Download or read book Graphing Stock Market Data in R written by Hanna Kattilakoski and published by GRIN Verlag. This book was released on 2020-06-22 with total page 31 pages. Available in PDF, EPUB and Kindle. Book excerpt: Seminar paper from the year 2018 in the subject Computer Science - Commercial Information Technology, grade: 90.00, Cologne Business School Köln, language: English, abstract: R is a programming language similar to S, designed for statistical computing and graphics. R is a GNU project developed at Bell Laboratories, with the first version launched in 2000. This paper is a demonstration of different graphing applications that can be accomplished through the R programming language. The majority of the focus will be on the analysis of stock market information in R. The starting point for this paper is with the first project that was conducted: a scatterplot combining aesthetic elements. With a basic code, the project added a creative twist to graphing in R. The outcome of this project was a scatterplot graphing heartweight and bodyweight of male and female cats. This project was found on R-Bloggers, and changes were made accordingly to the code. Instead of using normal points on the graph, the dots were replaced with .png images of cats. This provided a fun, visual example that made differentiating between male and female cats easier, therefore allowing for easier analysis of trends based on the sex of the cat. A linear regression trend line is also implemented, with paw prints, to further illustrate the correlation between the data.

Download or read book R Graphs Cookbook Second Edition written by Jaynal Abedin and published by Packt Publishing Ltd. This book was released on 2014-10-28 with total page 581 pages. Available in PDF, EPUB and Kindle. Book excerpt: Targeted at those with an existing familiarity with R programming, this practical guide will appeal directly to programmers interested in learning effective data visualization techniques with R and a wide-range of its associated libraries.

Download or read book Practical Graph Mining with R written by Nagiza F. Samatova and published by CRC Press. This book was released on 2013-07-15 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste

Download or read book R Cookbook written by Paul Teetor and published by "O'Reilly Media, Inc.". This book was released on 2011-03-03 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an experienced data programmer, it will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process. Create vectors, handle variables, and perform other basic functions Input and output data Tackle data structures such as matrices, lists, factors, and data frames Work with probability, probability distributions, and random variables Calculate statistics and confidence intervals, and perform statistical tests Create a variety of graphic displays Build statistical models with linear regressions and analysis of variance (ANOVA) Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language—one practical example at a time."—Jeffrey Ryan, software consultant and R package author

Download or read book Stock Charts For Dummies written by Greg Schnell and published by John Wiley & Sons. This book was released on 2018-02-21 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: The easy way to get started in stock charts Many trading and technical analysis books focus on how to use charts to make stock trading decisions, but what about how to actually build a chart? Stock Charts For Dummies reveals the important stories charts tell, and how different parameters can impact what you see on the screen. This book will explain some of the most powerful display settings that help traders understand the information in a chart to find outperformance as its beginning. Stock Charts for Dummies will teach you how to build a visually appealing chart and add tools based on the type of trading or investing decision you're trying to make. It will also introduce you to the pros, cons, and best practices of using three key types of charts: Candlesticks, Bar Charts, and Line Charts. Build and use technical chart patterns Increase profits and minimize risk Track and identify specific trends within charts A unique guide for beginning traders and investors, Stock Charts for Dummies will help you make sense of stock charts.

Download or read book Computational Finance and Financial Econometrics written by Eric Zivot and published by CRC Press. This book was released on 2017-01-15 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents mathematical, programming and statistical tools used in the real world analysis and modeling of financial data. The tools are used to model asset returns, measure risk, and construct optimized portfolios using the open source R programming language and Microsoft Excel. The author explains how to build probability models for asset returns, to apply statistical techniques to evaluate if asset returns are normally distributed, to use Monte Carlo simulation and bootstrapping techniques to evaluate statistical models, and to use optimization methods to construct efficient portfolios.

