xts 的每一天随时间变化的最小值和最大值
min and max over time range on each day of xts
我有一个包含几年内 OHLC 日内价格数据的 xts 对象。我希望能够编写一个函数来每天计算 04:00:00 和 05:00:00 之间的最小值和最大值，并将其作为列包含在 xts 对象中。我不太熟悉操作 xts 对象。谁能指出我正确的方向？这是 xts 对象的头部。
Open High Low Close Volume
2017-01-01 00:00:00 968.29 968.76 966.74 966.97 106562
2017-01-01 00:05:00 966.97 967.00 966.89 966.89 13731
2017-01-01 00:10:00 966.89 966.89 964.86 964.86 124137
2017-01-01 00:15:00 964.86 964.99 964.80 964.80 3001
2017-01-01 00:20:00 964.80 964.80 964.80 964.80 0
2017-01-01 00:25:00 964.80 965.09 964.54 964.91 48000
2017-01-01 00:30:00 964.91 965.01 964.91 965.01 2501
2017-01-01 00:35:00 965.01 967.82 965.57 967.82 71501
2017-01-01 00:40:00 967.82 967.82 967.08 967.08 50
2017-01-01 00:45:00 967.08 967.40 967.40 967.40 50
2017-01-01 00:50:00 967.40 968.08 967.40 968.08 14000
2017-01-01 00:55:00 968.08 968.08 966.89 968.00 1008
2017-01-01 01:00:00 968.00 968.10 968.00 968.10 1002
2017-01-01 01:05:00 968.10 968.10 967.62 967.62 5200
2017-01-01 01:10:00 967.62 967.70 966.29 966.29 35476
2017-01-01 01:15:00 966.29 966.29 966.28 966.28 3068
2017-01-01 01:20:00 966.28 966.66 965.00 965.00 30471
2017-01-01 01:25:00 965.00 965.01 964.00 964.00 77884
2017-01-01 01:30:00 964.00 964.76 964.76 964.76 500
2017-01-01 01:35:00 964.76 967.48 964.69 965.00 134129
2017-01-01 01:40:00 965.00 965.00 963.67 963.67 59676
2017-01-01 01:45:00 963.67 963.67 963.67 963.67 0
2017-01-01 01:50:00 963.67 964.56 963.66 964.55 5531
2017-01-01 01:55:00 964.55 963.43 963.40 963.40 3000
2017-01-01 02:00:00 963.40 964.60 963.40 964.60 1301
2017-01-01 02:05:00 964.60 964.60 964.60 964.60 0
2017-01-01 02:10:00 964.60 964.60 964.00 964.11 49954
2017-01-01 02:15:00 964.11 964.60 964.59 964.60 5000
2017-01-01 02:20:00 964.60 964.60 964.60 964.60 0
2017-01-01 02:25:00 964.60 964.60 964.51 964.51 2000
2017-01-01 02:30:00 964.51 964.51 964.51 964.51 0
2017-01-01 02:35:00 964.51 964.51 963.23 963.99 16667
2017-01-01 02:40:00 963.99 963.99 963.65 963.66 10000
2017-01-01 02:45:00 963.66 964.26 963.16 964.26 75500
2017-01-01 02:50:00 964.26 964.26 964.26 964.26 0
2017-01-01 02:55:00 964.26 964.26 964.26 964.26 0
2017-01-01 03:00:00 964.26 964.61 963.98 964.61 13000
2017-01-01 03:05:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:10:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:15:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:20:00 964.61 964.82 964.48 964.82 16666
2017-01-01 03:25:00 964.82 965.00 963.99 964.97 50500
2017-01-01 03:30:00 964.97 964.97 964.02 964.02 56000
2017-01-01 03:35:00 964.02 964.29 964.29 964.29 500
2017-01-01 03:40:00 964.29 963.53 963.52 963.52 24000
2017-01-01 03:45:00 963.52 963.52 963.43 963.43 16500
2017-01-01 03:50:00 963.43 963.67 963.42 963.42 25002
2017-01-01 03:55:00 963.42 963.42 961.69 961.69 84507
2017-01-01 04:00:00 961.69 961.69 960.90 960.93 57909
