r - R根据股票投资组合的买卖日期提取最高价和最低价

Question

我收到了一个 csv 文件，其中包含对冲基金在 10 月份买卖的 50 种不同的股票代码。我必须检查以确保购买价格在特定的买卖日介于高价和低价之间。

这是格式化的 CSV：

head(hilo)

# A tibble: 6 x 9
  Symbol `Date of Purchase` `Date of Sale` `Purchase price` `Sale Price` open  high  low   close
  <chr>  <date>             <date>                    <dbl>        <dbl> <chr> <chr> <chr> <chr>
1 PCH    2018-10-02         2018-10-03                 40.2         38.4 NA    NA    NA    NA   
2 NBHC   2018-10-03         2018-10-11                 37.8         36.2 NA    NA    NA    NA   
3 STWD   2018-10-08         2018-10-10                 21.3         21.0 NA    NA    NA    NA   
4 RWT    2018-10-08         2018-10-11                 16.1         16   NA    NA    NA    NA   
5 NVEE   2018-10-08         2018-10-10                 84.3         83.0 NA    NA    NA    NA   
6 PRIM   2018-10-08         2018-10-10                 23.5         23.1 NA    NA    NA    NA

然后我使用 getSymbols() 收集符号，将它们格式化为一个列表，然后抓取高低列。

dataEnv <- new.env()

#Get symbol data from Yahoo!
getSymbols(hilo$Symbol, from = min(hilo$`Date of Purchase`, na.rm = TRUE), to = max(hilo$`Date of Sale`, na.rm = TRUE), env = dataEnv)

slist <- as.list(dataEnv)
Yhilo <- xts()
for (i in 1:length(slist)) {
  Yhilo <- cbind(Yhilo, slist[[i]][,2:3])
}

