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BOOK EXCERPT:
Quantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.
Product Details :
Genre |
: Business & Economics |
Author |
: Harry Georgakopoulos |
Publisher |
: Springer |
Release |
: 2015-02-02 |
File |
: 281 Pages |
ISBN-13 |
: 9781137437471 |
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BOOK EXCERPT:
Implement machine learning, time-series analysis, algorithmic trading and more About This Book Understand the basics of R and how they can be applied in various Quantitative Finance scenarios Learn various algorithmic trading techniques and ways to optimize them using the tools available in R. Contain different methods to manage risk and explore trading using Machine Learning. Who This Book Is For If you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required. What You Will Learn Get to know the basics of R and how to use it in the field of Quantitative Finance Understand data processing and model building using R Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis Build and analyze quantitative finance models using real-world examples How real-life examples should be used to develop strategies Performance metrics to look into before deciding upon any model Deep dive into the vast world of machine-learning based trading Get to grips with algorithmic trading and different ways of optimizing it Learn about controlling risk parameters of financial instruments In Detail The role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language. You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate. We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging. By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R. Style and approach This book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example.
Product Details :
Genre |
: Computers |
Author |
: Dr. Param Jeet |
Publisher |
: Packt Publishing Ltd |
Release |
: 2017-03-23 |
File |
: 276 Pages |
ISBN-13 |
: 9781786465252 |
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BOOK EXCERPT:
After the fundamental volume and the advanced technique volume, this volume focuses on R applications in the quantitative investment area. Quantitative investment has been hot for some years, and there are more and more startups working on it, combined with many other internet communities and business models. R is widely used in this area, and can be a very powerful tool. The author introduces R applications with cases from his own startup, covering topics like portfolio optimization and risk management.
Product Details :
Genre |
: Computers |
Author |
: Dan Zhang |
Publisher |
: CRC Press |
Release |
: 2018-04-24 |
File |
: 387 Pages |
ISBN-13 |
: 9781315352534 |
eBook Download
BOOK EXCERPT:
If you want to learn how to quantitatively answer scientific questions for practical purposes using the powerful R language and the open source R tool ecosystem, this book is ideal for you. It is ideally suited for scientists who understand scientific concepts, know a little R, and want to be able to start applying R to be able to answer empirical scientific questions. Some R exposure is helpful, but not compulsory.
Product Details :
Genre |
: Computers |
Author |
: Paul Gerrard |
Publisher |
: Packt Publishing Ltd |
Release |
: 2015-01-31 |
File |
: 432 Pages |
ISBN-13 |
: 9781783555260 |
eBook Download
BOOK EXCERPT:
Quantitative Finance with R offers a winning strategy for devising expertly-crafted and workable trading models using the R open source programming language, providing readers with a step-by-step approach to understanding complex quantitative finance problems and building functional computer code.
Product Details :
Genre |
: Business & Economics |
Author |
: Harry Georgakopoulos |
Publisher |
: Springer |
Release |
: 2015-02-02 |
File |
: 364 Pages |
ISBN-13 |
: 9781137437471 |
eBook Download
BOOK EXCERPT:
Explore over 110 recipes to analyze data and build predictive models with simple and easy-to-use R code About This Book Apply R to simplify predictive modeling with short and simple code Use machine learning to solve problems ranging from small to big data Build a training and testing dataset, applying different classification methods. Who This Book Is For This book is for data science professionals, data analysts, or people who have used R for data analysis and machine learning who now wish to become the go-to person for machine learning with R. Those who wish to improve the efficiency of their machine learning models and need to work with different kinds of data set will find this book very insightful. What You Will Learn Create and inspect transaction datasets and perform association analysis with the Apriori algorithm Visualize patterns and associations using a range of graphs and find frequent item-sets using the Eclat algorithm Compare differences between each regression method to discover how they solve problems Detect and impute missing values in air quality data Predict possible churn users with the classification approach Plot the autocorrelation function with time series analysis Use the Cox proportional hazards model for survival analysis Implement the clustering method to segment customer data Compress images with the dimension reduction method Incorporate R and Hadoop to solve machine learning problems on big data In Detail Big data has become a popular buzzword across many industries. An increasing number of people have been exposed to the term and are looking at how to leverage big data in their own businesses, to improve sales and profitability. However, collecting, aggregating, and visualizing data is just one part of the equation. Being able to extract useful information from data is another task, and a much more challenging one. Machine Learning with R Cookbook, Second Edition uses a practical approach to teach you how to perform machine learning with R. Each chapter is divided into several simple recipes. Through the step-by-step instructions provided in each recipe, you will be able to construct a predictive model by using a variety of machine learning packages. In this book, you will first learn to set up the R environment and use simple R commands to explore data. The next topic covers how to perform statistical analysis with machine learning analysis and assess created models, covered in detail later on in the book. You'll also learn how to integrate R and Hadoop to create a big data analysis platform. The detailed illustrations provide all the information required to start applying machine learning to individual projects. With Machine Learning with R Cookbook, machine learning has never been easier. Style and approach This is an easy-to-follow guide packed with hands-on examples of machine learning tasks. Each topic includes step-by-step instructions on tackling difficulties faced when applying R to machine learning.