2017-01-01 04:05:00 960.93 960.93 960.93 960.93 0
2017-01-01 04:10:00 960.93 961.19 961.19 961.19 400
2017-01-01 04:15:00 961.19 962.09 961.19 962.09 7001
2017-01-01 04:20:00 962.09 962.09 962.09 962.09 0
2017-01-01 04:25:00 962.09 962.10 961.14 961.14 32000
2017-01-01 04:30:00 961.14 961.14 960.93 960.93 41900
2017-01-01 04:35:00 960.93 961.94 960.93 961.64 640
2017-01-01 04:40:00 961.64 961.71 961.64 961.71 1
2017-01-01 04:45:00 961.71 962.00 961.90 961.99 5499
2017-01-01 04:50:00 961.99 961.99 961.99 961.99 0
2017-01-01 04:55:00 961.99 961.99 961.99 961.99 1
2017-01-01 05:00:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:05:00 961.99 961.99 961.99 961.99 40
2017-01-01 05:10:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:15:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:20:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:25:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:30:00 961.99 962.10 961.99 962.10 1382
2017-01-01 05:35:00 962.10 968.84 962.10 968.84 122909
2017-01-01 05:40:00 968.84 968.86 963.78 965.53 161263
2017-01-01 05:45:00 965.53 964.81 963.11 963.81 18021
2017-01-01 05:50:00 963.81 964.39 963.85 964.39 40006
2017-01-01 05:55:00 964.39 964.47 964.00 964.47 39966
2017-01-01 06:00:00 964.47 964.47 964.47 964.47 0
您可以通过过滤索引的时间然后使用 period.max 和 period.min 函数来实现。这些值将被放入所选小时的最后一条记录中。请参阅下面的示例，其中包含 MSFT 的日内数据，15:00 和 16:00 之间的最大值和最小值。
library(xts)
# max of high values between 15 and 16. (excluding 16:00)
msft$max <- period.max(msft$high[.indexhour(msft) == 15], endpoints(msft$high[.indexhour(msft) == 15], on = "hour"))
# min of low values between 15 and 16. (excluding 16:00)
msft$min <- period.min(msft$low[.indexhour(msft) == 15], endpoints(msft$low[.indexhour(msft) == 15], on = "hour"))
head(msft[8:24], 16)
open high low close volume max min
2020-01-23 14:50:00 166.180 166.2300 166.090 166.1050 87934 NA NA
2020-01-23 14:55:00 166.105 166.2200 166.103 166.1700 92280 NA NA
2020-01-23 15:00:00 166.160 166.3500 166.160 166.3400 114359 NA NA
2020-01-23 15:05:00 166.335 166.3400 166.285 166.2850 102633 NA NA
2020-01-23 15:10:00 166.290 166.3050 166.170 166.2550 125558 NA NA
2020-01-23 15:15:00 166.250 166.2750 166.210 166.2400 103938 NA NA
2020-01-23 15:20:00 166.230 166.2500 166.180 166.2350 99649 NA NA
2020-01-23 15:25:00 166.240 166.3000 166.225 166.2850 93846 NA NA
2020-01-23 15:30:00 166.270 166.4164 166.175 166.3600 183154 NA NA
2020-01-23 15:35:00 166.360 166.5000 166.320 166.4600 177178 NA NA
2020-01-23 15:40:00 166.450 166.4650 166.380 166.3800 112174 NA NA
2020-01-23 15:45:00 166.385 166.4050 166.290 166.3875 152806 NA NA
2020-01-23 15:50:00 166.382 166.5200 166.362 166.4500 205667 NA NA
2020-01-23 15:55:00 166.450 166.6900 166.305 166.6700 508469 166.69 166.16
2020-01-23 16:00:00 166.660 166.7200 166.589 166.7200 934090 NA NA
2020-01-24 09:35:00 167.510 167.5300 166.890 166.8918 1152646 NA NA
