R 根据股票组合的购买和销售日期提取最高价和最低价
R Extracting the High and Low based on purchase and sale dates on a portfolio of stocks
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 种不同股票代码。我必须检查以确保购买价格在购买和销售的特定日期处于最高价和最低价之间。
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，将它们格式化为列表，并获取最高和最低列。
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
我遇到的困难是弄清楚如何从该数据集中提取 hilo 中相应交易品种在买卖日期的最高价和最低价。
下面是一些代码，可以return满足您的需要。我使用 quantmod、purrr 和 dplyr 作为包。请注意使用 pmap 和 getSymbols 来获取数据。在对每个代码使用 getSymbols 时，您将获得相同数量的行。这对于大多数请求来说有点矫枉过正，因为在大多数情况下持有期更短。现在每个代码只会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。这里假设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 中删除了开盘价、最高价、最低价和收盘价列，因为本示例不需要这些列。
我将检查买入/卖出价格是否在最高价和最低价之间。
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)
}
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 种不同股票代码。我必须检查以确保购买价格在购买和销售的特定日期处于最高价和最低价之间。
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，将它们格式化为列表，并获取最高和最低列。
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
我遇到的困难是弄清楚如何从该数据集中提取 hilo 中相应交易品种在买卖日期的最高价和最低价。
下面是一些代码，可以return满足您的需要。我使用 quantmod、purrr 和 dplyr 作为包。请注意使用 pmap 和 getSymbols 来获取数据。在对每个代码使用 getSymbols 时，您将获得相同数量的行。这对于大多数请求来说有点矫枉过正，因为在大多数情况下持有期更短。现在每个代码只会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。这里假设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 中删除了开盘价、最高价、最低价和收盘价列，因为本示例不需要这些列。
我将检查买入/卖出价格是否在最高价和最低价之间。
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)
}