Download or read book Hands On Data Analysis in R for Finance written by Jean-Francois Collard and published by CRC Press. This book was released on 2022-11-16 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: The subject of this textbook is to act as an introduction to data science / data analysis applied to finance, using R and its most recent and freely available extension libraries. The targeted academic level is undergrad students with a major in data science and/or finance and graduate students, and of course practitioners or professionals who need a desk reference. Assumes no prior knowledge of R The content has been tested in actual university classes Makes the reader proficient in advanced methods such as machine learning, time series analysis, principal component analysis and more Gives comprehensive and detailed explanations on how to use the most recent and free resources, such as financial and statistics libraries or open database on the internet

Download or read book The Statistics and Machine Learning with R Workshop written by Liu Peng and published by Packt Publishing Ltd. This book was released on 2023-10-25 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the fundamentals of statistics and machine learning using R libraries for data processing, visualization, model training, and statistical inference Key Features Advance your ML career with the help of detailed explanations, intuitive illustrations, and code examples Gain practical insights into the real-world applications of statistics and machine learning Explore the technicalities of statistics and machine learning for effective data presentation Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts. Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career. By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.What you will learn Hone your skills in different probability distributions and hypothesis testing Explore the fundamentals of linear algebra and calculus Master crucial statistics and machine learning concepts in theory and practice Discover essential data processing and visualization techniques Engage in interactive data analysis using R Use R to perform statistical modeling, including Bayesian and linear regression Who this book is forThis book is for beginner to intermediate-level data scientists, undergraduate to masters-level students, and early to mid-senior data scientists or analysts looking to expand their knowledge of machine learning by exploring various R libraries. Basic knowledge of linear algebra and data modeling is a must.

Download or read book Practical R for Mass Communication and Journalism written by Sharon Machlis and published by CRC Press. This book was released on 2018-12-21 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you want to use R to tell stories? This book was written for you—whether you already know some R or have never coded before. Most R texts focus only on programming or statistical theory. Practical R for Mass Communication and Journalism gives you ideas, tools, and techniques for incorporating data and visualizations into your narratives. You’ll see step by step how to: Analyze airport flight delays, restaurant inspections, and election results Map bank locations, median incomes, and new voting districts Compare campaign contributions to final election results Extract data from PDFs Whip messy data into shape for analysis Scrape data from a website Create graphics ranging from simple, static charts to interactive visualizations for the Web If you work or plan to work in a newsroom, government office, non-profit policy organization, or PR office, Practical R for Mass Communication and Journalism will help you use R in your world. This book has a companion website with code, links to additional resources, and searchable tables by function and task. Sharon Machlis is the author of Computerworld’s Beginner’s Guide to R, host of InfoWorld’s Do More With R video screencast series, admin for the R for Journalists Google Group, and is well known among Twitter users who follow the #rstats hashtag. She is Director of Editorial Data and Analytics at IDG Communications (parent company of Computerworld, InfoWorld, PC World and Macworld, among others) and a frequent speaker at data journalism and R conferences.

Download or read book Identifying Patterns in Financial Markets written by João Leitão and published by Springer. This book was released on 2017-12-26 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes a new pattern discovery approach based on the combination among rules between Perceptually Important Points (PIPs) and the Symbolic Aggregate approximation (SAX) representation optimized by Genetic Algorithm (GA). The proposed approach was tested with real data from S&P500 index and all the results obtained outperform the Buy&Hold strategy. Three different case studies are presented by the authors.

Download or read book Quantitative Economics with R written by Vikram Dayal and published by Springer Nature. This book was released on 2020-02-03 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a contemporary treatment of quantitative economics, with a focus on data science. The book introduces the reader to R and RStudio, and uses expert Hadley Wickham’s tidyverse package for different parts of the data analysis workflow. After a gentle introduction to R code, the reader’s R skills are gradually honed, with the help of “your turn” exercises. At the heart of data science is data, and the book equips the reader to import and wrangle data, (including network data). Very early on, the reader will begin using the popular ggplot2 package for visualizing data, even making basic maps. The use of R in understanding functions, simulating difference equations, and carrying out matrix operations is also covered. The book uses Monte Carlo simulation to understand probability and statistical inference, and the bootstrap is introduced. Causal inference is illuminated using simulation, data graphs, and R code for applications with real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables. The interplay of growth related data and models is presented, before the book introduces the reader to time series data analysis with graphs, simulation, and examples. Lastly, two computationally intensive methods—generalized additive models and random forests (an important and versatile machine learning method)—are introduced intuitively with applications. The book will be of great interest to economists—students, teachers, and researchers alike—who want to learn R. It will help economics students gain an intuitive appreciation of applied economics and enjoy engaging with the material actively, while also equipping them with key data science skills.

