如何利用R从雅虎财经中榨取财务资金

Question

我有兴趣使用R分析来自Yahoo Finance的多个代码的余额,收入和现金流量表.

我已经看到有R套件从雅虎财经中提取信息,但我看到的所有例子都涉及历史股价信息.有没有办法可以使用R从这些语句中提取历史信息？

例如,对于Apple(AAPL),可检索链接如下:

• https://finance.yahoo.com/quote/AAPL/financials?p=AAPL
• https://finance.yahoo.com/quote/AAPL/balance-sheet?p=AAPL
• https://finance.yahoo.com/quote/AAPL/cash-flow?p=AAPL

实质上,目标是创建三个数据框(AAPL_cashflow,AAPL_income&AAPL_balance),它们与网站上的模式相同.每行由财务类型标识,列为日期.

有没有人有解析和刮表的经验？我认为这rvest有助于此,对吗？

提前致谢!

Answer 1

有了一些来自的包tidyverse,这应该让你开始:

library(tidyverse)
library(rvest)

"https://finance.yahoo.com/quote/AAPL/financials?p=AAPL" %>% 
  read_html() %>% 
  html_table() %>% 
  map_df(bind_cols) %>% 
  as_tibble()

# A tibble: 28 x 5
   X1                                 X2                 X3                 X4                 X5      
   <chr>                              <chr>              <chr>              <chr>              <chr>   
 1 Revenue                            9/30/2017          9/24/2016          9/26/2015          9/27/20…
 2 Total Revenue                      229,234,000        215,639,000        233,715,000        182,795…
 3 Cost of Revenue                    141,048,000        131,376,000        140,089,000        112,258…
 4 Gross Profit                       88,186,000         84,263,000         93,626,000         70,537,…
 5 Operating Expenses                 Operating Expenses Operating Expenses Operating Expenses Operati…
 6 Research Development               11,581,000         10,045,000         8,067,000          6,041,0…
 7 Selling General and Administrative 15,261,000         14,194,000         14,329,000         11,993,…
 8 Non Recurring                      -                  -                  -                  -       
 9 Others                             -                  -                  -                  -       
10 Total Operating Expenses           167,890,000        155,615,000        162,485,000        130,292…
# ... with 18 more rows

请注意,如果要获取第一行并将其视为列名,请添加header = TRUE到该html_table调用.例如,这将为finances数据框中的日期提供列名称.

此外,此数据框内有多个表,因此您需要对其进行整形以便使用数据.例如,当var 应为数字类型时,var X2through X5当前是字符.

一个例子可能是:

finances <- "https://finance.yahoo.com/quote/AAPL/financials?p=AAPL" %>% 
  read_html() %>% 
  html_table(header = TRUE) %>% 
  map_df(bind_cols) %>% 
  as_tibble()

finances %>% 
  mutate_all(funs(str_replace_all(., ",", ""))) %>% 
  mutate_all(funs(str_replace(., "-", NA_character_))) %>%
  mutate_at(vars(-Revenue), funs(str_remove_all(., "[a-zA-Z]"))) %>% 
  mutate_at(vars(-Revenue), funs(as.numeric)) %>% 
  drop_na()

# A tibble: 14 x 5
   Revenue                                `9/30/2017` `9/24/2016` `9/26/2015` `9/27/2014`
   <chr>                                        <dbl>       <dbl>       <dbl>       <dbl>
 1 Total Revenue                           229234000.  215639000.  233715000.  182795000.
 2 Cost of Revenue                         141048000.  131376000.  140089000.  112258000.
 3 Gross Profit                             88186000.   84263000.   93626000.   70537000.
 4 Research Development                     11581000.   10045000.    8067000.    6041000.
 5 Selling General and Administrative       15261000.   14194000.   14329000.   11993000.
 6 Total Operating Expenses                167890000.  155615000.  162485000.  130292000.
 7 Operating Income or Loss                 61344000.   60024000.   71230000.   52503000.
 8 Total Other Income/Expenses Net           2745000.    1348000.    1285000.     980000.
 9 Earnings Before Interest and Taxes       61344000.   60024000.   71230000.   52503000.
10 Income Before Tax                        64089000.   61372000.   72515000.   53483000.
11 Income Tax Expense                       15738000.   15685000.   19121000.   13973000.
12 Net Income From Continuing Ops           48351000.   45687000.   53394000.   39510000.
13 Net Income                               48351000.   45687000.   53394000.   39510000.
14 Net Income Applicable To Common Shares   48351000.   45687000.   53394000.   39510000.

我们可以更进一步,使用以下内容使数据框架更加"整洁" gather:

finances %>% 
  mutate_all(funs(str_replace_all(., ",", ""))) %>% 
  mutate_all(funs(str_replace(., "-", NA_character_))) %>%
  mutate_at(vars(-Revenue), funs(str_remove_all(., "[a-zA-Z]"))) %>% 
  mutate_at(vars(-Revenue), funs(as.numeric)) %>% 
  drop_na() %>% 
  gather(key = "date", value, -Revenue) %>% 
  mutate(date = lubridate::mdy(date)) %>% 
  rename("var" = Revenue) %>% 
  as_tibble()

# A tibble: 56 x 3
   var                                date            value
   <chr>                              <date>          <dbl>
 1 Total Revenue                      2017-09-30 229234000.
 2 Cost of Revenue                    2017-09-30 141048000.
 3 Gross Profit                       2017-09-30  88186000.
 4 Research Development               2017-09-30  11581000.
 5 Selling General and Administrative 2017-09-30  15261000.
 6 Total Operating Expenses           2017-09-30 167890000.
 7 Operating Income or Loss           2017-09-30  61344000.
 8 Total Other Income/Expenses Net    2017-09-30   2745000.
 9 Earnings Before Interest and Taxes 2017-09-30  61344000.
10 Income Before Tax                  2017-09-30  64089000.
# ... with 46 more rows