### 统计代写|r语言作业代写代做|What is Reproducible Finance

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

statistics-lab™ 为您的留学生涯保驾护航 在代写r语言方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写r语言代写方面经验极为丰富，各种代写r语言相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|r语言作业代写代考|What is Reproducible Finance

Reproducible finance is a philosophy about how to do quantitative, data sciencedriven financial analysis. The root of this philosophy is that the data and code that lead to a decision or conclusion should be able to be understood and then replicated in an efficient way. The code itself should tell a clear story when read by a human, just as it tells a clear story when read by a computer. This book applies the reproducible philosophy to $\mathrm{R}$ code for portfolio management.
That reproducible philosophy will manifest itself in how we tackle problems throughout this book. More specifically, instead of looking for the most clever code or smartest algorithm, this book prioritizes readable, reusable, reproducible work flows using a variety of $R$ packages and functions. We will frequently solve problems in different ways, writing code from different packages and using different data structures to arrive at the exact same conclusion. To repeat, we will solve the same problems in a multitude of ways with different coding paradigms.

The motivation for this masochistic approach is for the reader to become comfortable working with different coding paradigms and data structures. Our goal is to be fluent or at least conversational to the point that we can collaborate with a variety of $\mathrm{R}$ coders, understand their work and make our work understandable to them. It’s not enough that our work be reproducible for ourselves and other humans who possess our exact knowledge and skills. We want our work to be reproducible and reusable by a broad population of data scientists and quants.

## 统计代写|r语言作业代写代考|Three Universes

This book focuses on three universes or paradigms for portfolio analysis with R. There are probably more than three fantastic paradigms but these are the three I encounter most frequently in industry.
xts
The first universe is what I call the xts world. xts is both a package and a type of object. xts stands for extensible time series. Most of our work in this

book will be with time series, and indeed most financial work involves time series. An xts object is a matrix, that also, always, has a time index for the order of the data. It holds a time series, meaning it holds the observations and the times at which they occurred. An interesting feature of an xts object is that it holds dates in an index column. In fact that index column is considered column number zero, meaning it is not really a column at all. If we have an object called financial_data and wanted to access the dates, we would use index (financial_data).

Why is the date index not given its own column? Because it is impossible to have an xts object but not have a date index. If the date index were its own column, that would imply that it could be deleted or removed.

In the xts world, there are two crucial packages that we will use: quantmod and PerformanceAnalytics. quantmod is how we will access the internet and pull in pricing data. That data will arrive to us formatted as an xts object.
PerformanceAnalytics, as the name implies, has several useful functions for analyzing portfolio performance in an xts object, such as StdDev(), SharpeRatio(), SortinoRatio(), CAPM. Beta(). We will make use of this package in virtually all of the chapters.
The second universe is known throughout the $\mathrm{R}$ community as the ‘tidyverse’. The tidyverse is a collection of $\mathrm{R}$ packages for doing data science in a certain way. It is not specific to financial services and is not purpose built for time series analysis.

## 统计代写|r语言作业代写代考|Data Visualization

Data visualization is where we translate numbers into shapes and colors, and it will get a lot of attention in this book. We do this work so that humans who do not wish to dig into our data and code can still derive value from what we do. This human communication is how our quiet quantitative toiling becomes a transcendent revenue generator or alpha-producing strategy, Even if we plan to implement algorithms and never share our work outside of our own firm, the ability to explain and communicate is hugely important.

To the extent that clients, customers, partners, bosses, portfolio managers and anyone else want actionable insights from us, data visualizations will most certainly be more prominent in the discussion than the nitty gritty of code, data or even statistics. I will emphasize data visualization throughout the book and implore you to spend as much or more time on data visualizations as you do on the rest of quantitative finance.

When we visualize our results, object structure will again play a a role. We will generally chart xts objects using the highcharter package and tidy objects using the ggplot2 package.
highcharter is an $\mathrm{R}$ package but Highcharts is a JavaScript library – the a

$\mathrm{R}$ package is a hook into the JavaScript library. Highcharts is fantastic for visualizing time series and it comes with great built-in widgets for viewing different time frames. I highly recommend it for visualizing financial time series but you do need to buy a license to use it in a commercial setting.
www.highcharts.com and cran.r-project.org/web/packages/highcharter/highcharter.pdf
ggplot2 is itself part of the tidyverse and as such it works best when data is tidy (we will cover what that word ‘tidy’ means when applied to a data object). It is one of the most popular data visualization packages in the $R$ world.

## 统计代写|r语言作业代写代考|Three Universes

xts

PerformanceAnalytics，顾名思义，有几个有用的函数用于分析 xts 对象中的投资组合表现，例如 StdDev()、SharpeRatio()、SortinoRatio()、CAPM。贝塔（）。我们将在几乎所有章节中使用这个包。

## 统计代写|r语言作业代写代考|Data Visualization

highcharter 是一个R包，但 Highcharts 是一个 JavaScript 库——一个

Rpackage 是 JavaScript 库的一个钩子。Highcharts 非常适合可视化时间序列，它带有用于查看不同时间范围的出色内置小部件。我强烈推荐它用于可视化金融时间序列，但您确实需要购买许可证才能在商业环境中使用它。

www.highcharts.com 和 cran.r-project.org/web/packages/highcharter/highcharter.pdf
ggplot2 本身就是 tidyverse 的一部分，因此它在数据整洁时效果最好（我们将介绍“整洁”一词表示应用于数据对象时）。它是目前最流行的数据可视化包之一R世界。

## 广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。