Download or read book Tidy Finance with R written by Christoph Scheuch and published by CRC Press. This book was released on 2023-04-05 with total page 275 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with R, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using the tidyverse family of R packages. We then provide the code to prepare common open source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques. Highlights 1. Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. 2. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copy-pasting the code we provide. 3. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. 4. Chapter 2 on accessing and managing financial data shows how to retrieve and prepare the most important datasets in the field of financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most relevant data characteristics. 5. Each chapter provides exercises that are based on established lectures and exercise classes and which are designed to help students to dig deeper. The exercises can be used for self-studying or as a source of inspiration for teaching exercises.

Download or read book An Introduction to R for Quantitative Economics written by Vikram Dayal and published by Springer. This book was released on 2015-03-17 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives an introduction to R to build up graphing, simulating and computing skills to enable one to see theoretical and statistical models in economics in a unified way. The great advantage of R is that it is free, extremely flexible and extensible. The book addresses the specific needs of economists, and helps them move up the R learning curve. It covers some mathematical topics such as, graphing the Cobb-Douglas function, using R to study the Solow growth model, in addition to statistical topics, from drawing statistical graphs to doing linear and logistic regression. It uses data that can be downloaded from the internet, and which is also available in different R packages. With some treatment of basic econometrics, the book discusses quantitative economics broadly and simply, looking at models in the light of data. Students of economics or economists keen to learn how to use R would find this book very useful.

Download or read book Algorithms and Models for the Web Graph written by Anthony Bonato and published by Springer. This book was released on 2016-11-10 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 13th International Workshop on Algorithms and Models for the Web Graph, WAW 2016, held in Montreal, QC, Canada, in December 2016. The 13 full papers presented in this volume were carefully reviewed and selected from 14 submissions. The workshop gathered the researchers who are working on graph-theoretic and algorithmic aspects of related complex networks, including social networks, citation networks, biological networks, molecular networks, and other networks arising from the Internet.

Download or read book Essentials of Excel VBA Python and R written by John Lee and published by Springer Nature. This book was released on 2023-01-02 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This advanced textbook for business statistics teaches, statistical analyses and research methods utilizing business case studies and financial data, with the applications of Excel VBA, Python and R. Each chapter engages the reader with sample data drawn from individual stocks, stock indices, options, and futures. Now in its second edition, it has been expanded into two volumes, each of which is devoted to specific parts of the business analytics curriculum. To reflect the current age of data science and machine learning, the used applications have been updated from Minitab and SAS to Python and R, so that readers will be better prepared for the current industry. This first volume is designed for advanced courses in financial statistics, investment analysis and portfolio management. It is also a comprehensive reference for active statistical finance scholars and business analysts who are looking to upgrade their toolkits. Readers can look to the second volume for dedicated content on financial derivatives, risk management, and machine learning.

Download or read book Modern Graph Theory Algorithms with Python written by Colleen M. Farrelly and published by Packt Publishing Ltd. This book was released on 2024-06-07 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key Features Learn how to wrangle different types of datasets and analytics problems into networks Leverage graph theoretic algorithms to analyze data efficiently Apply the skills you gain to solve a variety of problems through case studies in Python Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learn Transform different data types, such as spatial data, into network formats Explore common network science tools in Python Discover how geometry impacts spreading processes on networks Implement machine learning algorithms on network data features Build and query graph databases Explore new frontiers in network science such as quantum algorithms Who this book is for If you’re a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

Download or read book Candlestick Charting written by Michael C. Thomsett and published by Walter de Gruyter GmbH & Co KG. This book was released on 2017-12-18 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investors and traders seek methods to identify reversal and continuation to better time their trades. This applies for virtually everyone, whether employing a swing trading strategy, engaging in options trading, or timing entry and exit to spot bull and bear reversals. Key signals are found in the dozens of candlesticks, combined with technical signals such as gaps and moves outside of the trading range; size of wicks (shadows) and size of real bodies. The science of candlestick analysis has a proven track record not only from its inception in 17th century Japan, but today as well. This book explains and demonstrates candlestick signals, including both the appearance of each but in context on an actual stock chart. It further takes the reader through the rationale of reversal and continuation signals and demonstrates the crucial importance of confirmation (in the form of other candlesticks, traditional technical signals, volume, momentum and moving averages). Michael C. Thomsett is a market expert, author, speaker and coach. His many books include Mathematics of Options, Real Estate Investor’s Pocket Calculator, and A Technical Approach to Trend Analysis. A video of the author titled "Candlesticks for Option Timing" can be found here: https://www.youtube.com/watch?v=IItH6OLh7TI