head(Yhilo)
                   CEQP.High CEQP.Low TCP.High TCP.Low CRC.High CRC.Low WPC.High WPC.Low IRTC.High IRTC.Low RWT.High RWT.Low STWD.High STWD.Low NVEE.High NVEE.Low TILE.High TILE.Low
2018-10-01 02:00:00     37.69    36.80    31.17   30.30   49.040  47.850    64.35   63.46     95.50   92.300    16.31   16.08     21.54    21.33    87.300   84.000     23.50    22.28
2018-10-02 02:00:00     37.57    36.80    31.69   30.63   49.417  47.530    64.19   63.26     93.25   91.595    16.29   16.10     21.48    21.30    84.900   83.000     22.47    22.14
2018-10-03 02:00:00     37.47    36.88    31.10   30.49   50.340  48.380    64.15   62.80     92.33   90.212    16.38   16.26     21.56    21.30    83.790   82.620     22.54    22.05
2018-10-04 02:00:00     38.38    37.45    31.23   30.30   50.050  47.660    63.07   62.12     92.72   86.530    16.31   16.14     21.36    21.10    85.600   82.615     22.47    22.06
2018-10-05 02:00:00     38.36    37.61    31.16   30.42   48.368  44.627    63.77   62.90     88.34   83.790    16.22   16.04     21.34    21.03    84.518   82.460     22.54    22.06
2018-10-08 02:00:00     38.20    37.37    31.10   30.57   45.760  44.020    64.21   62.98     85.79   81.739    16.16   16.02     21.30    21.00    84.340   82.399     22.24    21.96
                    AEE.High AEE.Low AAWW.High AAWW.Low ABG.High ABG.Low TREX.High TREX.Low MNK.High MNK.Low ORBK.High ORBK.Low AHL.High AHL.Low AAON.High AAON.Low HURN.High HURN.Low
2018-10-01 02:00:00    63.42   62.70     64.47    61.73    69.78   67.90     77.50    73.72    29.90   29.12     59.92    59.00    41.92   41.65     38.10    36.37     49.64    48.74
2018-10-02 02:00:00    64.47   63.47     62.59    61.83    68.47   66.93     75.50    73.78    30.06   28.89     60.00    58.99    41.86   41.73     36.67    35.35     49.22    48.59
2018-10-03 02:00:00    64.66   63.08     63.23    61.92    67.05   65.70     75.10    73.79    31.41   29.93     60.35    59.31    42.02   41.82     36.06    35.37     49.48    48.35
2018-10-04 02:00:00    64.08   62.87     63.38    61.94    65.71   64.18     74.28    71.90    29.88   25.39     59.76    58.75    41.96   41.80     35.72    34.83     48.69    48.05
2018-10-05 02:00:00    65.29   63.94     61.08    59.44    64.55   62.47     73.42    69.80    27.25   25.12     59.87    58.60    42.16   41.75     35.03    34.01     49.38    48.17
2018-10-08 02:00:00    66.36   65.07     59.84    58.42    63.83   62.46     72.26    70.47    26.45   25.38     59.20    58.45    41.86   41.73     35.14    33.85     50.42    48.58
                    ATHN.High ATHN.Low AGIO.High AGIO.Low LBTYA.High LBTYA.Low AVYA.High AVYA.Low NBHC.High NBHC.Low OUT.High OUT.Low QUAD.High QUAD.Low GTT.High GTT.Low BKD.High
2018-10-01 02:00:00    133.80   126.18     79.17    76.30      29.48     28.02    22.358    21.66     37.80    37.10    20.07   19.82     21.08    20.17    43.96   42.69     9.98
2018-10-02 02:00:00    128.99   125.65     76.65    73.44      28.42     27.95    21.890    21.45     37.35    36.57    20.11   19.83     20.40    19.34    44.64   42.86     9.73
2018-10-03 02:00:00    127.67   125.27     74.28    70.54      28.56     27.22    22.030    21.60     37.83    36.54    20.08   19.44     20.14    19.20    46.22   44.06     9.56
2018-10-04 02:00:00    125.99   122.02     74.00    70.25      27.48     26.78    21.895    21.42     38.22    37.25    19.48   19.17     20.01    19.02    44.87   43.29     9.41
2018-10-05 02:00:00    126.72   121.95     72.05    67.96      27.19     26.23    21.980    21.27     37.88    36.92    19.56   19.29     19.21    18.50    45.92   42.27     9.21
2018-10-08 02:00:00    126.32   124.01     69.18    66.24      27.24     26.46    21.790    20.74     37.57    36.92    19.49   19.25     19.49    18.81    43.37   41.45     9.30
                    BKD.Low ATU.High ATU.Low CAKE.High CAKE.Low PCH.High PCH.Low MXL.High MXL.Low CATM.High CATM.Low RBA.High RBA.Low CNC.High CNC.Low CRUS.High CRUS.Low SMTC.High
2018-10-01 02:00:00    9.62    28.48   27.68     53.68    52.36    41.38   39.95    20.20   19.89     32.52    31.47    36.46   35.86   146.41  144.87     38.77    37.81     56.39
2018-10-02 02:00:00    9.35    28.11   27.66     53.19    51.99    40.39   39.33    20.20   19.75     31.94    31.20    36.59   36.10   145.95  144.16     38.44    37.80     55.77
2018-10-03 02:00:00    9.19    28.03   27.71     52.66    51.63    40.20   38.08    19.99   19.35     32.52    30.96    36.48   36.04   145.37  144.29     38.02    37.18     54.82
2018-10-04 02:00:00    9.05    28.10   27.61     52.02    51.20    38.81   37.84    19.79   19.08     32.04    31.43    36.57   36.11   145.90  142.65     38.03    37.35     54.66
2018-10-05 02:00:00    8.70    27.97   27.29     52.56    51.55    38.91   38.38    19.09   17.89     32.74    31.22    36.67   36.10   144.45  142.66     37.70    35.71     54.45
2018-10-08 02:00:00    8.96    27.55   27.20     52.32    51.35    39.34   38.44    17.79   17.15     33.22    32.11    36.77   36.05   144.33  141.05     36.60    35.38     52.39
                    SMTC.Low AGNC.High AGNC.Low DDD.High DDD.Low NUE.High NUE.Low ATGE.High ATGE.Low ANDE.High ANDE.Low TECH.High TECH.Low PCTY.High PCTY.Low WY.High WY.Low CNK.High
2018-10-01 02:00:00    54.52     18.68    18.48    19.13   17.93    64.83   63.42     48.68    47.77     38.26    37.04    205.74   202.75    81.300   78.730   32.37  31.83    40.46
2018-10-02 02:00:00    54.35     18.74    18.58    18.13   17.16    65.42   64.08     48.33    46.25     37.35    36.66    203.51   200.33    78.830   75.860   32.14  31.56    39.63
2018-10-03 02:00:00    53.80     18.76    18.42    18.51   17.65    65.63   65.04     46.94    46.17     37.70    36.53    204.29   199.34    77.720   75.700   31.93  30.65    39.53
2018-10-04 02:00:00    53.40     18.44    18.22    18.51   17.51    66.03   64.56     46.86    45.26     37.31    36.72    199.20   192.76    77.049   72.871   30.86  30.20    39.46
2018-10-05 02:00:00    51.71     18.34    18.09    19.19   17.72    65.05   63.54     46.17    44.95     37.14    36.34    195.24   190.62    74.200   70.410   30.79  30.18    39.83
2018-10-08 02:00:00    50.91     18.30    18.08    18.03   17.19    64.80   63.77     46.37    45.21     37.59    36.66    192.37   187.11    72.140   67.990   30.96  30.26    40.36
                    CNK.Low PRIM.High PRIM.Low JCOM.High JCOM.Low LOGM.High LOGM.Low ALRM.High ALRM.Low HUN.High HUN.Low
2018-10-01 02:00:00   39.45     25.20    24.23     83.25    81.52     92.89    87.34    60.200    56.67    27.41   26.73
2018-10-02 02:00:00   38.60     24.51    23.98     81.59    78.97     88.69    86.76    56.910    53.79    27.48   26.74
2018-10-03 02:00:00   38.80     24.42    24.08     80.38    79.06     88.44    86.79    56.430    54.55    27.77   26.82
2018-10-04 02:00:00   38.74     24.29    23.66     78.97    77.46     86.78    85.14    56.000    54.84    27.45   26.88
2018-10-05 02:00:00   39.17     23.87    23.26     78.31    76.82     87.45    82.60    53.790    49.27    27.10   25.82
2018-10-08 02:00:00   39.17     23.61    23.05     77.27    75.49     84.09    81.57    51.485    47.93    26.16   25.65

