Ebook Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective
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Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective
Ebook Quantitative Trading with R: Understanding Mathematical and Computational Tools from a Quant’s Perspective
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Product details
Hardcover: 272 pages
Publisher: Palgrave Macmillan; 2015 edition (January 20, 2015)
Language: English
ISBN-10: 9781137354075
ISBN-13: 978-1137354075
ASIN: 1137354070
Product Dimensions:
6.5 x 1 x 10 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.2 out of 5 stars
32 customer reviews
Amazon Best Sellers Rank:
#682,521 in Books (See Top 100 in Books)
While this book covers excellent material related to quantitative trading, it suffers from several problems.First and foremost, it suffers from the ever increasingly common problem of poor editing. Function names are misspelled, variables names are mismatched between code and text, and figures are referenced by the wrong number. Code, tables and figures are often referred to as "below" when they aren't, and there are no numbers and multiple figures per page. Really amateur stuff.As far as the content goes, it swings to wildly between overly simplified and ridiculously complex. For example, the introduction to statistics spends way to much time using the mean as an example, but then proceeds to a detailed yet too difficult description of bias, before abruptly pivoting to variance and variance-bias trade off in just a few lines.The R code examples are well motivated but no one should fall for the authors pretense of this being accessible to beginners. Although well formed (if not always best written), the code is not described in sufficient detail in text.Overall, this book could have used another round of editing and maybe 50 pages of additional material in the early chapters. Also, some of the later material should have been reworked and marketed to intermediate to advanced programmers with at least some background in quantitative finance.
This book provides a basic introduction to quantitative finance in the R language. The material really covers a very cursory set of information across a broad swath of topics (1. how to write R code, 2. import/export data in R, 3. basic statistics/time series concepts, 4. basic backtesting/strategy testing, etc.).Beyond authoring an introduction book to this subject, I suspect the author's intent was to create a usable book that would accompany his teachings of quantitative finance @ Loyola University of Chicago. It's an understandable and common occurrence that professors often write books (bought by their students). Perhaps, it is even more needed in this area given the dearth of books in this topic. Motivations aside, I think this book will be finished quickly by any intermediate-advanced R quants. Certainly an intermediate-advanced quant might pick up a few tricks along the way. But I think the most use of this book will come from relative newcomers.This book provides a nice roadmap or framework for introducing the various components that typify a quant trader's workflow (quantmod, dplyr, quantstrat, xlsx, etc.), which a reader can then expand upon in their own right. However, given the wide scope of material the book touches upon, there is a lack of density, examples, and depth in any particular area. It would really take 2-3x the material to cover each subject far more in-depth (to my personal liking). I agree with another reviewer's comments who wishes the author had produced a book only focused on the heart of developing/executing quantitative strategies (case by case examples) with an assumption that the reader had a basic R framework/stats background, rather than authoring a more generalist R book aimed at introductory quant. Truthfully, no so such book exists and it may be a few years until we see that book. Perhaps Mr. Georgakopoulos will continue to expand upon the book until future editions tackle each distinct area with the depth needed (wanted) by the more advanced user.
I would highly recommend this text to anyone with a strong statistics and mathematics background, but has almost no knowledge of finance and how to analyze financial data. The author is clear and concise, and the book is full of great examples. Each chapter starts with a premise and typically builds off of the last chapter. The author walks you through step-by-step to show you the ropes of empirical quantitative finance, and gives you information on all of the relevant packages and techniques being used in the industry. The only downside is that the book is not extremely thorough on all of the topics that are covered. For instance, the author does not go into detail on the theory of time series analysis and econometrics, but rather he gives you a good outline and starting place for researching these topics on your own.I am a master's student in an economics and statistics program and my only finance knowledge are the two finance courses I took in undergrad. I bought this book because I just started a job at a fund to build quantitative trading strategies. I can't tell you how much this book has truly helped me out. It is the bible at this point in my life.
This book is amazing. It teaches you both R programming and financial modeling.Also, the math, statistics, and finance theories are combined in a very smart way with applications and examples of models and algorithms.Using the information in this book, I was able to build a daily trading strategy in R. I also learned the essential tactics and strategies that we need to implement before we can use any algorithms in real trading.I highly recommend this book for graduate students, quant traders, and anyone looking for knowledge in the field of financial modeling.
Watching stocks for a while. New to R. New to Quantitative Trading. That being said, I loved this book. If you are fluent in any these, definitely not the book for you. From my standpoint, it offers a comprehensive intro into all of these which allows you to start developing your own strategies once your done. You can pretty much find all the information included online; however, it was nice to have it one place.
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