### 统计代写|r语言作业代写代做|Standard Deviation

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|Rolling Standard Deviation

We have calculated the average volatility for the entire life of the portfolio but it would help if we could better understand how that volatility has changed over time or behaved in different market conditions.

We might miss a 3 -month or 6-month period where the volatility spiked or plummeted or did both. And the longer our portfolio life, the more likely we are to miss something important. If we had 10 or 20 years of data and we calculated the standard deviation for the entire history, we could, or most certainly would, fail to notice a period in which volatility was very high, and hence we would fail to ponder the probability that it could occur again.

Imagine a portfolio which had a standard deviation of returns for each 6-month period of $3 \%$ and it never changed. Now imagine a portfolio whose volatility fluctuated every few 6 -month periods from $0 \%$ to $6 \%$. We might find a $3 \%$ standard deviation of monthly returns over a 10-year sample for both, but those two portfolios are not exhibiting the same volatility. The rolling volatility of each would show us the differences and then we could hypothesize about the past causes and future probabilities for those differences. We might also want to think about dynamically rebalancing our portfolio to better manage volatility if we are seeing large spikes in the rolling windows.

## 统计代写|r语言作业代写代考|Rolling Standard Deviation with the tidyverse and tibbletime

Tibbletime and its rollify() function are built for time series analysis and allow us to solve this problem.

We use rollify () to define a rolling standard deviation function. We want to roll the sd() function with a width equal to window so we define sd_roll_24 $<-\operatorname{rollify}(\mathrm{sd}$, window $=$ window $)$. sd_roll_24 <- rollify $(\mathrm{sd}$, window $=$ window $)$ Then we use mutate () to pass it into the code flow. Note that we convert our tibble to a tibbletime data frame with as_tbl_time (index = date). port_rolling_sd_tidy_tibbletime <- portfolio_returns_tq_rebalanced_monthly $\%>\%$
as_tbl_time (index = date) $\%>\%$
mutate(sd = sd_roll_24 (returns)) $\%>\%$
select(-returns) $\%>\%$
na. omit()
tail (port_rolling_sd_tidy_tibbletime, 3)

$\begin{array}{lr}\text { date } & \text { sd } \ \langle\text { dates } & \langle\text { dbl> }\end{array}$
port_rolling_sd_tidy_tibbletime <- portfolio_returns_tq_rebalanced_monthly $\%>\%$
as_tbl_time(index=_ate) $\%>\%$
mutate(sd = sd_roll_24(returns)) $\%>\%$
select(-returns) $\%>\%$
na.omit()
tail (port_rolling_sd_tidy_tibbletime, 3)

date
cdate>
1 sdbl-10-31 $0.0234$
2 2017-11-30 $0.0233$
3 2017-12-31 $0.0217$
$12017-10-310.0234$
2 2017-11-30 0.0233
$3 \quad 2017-12-31 \quad 0.0217$
That nifty combination of the tidyverse and tibbletime is generalizable to other functions beyond standard deviation. Tibbletime is changing and improving rapidly as of the time of this writing (Spring of 2018). We will keep an eye on the package and post new use cases to the website as things develop. Stay tuned!

## 统计代写|r语言作业代写代考|Rolling Standard Deviation in the tidyquant world

The tidyquant package has a nice way to apply a rolling function to data frames as well. We take tq_mutate() and supply mutate_fun = rollapply as our mutation function argument. Then, we invoke FUN $=$ sd as the nested function beneath rollapply ().
port_rolling_sd_tq <- portfolio_returns_tq_rebalanced_monthly $\%>\%$
tq_mutate(mutate_fun = rollapply,
width = window,
FUN = sd,
col_rename = “rolling_sd”) $\%>\%$
select(date, rolling_sd) $\%>\%$
na. omit()
Take a quick peek to confirm consistent results.
port_rolling_sd_tidy_tibbletime $\%>\%$
mutate(sd_tq = port_rolling_sd_tq\$rolling_sd, sd_xts = round(port_rolling_sd_xts \$rolling_sd, 4)) $\%>\%$
$\operatorname{tail}(3)$

date sd sd_tq sd_xts
$\langle$ date $\rangle\langle\mathrm{dbl}\rangle\langle\mathrm{dbl}\rangle\langle\mathrm{S} 3: \mathrm{xts}\rangle$
$12017-10-31 \quad 0.02340 .02340 .0234$
$\begin{array}{llllll}2 & 2017-11-30 & 0.0233 & 0.0233 & 0.0233\end{array}$
$\begin{array}{llllll}3 & 2017-12-31 & 0.0217 & 0.0217 & 0.0217\end{array}$
We now have an xts object called port_rolling_sd_xts, a tibbletime
tibble called port_rolling_sd_tidy_tibbletime and a tibble object
called port_rolling_sd_tq. Each contains the 24 -month rolling standard
deviation of portfolio returns.
At the outset of this section, we opined that rolling volatility might add some insight that is obscured by the total volatility. Visualizing the rolling standard deviation should help to illuminate this and that is where we head next.

## 统计代写|r语言作业代写代考|Rolling Standard Deviation with the tidyverse and tibbletime

Tibbletime 及其 rollify() 函数是为时间序列分析而构建的，可以让我们解决这个问题。

as_tbl_time（索引 = 日期）%>%

日期  sd  ⟨ 日期 ⟨ 数据库>
port_rolling_sd_tidy_tibbletime <-portfolio_returns_tq_rebalanced_monthly%>%
as_tbl_time(index=_ate)%>%

na.omit()

cdate>
1 sdbl-10-310.0234
2 2017-11-30 0.0233
3 2017-12-31 0.0217
12017−10−310.0234
2 2017-11-30 0.0233
32017−12−310.0217
tidyverse 和 tibbletime 的巧妙组合可以推广到标准偏差之外的其他函数。截至撰写本文时（2018 年春季），Tibbletime 正在迅速变化和改进。随着事情的发展，我们将密切关注该软件包并在网站上发布新的用例。敬请关注！

## 统计代写|r语言作业代写代考|Rolling Standard Deviation in the tidyquant world

tidyquant 包也有一个很好的方法来将滚动函数应用于数据帧。我们采用 tq_mutate() 并提供 mutate_fun = rollapply 作为我们的变异函数参数。然后，我们调用 FUN=sd 作为 rollapply () 下的嵌套函数。
port_rolling_sd_tq <-portfolio_returns_tq_rebalanced_monthly%>%
tq_mutate(mutate_fun = rollapply,
width = window,
FUN = sd,
col_rename = “rolling_sd”)%>%

port_rolling_sd_tidy_tibbletime%>%

⟨日期⟩⟨dbl⟩⟨dbl⟩⟨小号3:X吨s⟩
12017−10−310.02340.02340.0234
22017−11−300.02330.02330.0233
32017−12−310.02170.02170.0217

tibble 和一个

## 广义线性模型代考

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## 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。