### 统计代写|r语言作业代写代做|Asset Prices to Returns

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## 统计代写|r语言作业代写代考|Converting Daily Prices to Monthly Returns in the xts world

Next we will convert daily prices to monthly log returns.
I mentioned in the introduction that we would be working in three universes xts, tidyverse and tidyquant – the prices object is an xts, so we will start there.

The first observation in our prices object is December 31,2012 (the last trading day of that year) and we have daily prices. We want to convert to those daily prices to monthly log returns based on the last reading of each month.

We will use to. monthly (prices, indexAt = “last”, OHLC = FALSE) from the quantmod package. The argument indexAt = “lastof” tells the function whether we want to index to the first day of the month or the last day. If we wanted to use the first day, we would change it to indexAt = “firstof”.

We have moved from an xts object of daily prices to an xts object of monthly
2.2 Converting Daily Prices to Monthly Returns in the xts world
13 prices. Note that we now have one reading per month, for the last day of each month.

Now we call Return. calculate(prices_monthly, method $=$ “log”) to convert to returns and save as an object called asset_returns_xts. Note this will give us log returns by the method $=$ “log” argument. We could have used method $=$ “discrete” to get simple returns.

## 统计代写|r语言作业代写代考|Converting Daily Prices to Monthly Returns in the tidyverse

We now take the same raw prices object and convert it to monthly returns using the tidyverse. We are leaving the xts structure and converting to a data frame. There are several differences between an xts object and a tibble but a very important one is the date, an essential component of most financial analysis. xts objects have a date index. In contrast, data frames have a date column, and that column is not necessary to the existence of the data frame. We could create a data frame that does not have a date column, for example, to hold cross-sectional data. An xts object, by definition, has a date index.
Our conversion from xts to a tibble starts with data.frame(date = index (.)), which (i) coerces our object into a data frame and (ii) adds a date column based on the index with date $=$ index (.)). The date index of the xts object is preserved as row names and must be explicitly removed with remove_rownames ().

Next we turn to the gather() function from the tidyr package to convert our new data frame into long format. We have not done any calculations yet, we have only shifted from wide format, to long, tidy format.

Next, we want to calculate log returns and add those returns to the data frame. We will use mutate and our own calculation to get log returns: mutate (returns = $(\log ($ prices $)$ – log(lag(prices)))). We now have log returns and will not need the raw prices data. It can be removed with select (-prices). The select() function will select columns to keep, but if we had a negative sign, it will remove a column.

Our last two steps are to spread the data back to wide format, which makes it easier to compare to the xts object and easier to read, but is not a best practice in the tidyverse. We are going to look at this new object and compare to the $x t s$ object above, so we will stick with wide format for now.

Finally, we want to reorder the columns to align with how we first imported the data using the symbols vector – the first line of code we ran in this chapter.

## 统计代写|r语言作业代写代考|Converting Daily Prices to Monthly Returns in the tidyquant world

Let’s explore the tidyquant paradigm for converting to log returns. We will start with the very useful tk_tbl() function from the timetk package.

In the piped workflow below, our call to tk_tbl (preserve_index = TRUE, rename_index = “date”) (1) converts prices from xts to tibble, (2) con-

verts the date index to a date column, and (3) renames it as “date” (since the index is being converted, it does not need to be removed as we did above).
Next, instead of using to. monthly and mutate, and then supplying our own calculation, we use tq_transmute (mutate_fun = periodReturn, period = “monthly”, type = “log”) and go straight from daily prices to monthly log returns.

Once again, we needed to remove the first row and did so with slice (-1), which removed the first row (if we had used slice(1), we would have $k e p t$ just the first row).

That tidyquant method produced the same output as the tidyverse method a tibble of monthly log returns.

## 统计代写|r语言作业代写代考|Converting Daily Prices to Monthly Returns in the xts world

2.2 将每日价格转换为 xts 世界
13 价格中的每月收益的 xts 对象。请注意，我们现在每个月的最后一天都有一个读数。

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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。