数据：
msft <- structure(c(166.224, 166.29, 166.29, 166.2456, 166.165, 166.1446,
166.1601, 166.18, 166.105, 166.16, 166.335, 166.29, 166.25, 166.23,
166.24, 166.27, 166.36, 166.45, 166.385, 166.382, 166.45, 166.66,
167.51, 167.03, 167.265, 167.325, 167.37, 167.16, 167.405, 167.35,
167.31, 167.39, 167.17, 167.1, 166.845, 167.03, 167.1223, 167.125,
167.21, 167.34, 167.235, 167.3, 167.37, 167.1977, 166.9814, 166.8499,
166.99, 166.93, 166.83, 166.64, 166.775, 166.85, 166.71, 166.6838,
166.46, 166.35, 165.765, 166.2269, 166.01, 166.19, 166.13, 166.31,
166.36, 166.42, 166.3682, 165.99, 166.1328, 165.85, 165.74, 165.8439,
165.655, 165.5434, 165.47, 165.3227, 165.0627, 165.03, 165.2546,
165.14, 165.1, 164.91, 164.75, 164.65, 164.53, 164.81, 164.8979,
164.6, 164.89, 164.94, 165.03, 165.12, 165.17, 165.24, 165.4,
165.335, 165.2734, 164.985, 164.9, 164.61, 164.93, 165.18, 166.315,
166.29, 166.3, 166.265, 166.22, 166.2201, 166.2, 166.23, 166.22,
166.35, 166.34, 166.305, 166.275, 166.25, 166.3, 166.4164, 166.5,
166.465, 166.405, 166.52, 166.69, 166.72, 167.53, 167.34, 167.39,
167.495, 167.47, 167.48, 167.4251, 167.3699, 167.42, 167.41,
167.2, 167.1, 167.03, 167.21, 167.23, 167.255, 167.35, 167.35,
167.33, 167.405, 167.38, 167.25, 167.01, 167, 167.02, 167.02,
166.8384, 166.9056, 166.86, 166.94, 166.75, 166.6844, 166.47,
166.42, 166.22, 166.4049, 166.221, 166.2003, 166.3749, 166.3999,
166.43, 166.43, 166.375, 166.175, 166.16, 165.96, 165.93, 165.86,
165.671, 165.64, 165.49, 165.4, 165.08, 165.27, 165.26, 165.34,
165.12, 165, 164.825, 164.765, 164.82, 164.89, 165, 164.89, 164.99,
165.041, 165.293, 165.23, 165.27, 165.44, 165.6046, 165.37, 165.295,
165.18, 164.93, 164.945, 165.185, 165.24, 166.22, 166.225, 166.25,
166.15, 166.145, 166.13, 166.1015, 166.09, 166.103, 166.16, 166.285,
166.17, 166.21, 166.18, 166.225, 166.175, 166.32, 166.38, 166.29,
166.362, 166.305, 166.589, 166.89, 167.03, 167.22, 167.32, 167.225,
167.16, 167.2, 167.23, 167.2801, 167.145, 167.05, 166.84, 166.77,
167, 167.1, 167.02, 167.18, 167.23, 167.223, 167.28, 167.1843,
166.862, 166.85, 166.821, 166.8121, 166.85, 166.55, 166.6303,
166.69, 166.7, 166.54, 166.4, 166.31, 165.76, 165.74, 165.8966,
165.91, 166.07, 166.09, 166.171, 166.32, 166.22, 165.96, 165.97,
165.82, 165.73, 165.72, 165.64, 165.49, 165.45, 165.32, 165.045,
164.89, 164.91, 165.09, 165.1, 164.91, 164.74, 164.53, 164.529,
164.53, 164.735, 164.59, 164.54, 164.88, 164.938, 165.01, 165.0792,
165.12, 165.22, 165.335, 165.263, 164.88, 164.89, 164.58, 164.58,
164.87, 164.87, 166.29, 166.275, 166.26, 166.16, 166.145, 166.155,
166.19, 166.105, 166.17, 166.34, 166.285, 166.255, 166.24, 166.235,
166.285, 166.36, 166.46, 166.38, 166.3875, 166.45, 166.67, 166.72,
166.8918, 167.27, 167.325, 167.371, 167.2251, 167.4, 167.34,
167.29, 167.3988, 167.2, 167.1047, 166.86, 167.025, 167.11, 167.12,
