我有两只股票两个月的数据如下-
dt <- structure(list(date = structure(c(18718, 18722, 18723, 18724,
18725, 18726, 18729, 18730, 18731, 18732, 18733, 18736, 18737,
18738, 18739, 18740, 18743, 18744, 18745, 18746, 18747, 18750,
18751, 18752, 18753, 18754, 18757, 18758, 18759, 18760, 18761,
18764, 18765, 18766, 18767, 18768, 18771, 18772, 18773, 18774,
18778, 18718, 18722, 18723, 18724, 18725, 18726, 18729, 18730,
18731, 18732, 18733, 18736, 18737, 18738, 18739, 18740, 18743,
18744, 18745, 18746, 18747, 18750, 18751, 18752, 18753, 18754,
18757, 18758, 18759, 18760, 18761, 18764, 18765, 18766, 18767,
18768, 18771, 18772, 18773, 18774, 18778), class = "Date"),
close = c(123,
125.9, 126.21, 127.9, 130.36, 132.995, 131.24, 134.43, 132.03,
134.5, 134.16, 134.84, 133.11, 133.5, 131.94, 134.32, 134.72,
134.39, 133.58, 133.48, 131.46, 132.54, 127.85, 128.1, 129.74,
130.21, 126.85, 125.91, 122.77, 124.97, 127.45, 126.27, 124.85,
124.69, 127.31, 125.43, 127.1, 126.9, 126.85, 125.28, 124.61,
2137.75, 2225.55, 2224.75, 2249.68, 2265.44, 2285.88, 2254.79,
2267.27, 2254.84, 2296.66, 2297.76, 2302.4, 2293.63, 2293.29,
2267.92, 2315.3, 2326.74, 2307.12, 2379.91, 2429.89, 2410.12,
2395.17, 2354.25, 2356.74, 2381.35, 2398.69, 2341.66, 2308.76,
2239.08, 2261.97, 2316.16, 2321.41, 2303.43, 2308.71, 2356.09,
2345.1, 2406.67, 2409.07, 2433.53, 2402.51, 2411.56),
ticker = c("AAPL",
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL",
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL",
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL",
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL",
"AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL", "AAPL",
"GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG",
"GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG",
"GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG",
"GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG",
"GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG", "GOOG",
"GOOG")), row.names = c(NA, -82L), class = c("data.table", "data.frame"
))
我想仅使用 data.table 查找这些股票的月返回率。是否有任何现有功能或简单的方法来完成此操作？
我试图用下面的代码解决它，但它给出了错误-
dt[, return := rep(periodReturn(.SD, period = 'monthly', type = "arithmetic"), .N), by = .(ticker)]
这里是错误
Error in `[.data.table`(dt, , `:=`(return, rep(periodReturn(.SD, period = "monthly", :
Supplied 82 items to be assigned to group 1 of size 41 in column 'return'. The RHS length must either be 1 (single values are ok) or match the LHS length exactly. If you wish to 'recycle' the RHS please use rep() explicitly to make this intent clear to readers of your code.
任何见解都会有所帮助。
预期的输出是
ticker month return
AAPL 4 0.06878049
AAPL 5 -0.05210710
GOOG 4 0.1274096597
GOOG 5 0.0005974806
最佳答案
也许，如果我们指定 length.out 就可以解决大小不匹配的错误。在 rep
library(data.table)
library(quantmod)
dt[, return := rep(periodReturn(.SD, period = 'monthly',
type = "arithmetic"), length.out = .N), by = .(ticker)]
-输出
dt
date close ticker return
1: 2021-04-01 123.000 AAPL 0.0687804878
2: 2021-04-05 125.900 AAPL -0.0521071048
3: 2021-04-06 126.210 AAPL 0.0687804878
4: 2021-04-07 127.900 AAPL -0.0521071048
5: 2021-04-08 130.360 AAPL 0.0687804878
6: 2021-04-09 132.995 AAPL -0.0521071048
7: 2021-04-12 131.240 AAPL 0.0687804878
8: 2021-04-13 134.430 AAPL -0.0521071048
9: 2021-04-14 132.030 AAPL 0.0687804878
10: 2021-04-15 134.500 AAPL -0.0521071048
11: 2021-04-16 134.160 AAPL 0.0687804878
12: 2021-04-19 134.840 AAPL -0.0521071048
13: 2021-04-20 133.110 AAPL 0.0687804878
14: 2021-04-21 133.500 AAPL -0.0521071048