Product Details :
Genre |
: Computers |
Author |
: AshishSingh Bhatia |
Publisher |
: Packt Publishing Ltd |
Release |
: 2017-10-23 |
File |
: 564 Pages |
ISBN-13 |
: 9781787287808 |
eBook Download
BOOK EXCERPT:
Learn the art of building robust and powerful recommendation engines using R About This Book Learn to exploit various data mining techniques Understand some of the most popular recommendation techniques This is a step-by-step guide full of real-world examples to help you build and optimize recommendation engines Who This Book Is For If you are a competent developer with some knowledge of machine learning and R, and want to further enhance your skills to build recommendation systems, then this book is for you. What You Will Learn Get to grips with the most important branches of recommendation Understand various data processing and data mining techniques Evaluate and optimize the recommendation algorithms Prepare and structure the data before building models Discover different recommender systems along with their implementation in R Explore various evaluation techniques used in recommender systems Get to know about recommenderlab, an R package, and understand how to optimize it to build efficient recommendation systems In Detail A recommendation system performs extensive data analysis in order to generate suggestions to its users about what might interest them. R has recently become one of the most popular programming languages for the data analysis. Its structure allows you to interactively explore the data and its modules contain the most cutting-edge techniques thanks to its wide international community. This distinctive feature of the R language makes it a preferred choice for developers who are looking to build recommendation systems. The book will help you understand how to build recommender systems using R. It starts off by explaining the basics of data mining and machine learning. Next, you will be familiarized with how to build and optimize recommender models using R. Following that, you will be given an overview of the most popular recommendation techniques. Finally, you will learn to implement all the concepts you have learned throughout the book to build a recommender system. Style and approach This is a step-by-step guide that will take you through a series of core tasks. Every task is explained in detail with the help of practical examples.
Product Details :
Genre |
: Computers |
Author |
: Suresh K. Gorakala |
Publisher |
: Packt Publishing Ltd |
Release |
: 2015-09-29 |
File |
: 158 Pages |
ISBN-13 |
: 9781783554508 |
eBook Download
BOOK EXCERPT:
This book prepares students to execute the quantitative and computational needs of the finance industry. The quantitative methods are explained in detail with examples from real financial problems like option pricing, risk management, portfolio selection, etc. Codes are provided in R programming language to execute the methods. Tables and figures, often with real data, illustrate the codes. References to related work are intended to aid the reader to pursue areas of specific interest in further detail. The comprehensive background with economic, statistical, mathematical, and computational theory strengthens the understanding. The coverage is broad, and linkages between different sections are explained. The primary audience is graduate students, while it should also be accessible to advanced undergraduates. Practitioners working in the finance industry will also benefit.
Product Details :
Genre |
: Business & Economics |
Author |
: Rituparna Sen |
Publisher |
: Springer Nature |
Release |
: 2023-05-16 |
File |
: 352 Pages |
ISBN-13 |
: 9789811920080 |
eBook Download
BOOK EXCERPT:
"Building Algorithmic Trading Systems: A Step-by-Step Guide" is an essential resource for anyone seeking to understand and master the art and science of algorithmic trading. This comprehensive guide navigates the complex interplay between technology, finance, and mathematics, offering readers a systematic approach to designing, coding, and deploying sophisticated trading algorithms. With clarity and precision, it illuminates foundational concepts while providing practical insights into data analysis, risk management, and the latest innovations in machine learning and AI applications within trading. The book delves deeply into the infrastructure required to support algorithmic trading, detailing the technological frameworks necessary for success in modern financial markets. Readers will benefit from expertly crafted sections on backtesting strategies, portfolio optimization, and ethical considerations, ensuring that they are well-equipped to create robust, efficient, and ethical trading systems. As markets evolve, this book stands as a beacon, guiding traders through emerging trends and regulatory landscapes, setting the stage for sustainable and informed trading practices. Whether you are a novice eager to explore the potentials of algorithmic trading or a seasoned professional looking to enhance your strategic acumen, "Building Algorithmic Trading Systems" offers invaluable knowledge and tools, ensuring your place at the forefront of financial innovation.
Product Details :
Genre |
: Business & Economics |
Author |
: William Johnson |
Publisher |
: HiTeX Press |
Release |
: 2024-10-17 |
File |
: 719 Pages |
ISBN-13 |
: PKEY:6610000659067 |
eBook Download
BOOK EXCERPT:
Illustrates how R may be used successfully to solve problems in quantitative finance Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R provides R recipes for asset allocation and portfolio optimization problems. It begins by introducing all the necessary probabilistic and statistical foundations, before moving on to topics related to asset allocation and portfolio optimization with R codes illustrated for various examples. This clear and concise book covers financial engineering, using R in data analysis, and univariate, bivariate, and multivariate data analysis. It examines probabilistic calculus for modeling financial engineering—walking the reader through building an effective financial model from the Geometric Brownian Motion (GBM) Model via probabilistic calculus, while also covering Ito Calculus. Classical mathematical models in financial engineering and modern portfolio theory are discussed—along with the Two Mutual Fund Theorem and The Sharpe Ratio. The book also looks at R as a calculator and using R in data analysis in financial engineering. Additionally, it covers asset allocation using R, financial risk modeling and portfolio optimization using R, global and local optimal values, locating functional maxima and minima, and portfolio optimization by performance analytics in CRAN. Covers optimization methodologies in probabilistic calculus for financial engineering Answers the question: What does a "Random Walk" Financial Theory look like? Covers the GBM Model and the Random Walk Model Examines modern theories of portfolio optimization, including The Markowitz Model of Modern Portfolio Theory (MPT), The Black-Litterman Model, and The Black-Scholes Option Pricing Model Applied Probabilistic Calculus for Financial Engineering: An Introduction Using R s an ideal reference for professionals and students in economics, econometrics, and finance, as well as for financial investment quants and financial engineers.
Product Details :
Genre |
: Mathematics |
Author |
: Bertram K. C. Chan |
Publisher |
: John Wiley & Sons |
Release |
: 2017-09-11 |
File |
: 478 Pages |
ISBN-13 |
: 9781119388043 |