我遇到的麻烦是弄清楚如何从该数据集中提取 hilo 中相应符号的购买和销售日期的高点和低点。

score 0 · Accepted Answer

下面是一些将返回您需要的代码。我正在使用 quantmod、purrr 和 dplyr 作为软件包。注意使用pmapwithgetSymbols来获取数据。在您对每个股票代码使用 getSymbols 时，您将获得相同数量的行。这对于大多数请求来说是多余的，因为在大多数情况下，保留期较短。现在每个股票将只获取购买日期和销售日期之间的数据。

我创建了一个辅助函数来获取购买日期和销售日期的高点和低点。这里的假设是最后可用的记录确实是销售日期。如果不是，请调整此部分。我从 hilo 中删除了 open、high、low 和 close 列，因为本示例不需要这些列。

我将检查购买/销售价格是否在高位和低位之间留给您。

library(quantmod)
library(purrr)
library(dplyr)

# use pmap so 3 columns can correctly be passed to getSymbols    
stocklist <- pmap(list(hilo$Symbol, hilo$Date_of_Purchase, hilo$Date_of_Sale), function(x, y, z) getSymbols(Symbols = x, src = "yahoo", from = y, to = z + 1, auto.assign = FALSE))
names(stocklist) <- hilo$Symbol

# function described below
stock_hilo <- map_dfr(stocklist, hi_lo, hilo = hilo, .id = "Symbol")

hilo %>% 
  left_join(stock_hilo)

Joining, by = "Symbol"
  Symbol Date_of_Purchase Date_of_Sale Purchase_price Sale_Price pu_hi  pu_lo sa_hi sa_lo
1    PCH       2018-10-02   2018-10-03           40.2       38.4 40.39 39.330 40.39 39.33
2   NBHC       2018-10-03   2018-10-11           37.8       36.2 37.83 36.540 37.78 37.07
3   STWD       2018-10-08   2018-10-10           21.3       21.0 21.30 21.000 21.42 21.19
4    RWT       2018-10-08   2018-10-11           16.1       16.0 16.16 16.020 16.39 16.16
5   NVEE       2018-10-08   2018-10-10           84.3       83.0 84.34 82.399 85.50 84.15
6   PRIM       2018-10-08   2018-10-10           23.5       23.1 23.61 23.050 23.65 23.38

代码中使用的函数

hi_lo <- function(stock_data, hilo){
  stock_symbol <- stringi::stri_extract(names(stocklist$PCH)[1], regex = "^[A-Z]+")
  output <- data.frame(matrix(nrow = 1, ncol = 4))
  output <- setNames(output, c("pu_hi", "pu_lo", "sa_hi", "sa_lo"))

  # purchase date is the first record of the timeseries
  pu_data <- xts::first(stock_data)
  output$pu_hi <- as.numeric(quantmod::Hi(pu_data))
  output$pu_lo <- as.numeric(quantmod::Lo(pu_data))

  # assuming sales date is the last record of the timeseries
  sa_data <- xts::last(stock_data)
  output$sa_hi <- as.numeric(quantmod::Hi(sa_data))
  output$sa_lo <- as.numeric(quantmod::Lo(sa_data))

  return(output)
}

r - R根据股票投资组合的买卖日期提取最高价和最低价

1 回答 1

Related

Reference