the analysis of financial data using the freely available R software package and case studies to illustrate actual implementations of the discussed methods. The book begins with the basics of financial data, discussing their summary statistics and related visualization methods. Subsequent chapters explore basic time series analysis and simple econometric models for business, finance, and economics as well as related topics including:Linear time series analysis, with coverage of exponential smoothing for forecasting and methods for model comparisonDifferent approaches to calculating asset volatility and various volatility modelsHigh-frequency financial data and simple models for price changes, trading intensity, and realized volatilityQuantitative methods for risk management, including value at risk and conditional value at riskEconometric and statistical methods for risk assessment based on extreme value theory and quantile regression
Throughout the book, the visual nature of the topic is showcased through graphical representations in R, and two detailed case studies demonstrate the relevance of statistics in finance. A related website features additional data sets and R scripts so readers can create their own simulations and test their comprehension of the presented techniques.
An Introduction to Analysis of Financial Data with R is an excellent book for introductory courses on time series and business statistics at the upper-undergraduate and graduate level. The book is also an excellent resource for researchers and practitioners in the fields of business, finance, and economics who would like to enhance their understanding of financial data and today's financial markets.