将列添加到每个Quantmod符号

Question

学习R，不知道如何解决这个问题。

library(quantmod) 
library(xts) 

# get market data 
Nasdaq100_Symbols <- c("AAPL", "AAL") 
getSymbols(Nasdaq100_Symbols) 

# merge them together 
nasdaq100 <- data.frame(as.xts(merge(AAPL, AAL))) 
#tail(nasdaq100[,1:12],2) 

#make percent difference column 
nasdaq100$PD <- (((nasdaq100$AAPL.High - nasdaq100$AAPL.Open)/nasdaq100$AAPL.Open) * 100) 

我想添加一个百分比差异列，但上面的代码将只为AAPL符号（或任何符号使用），而不是为每个符号PD列工作。

您是否必须在与xts合并之前以某种方式添加该列，或者我可以告诉R为新合并框架中的每个符号创建它？

编辑：我做数据的训练，所以我需要的所有符号标题，如：

  AAPL.Ope AAPL.High AAPL.Volume AAL.Open AAL.High 

1/3/2007 86.29 86.58  309579900 53.89 56.92 
1/4/2007 84.05 85.95  211815100 56.3 59.15 
1/5/2007 85.77 86.2  208685400 58.83 59.15 

Answer 1

以我的经验，它通常更有道理，让您的财务数据为xts对象，为今后的操作与其他技术指标等，除非你打算运行在预测模型说caret在这种情况下转换为data.frame可能是有道理的。

考虑保持数据的符号作为容器的元素，如

update_sym_md <- function(sym, env = .GlobalEnv) { 
    x <- get(sym, env) 
    pd <- setNames((Hi(x) - Op(x))/Op(x), "PD") 
    merge(x, pd) 
} 

# Adjust env for location of xts symbol data 
l.syms <- lapply(Nasdaq100_Symbols, update_sym_md, env = .GlobalEnv) 

lapply(l.syms, head) 
# [[1]] 
# AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted   PD 
# 2007-01-03  86.29  86.58 81.90  83.80 309579900  10.85709 0.003360760 
# 2007-01-04  84.05  85.95 83.82  85.66 211815100  11.09807 0.022605556 
# 2007-01-05  85.77  86.20 84.40  85.05 208685400  11.01904 0.005013373 
# 2007-01-08  85.96  86.53 85.28  85.47 199276700  11.07345 0.006630991 
# 2007-01-09  86.45  92.98 85.15  92.57 837324600  11.99333 0.075534942 
# 2007-01-10  94.75  97.80 93.45  97.00 738220000  12.56728 0.032190006 
# 
# [[2]] 
# AAL.Open AAL.High AAL.Low AAL.Close AAL.Volume AAL.Adjusted   PD 
# 2007-01-03 53.89 56.92 53.89  56.30 2955600  54.80361 0.0562256273 
# 2007-01-04 56.30 59.15 53.65  58.84 2614500  57.27610 0.0506217238 
# 2007-01-05 58.83 59.15 57.90  58.29 1656300  56.74072 0.0054394015 
# 2007-01-08 57.30 60.48 57.04  57.93 2163200  56.39028 0.0554974006 
# 2007-01-09 59.44 60.20 57.56  57.90 2098600  56.36108 0.0127860366 
# 2007-01-10 60.03 60.04 57.34  58.93 3892200  57.36371 0.0001666167 

另外，如果你想价格的回报/原料价格全面符号比较一个XTS对象，而不是在数据.frame，你可能会发现qmao包有用。

例如：

install.packages("qmao", repos="http://R-Forge.R-project.org", type = "source") 
library(qmao) 

pf <- makePriceFrame(Nasdaq100_Symbols) 
head(pf, 3) 
#    AAPL  AAL 
# 2007-01-03 10.85709 54.80361 
# 2007-01-04 11.09807 57.27610 
# 2007-01-05 11.01904 56.74072 
rf <- makeReturnFrame(Nasdaq100_Symbols) 
head(rf) 

#     AAPL   AAL 
# 2007-01-03   NA   NA 
# 2007-01-04 0.021952895 0.0441273684 
# 2007-01-05 -0.007146715 -0.0093913155 
# 2007-01-08 0.004926208 -0.0061951917 
# 2007-01-09 0.079799692 -0.0005179716 
# 2007-01-10 0.046745798 0.0176329011 