15: 2021-04-22 131.940 AAPL 0.0687804878
16: 2021-04-23 134.320 AAPL -0.0521071048
17: 2021-04-26 134.720 AAPL 0.0687804878
18: 2021-04-27 134.390 AAPL -0.0521071048
19: 2021-04-28 133.580 AAPL 0.0687804878
20: 2021-04-29 133.480 AAPL -0.0521071048
21: 2021-04-30 131.460 AAPL 0.0687804878
22: 2021-05-03 132.540 AAPL -0.0521071048
23: 2021-05-04 127.850 AAPL 0.0687804878
24: 2021-05-05 128.100 AAPL -0.0521071048
25: 2021-05-06 129.740 AAPL 0.0687804878
26: 2021-05-07 130.210 AAPL -0.0521071048
27: 2021-05-10 126.850 AAPL 0.0687804878
28: 2021-05-11 125.910 AAPL -0.0521071048
29: 2021-05-12 122.770 AAPL 0.0687804878
30: 2021-05-13 124.970 AAPL -0.0521071048
31: 2021-05-14 127.450 AAPL 0.0687804878
32: 2021-05-17 126.270 AAPL -0.0521071048
33: 2021-05-18 124.850 AAPL 0.0687804878
34: 2021-05-19 124.690 AAPL -0.0521071048
35: 2021-05-20 127.310 AAPL 0.0687804878
36: 2021-05-21 125.430 AAPL -0.0521071048
37: 2021-05-24 127.100 AAPL 0.0687804878
38: 2021-05-25 126.900 AAPL -0.0521071048
39: 2021-05-26 126.850 AAPL 0.0687804878
40: 2021-05-27 125.280 AAPL -0.0521071048
41: 2021-05-31 124.610 AAPL 0.0687804878
42: 2021-04-01 2137.750 GOOG 0.1274096597
43: 2021-04-05 2225.550 GOOG 0.0005974806
44: 2021-04-06 2224.750 GOOG 0.1274096597
45: 2021-04-07 2249.680 GOOG 0.0005974806
46: 2021-04-08 2265.440 GOOG 0.1274096597
47: 2021-04-09 2285.880 GOOG 0.0005974806
48: 2021-04-12 2254.790 GOOG 0.1274096597
49: 2021-04-13 2267.270 GOOG 0.0005974806
50: 2021-04-14 2254.840 GOOG 0.1274096597
51: 2021-04-15 2296.660 GOOG 0.0005974806
52: 2021-04-16 2297.760 GOOG 0.1274096597
53: 2021-04-19 2302.400 GOOG 0.0005974806
54: 2021-04-20 2293.630 GOOG 0.1274096597
55: 2021-04-21 2293.290 GOOG 0.0005974806
56: 2021-04-22 2267.920 GOOG 0.1274096597
57: 2021-04-23 2315.300 GOOG 0.0005974806
58: 2021-04-26 2326.740 GOOG 0.1274096597
59: 2021-04-27 2307.120 GOOG 0.0005974806
60: 2021-04-28 2379.910 GOOG 0.1274096597
61: 2021-04-29 2429.890 GOOG 0.0005974806
62: 2021-04-30 2410.120 GOOG 0.1274096597
63: 2021-05-03 2395.170 GOOG 0.0005974806
64: 2021-05-04 2354.250 GOOG 0.1274096597
65: 2021-05-05 2356.740 GOOG 0.0005974806
66: 2021-05-06 2381.350 GOOG 0.1274096597
67: 2021-05-07 2398.690 GOOG 0.0005974806
68: 2021-05-10 2341.660 GOOG 0.1274096597
69: 2021-05-11 2308.760 GOOG 0.0005974806
70: 2021-05-12 2239.080 GOOG 0.1274096597
71: 2021-05-13 2261.970 GOOG 0.0005974806
72: 2021-05-14 2316.160 GOOG 0.1274096597
73: 2021-05-17 2321.410 GOOG 0.0005974806
74: 2021-05-18 2303.430 GOOG 0.1274096597
75: 2021-05-19 2308.710 GOOG 0.0005974806
76: 2021-05-20 2356.090 GOOG 0.1274096597
77: 2021-05-21 2345.100 GOOG 0.0005974806
78: 2021-05-24 2406.670 GOOG 0.1274096597
79: 2021-05-25 2409.070 GOOG 0.0005974806
80: 2021-05-26 2433.530 GOOG 0.1274096597
81: 2021-05-27 2402.510 GOOG 0.0005974806
82: 2021-05-31 2411.560 GOOG 0.1274096597
如果我们想总结，将其包装在 list 中作为xts建立在 matrix 之上的属性来自 periodReturn可能需要将其屏蔽在 list 中.当我们使用 rep , 它剥离了 xts/matrix属性，结果列为 numeric vector
dt[, .(return = .(periodReturn(.SD, period = 'monthly',
type = "arithmetic"))), .(ticker)]
ticker return
1: AAPL 0.06878049,-0.05210710
2: GOOG 0.1274096597,0.0005974806
或者删除 xts通过转换为 numeric 属性它应该可以工作
library(lubridate)
dt[, .(month = unique(month(date)),
return = as.numeric(periodReturn(.SD, period = 'monthly',
type = "arithmetic"))), .(ticker)]
ticker month return
1: AAPL 4 0.0687804878
2: AAPL 5 -0.0521071048
3: GOOG 4 0.1274096597
4: GOOG 5 0.0005974806
关于r - 如何使用 R 中的 data.table 查找股票的月返回率？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/67925627/