更新响应从OP评论：

要加入所有的数据到一行，试试这个：

（旁白：如果你”我们将在这个数据框架上使用非线性预测模型，请确保您考虑首先在每一行的证券中缩放您的数据点。）

x.cbind <- do.call(cbind, l.syms) 
head(x.cbind) 
# AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted   PD AAL.Open AAL.High AAL.Low AAL.Close AAL.Volume AAL.Adjusted   PD.1 
# 2007-01-03  86.29  86.58 81.90  83.80 309579900  10.85709 0.003360760 53.89 56.92 53.89  56.30 2955600  54.80361 0.0562256273 
# 2007-01-04  84.05  85.95 83.82  85.66 211815100  11.09807 0.022605556 56.30 59.15 53.65  58.84 2614500  57.27610 0.0506217238 
# 2007-01-05  85.77  86.20 84.40  85.05 208685400  11.01904 0.005013373 58.83 59.15 57.90  58.29 1656300  56.74072 0.0054394015 
# 2007-01-08  85.96  86.53 85.28  85.47 199276700  11.07345 0.006630991 57.30 60.48 57.04  57.93 2163200  56.39028 0.0554974006 
# 2007-01-09  86.45  92.98 85.15  92.57 837324600  11.99333 0.075534942 59.44 60.20 57.56  57.90 2098600  56.36108 0.0127860366 
# 2007-01-10  94.75  97.80 93.45  97.00 738220000  12.56728 0.032190006 60.03 60.04 57.34  58.93 3892200  57.36371 0.0001666167 

df.cbind <- data.frame("time" = index(x.cbind), coredata(x.cbind)) 
head(df.cbind) 
# time AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted   PD AAL.Open AAL.High AAL.Low AAL.Close AAL.Volume AAL.Adjusted   PD.1 
# 1 2007-01-03  86.29  86.58 81.90  83.80 309579900  10.85709 0.003360760 53.89 56.92 53.89  56.30 2955600  54.80361 0.0562256273 
# 2 2007-01-04  84.05  85.95 83.82  85.66 211815100  11.09807 0.022605556 56.30 59.15 53.65  58.84 2614500  57.27610 0.0506217238 
# 3 2007-01-05  85.77  86.20 84.40  85.05 208685400  11.01904 0.005013373 58.83 59.15 57.90  58.29 1656300  56.74072 0.0054394015 
# 4 2007-01-08  85.96  86.53 85.28  85.47 199276700  11.07345 0.006630991 57.30 60.48 57.04  57.93 2163200  56.39028 0.0554974006 
# 5 2007-01-09  86.45  92.98 85.15  92.57 837324600  11.99333 0.075534942 59.44 60.20 57.56  57.90 2098600  56.36108 0.0127860366 
# 6 2007-01-10  94.75  97.80 93.45  97.00 738220000  12.56728 0.032190006 60.03 60.04 57.34  58.93 3892200  57.36371 0.0001666167 

为了更好地理解qmao函数是如何工作的，为什么不看文档中的例子并从那里开始呢？ ?makeReturnFrame看看源代码，以真正了解发生了什么事情（和学习良好的编码风格成为在同一时间提供更好的[R程序员）

Answer 2

当我使用quantmod小号getSymbols功能我最常做的，是我自己写的小包装功能，沿着这样的：

library(quantmod) 
# 1. write the wrapper function 
my_wrapper <- function(tickers, from, to) { 
    result_list <- lapply(tickers, function(ticker) { 
    tmp <- getSymbols(ticker, from = from, to = to, auto.assign = F) 
    tmp_df <- data.frame(date = index(tmp), 
         ticker = ticker, 
         open = as.numeric(Op(tmp)), 
         high = as.numeric(Hi(tmp)), 
         close = as.numeric(Cl(tmp)), 
         adj = as.numeric(Ad(tmp))) 
    }) 

    result_df <- do.call(rbind, result_list) 
    return(result_df) 
} 

# 2. download and inspect the data 
my_df <- my_wrapper(c("AAPL", "AAL"), from = "2010-01-01", to = "2016-12-31") 
summary(my_df) 
# date    ticker   open    high   close    adj   
# Min. :2010-01-04 AAPL:1762 Min. : 3.99 Min. : 4.06 Min. : 4.00 Min. : 3.894 
# 1st Qu.:2011-09-30 AAL :1762 1st Qu.: 17.16 1st Qu.: 17.46 1st Qu.: 17.23 1st Qu.: 16.770 
# Median :2013-07-04    Median : 72.94 Median : 73.45 Median : 73.02 Median : 41.920 
# Mean :2013-07-02    Mean :168.48 Mean :170.10 Mean :168.40 Mean : 49.208 
# 3rd Qu.:2015-04-06    3rd Qu.:318.11 3rd Qu.:320.39 3rd Qu.:318.23 3rd Qu.: 72.969 
# Max. :2016-12-30    Max. :702.41 Max. :705.07 Max. :702.10 Max. :127.966 

然后，计算我会建议使用dplyr或data.table或其他一些数据帧manipulat的差异离子包。这里我使用dplyr。

# 3. Calculate the difference using dplyr 
library(dplyr) 

my_rets <- my_df %>% group_by(ticker) %>% mutate(pd = (high - open)/open) 

my_rets 
# Source: local data frame [3,524 x 7] 
# Groups: ticker [2] 
# 
#   date ticker open high close  adj   pd 
#  <date> <fctr> <dbl> <dbl> <dbl> <dbl>  <dbl> 
# 1 2010-01-04 AAPL 213.43 214.50 214.01 27.72704 0.0050133440 
# 2 2010-01-05 AAPL 214.60 215.59 214.38 27.77498 0.0046132153 
# 3 2010-01-06 AAPL 214.38 215.23 210.97 27.33318 0.0039649549 
# 4 2010-01-07 AAPL 211.75 212.00 210.58 27.28265 0.0011806659 
# 5 2010-01-08 AAPL 210.30 212.00 211.98 27.46403 0.0080837473 
# 6 2010-01-11 AAPL 212.80 213.00 210.11 27.22176 0.0009398731 
# 7 2010-01-12 AAPL 209.19 209.77 207.72 26.91211 0.0027725991 
# 8 2010-01-13 AAPL 207.87 210.93 210.65 27.29172 0.0147206905 
# 9 2010-01-14 AAPL 210.11 210.46 209.43 27.13366 0.0016657655 
# 10 2010-01-15 AAPL 210.93 211.60 205.93 26.68020 0.0031764188 
# # ... with 3,514 more rows 

P.你在这里找到一个很好的dplyr介绍：https://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html

Answer 3

我想补充使用比较新的和有趣的包装tidyquant一个解决方案非常适合这样的任务。您可以使用所有Tidyverse的东西，并且可以利用xt，quantmod和TTR的定量功能！看看有很多例子的vignette。

library(tidyquant) 
c("AAPL", "AAL") %>% 
    tq_get(get = "stock.prices") %>% 
    group_by(symbol.x) %>% 
    tq_mutate(ohlc_fun = OHLCV, mutate_fun = OpCl,col_rename='diff') %>% 
    select(- c(low,volume)) # I deselect low and volume to show the added colum ‘diff' 

Source: local data frame [5,110 x 7] 
Groups: symbol.x [2] 

    symbol.x  date open high close adjusted   diff 
     <chr>  <date> <dbl> <dbl> <dbl> <dbl>  <dbl> 
1  AAPL 2007-01-03 86.29 86.58 83.80 10.85709 0.0033607603 
2  AAPL 2007-01-04 84.05 85.95 85.66 11.09807 0.0226055559 
3  AAPL 2007-01-05 85.77 86.20 85.05 11.01904 0.0050133730 
4  AAPL 2007-01-08 85.96 86.53 85.47 11.07345 0.0066309913 
5  AAPL 2007-01-09 86.45 92.98 92.57 11.99333 0.0755349424 

UPDATE：有人问到符号添加到列名

假设你保存上述数据帧到变量stocks：

lapply(unique(stocks$symbol.x), function(x) stocks[stocks$symbol.x == x,]) %>% 
lapply(function(x) { 
    names(x) <- paste0(x$symbol.x[1],'.',colnames(x)) 
    x 
}) 
[[1]] 
Source: local data frame [2,555 x 7] 
Groups: symbol.x [1] 

    AAPL.symbol.x AAPL.date AAPL.open AAPL.high AAPL.close 
      <chr>  <date>  <dbl>  <dbl>  <dbl> 
1   AAPL 2007-01-03  86.29  86.58  83.80 
2   AAPL 2007-01-04  84.05  85.95  85.66 
3   AAPL 2007-01-05  85.77  86.20  85.05 
4   AAPL 2007-01-08  85.96  86.53  85.47 
5   AAPL 2007-01-09  86.45  92.98  92.57 
6   AAPL 2007-01-10  94.75  97.80  97.00 
7   AAPL 2007-01-11  95.94  96.78  95.80 
8   AAPL 2007-01-12  94.59  95.06  94.62 
9   AAPL 2007-01-16  95.68  97.25  97.10 
10   AAPL 2007-01-17  97.56  97.60  94.95 
# ... with 2,545 more rows, and 2 more variables: 
# AAPL.adjusted <dbl>, AAPL.diff <dbl> 

[[2]] 
Source: local data frame [2,555 x 7] 
Groups: symbol.x [1] 

    AAL.symbol.x AAL.date AAL.open AAL.high AAL.close 
      <chr>  <date> <dbl> <dbl>  <dbl> 
1   AAL 2007-01-03 53.89 56.92  56.30 
2   AAL 2007-01-04 56.30 59.15  58.84 
3   AAL 2007-01-05 58.83 59.15  58.29 
4   AAL 2007-01-08 57.30 60.48  57.93 
5   AAL 2007-01-09 59.44 60.20  57.90 
6   AAL 2007-01-10 60.03 60.04  58.93 
7   AAL 2007-01-11 59.18 61.20  61.20 
8   AAL 2007-01-12 61.20 62.50  60.81 
9   AAL 2007-01-16 60.81 62.10  61.96 
10   AAL 2007-01-17 60.96 61.89  58.65 
# ... with 2,545 more rows, and 2 more variables: 
# AAL.adjusted <dbl>, AAL.diff <dbl>