167.2, 167.345, 167.23, 167.29, 167.37, 167.1916, 167.0027, 166.85,
167, 166.94, 166.85, 166.64, 166.7738, 166.85, 166.72, 166.68,
166.4672, 166.3512, 165.79, 166.21, 165.9969, 166.18, 166.14,
166.2968, 166.36, 166.43, 166.36, 165.99, 166.13, 165.83, 165.73,
165.8405, 165.65, 165.545, 165.48, 165.33, 165.05, 165.0227,
165.26, 165.1425, 165.101, 164.91, 164.74, 164.6581, 164.5292,
164.805, 164.89, 164.59, 164.8801, 164.9498, 165.04, 165.12,
165.16, 165.2302, 165.4, 165.34, 165.28, 164.987, 164.89, 164.605,
164.94, 165.185, 165.04, 158120, 165333, 101115, 78491, 123999,
76037, 82733, 87934, 92280, 114359, 102633, 125558, 103938, 99649,
93846, 183154, 177178, 112174, 152806, 205667, 508469, 934090,
1152646, 558627, 277325, 321651, 255494, 333848, 272126, 395463,
194593, 211910, 193131, 242112, 210240, 193265, 139617, 204182,
179146, 159259, 237888, 410982, 213787, 233082, 188071, 193742,
132377, 118994, 264247, 182490, 109514, 138164, 221052, 194127,
169059, 458214, 247712, 169523, 115531, 161259, 263230, 155536,
82474, 87549, 109057, 101772, 130642, 171988, 117235, 134507,
236662, 219303, 217698, 219808, 420288, 208087, 149358, 197435,
218090, 267667, 320279, 422434, 340478, 273866, 258938, 212451,
268017, 323657, 267686, 214060, 222314, 293731, 288867, 219687,
304733, 251063, 425450, 455311, 741208, 1429645),
.Dim = c(100L, 5L),
.Dimnames = list(NULL, c("open", "high", "low", "close", "volume")),
index = structure(c(1579785300, 1579785600, 1579785900, 1579786200, 1579786500,
1579786800, 1579787100, 1579787400, 1579787700, 1579788000,
1579788300, 1579788600, 1579788900, 1579789200, 1579789500,
1579789800, 1579790100, 1579790400, 1579790700, 1579791000,
1579791300, 1579791600, 1579854900, 1579855200, 1579855500,
1579855800, 1579856100, 1579856400, 1579856700, 1579857000,
1579857300, 1579857600, 1579857900, 1579858200, 1579858500,
1579858800, 1579859100, 1579859400, 1579859700, 1579860000,
1579860300, 1579860600, 1579860900, 1579861200, 1579861500,
1579861800, 1579862100, 1579862400, 1579862700, 1579863000,
1579863300, 1579863600, 1579863900, 1579864200, 1579864500,
1579864800, 1579865100, 1579865400, 1579865700, 1579866000,
1579866300, 1579866600, 1579866900, 1579867200, 1579867500,
1579867800, 1579868100, 1579868400, 1579868700, 1579869000,
1579869300, 1579869600, 1579869900, 1579870200, 1579870500,
1579870800, 1579871100, 1579871400, 1579871700, 1579872000,
1579872300, 1579872600, 1579872900, 1579873200, 1579873500,
1579873800, 1579874100, 1579874400, 1579874700, 1579875000,
1579875300, 1579875600, 1579875900, 1579876200, 1579876500,
1579876800, 1579877100, 1579877400, 1579877700, 1579878000),
tzone = "",
tclass = c("POSIXct", "POSIXt")),
class = c("xts", "zoo"))
我有一个包含几年内 OHLC 日内价格数据的 xts 对象。我希望能够编写一个函数来每天计算 04:00:00 和 05:00:00 之间的最小值和最大值，并将其作为列包含在 xts 对象中。我不太熟悉操作 xts 对象。谁能指出我正确的方向？这是 xts 对象的头部。
Open High Low Close Volume
2017-01-01 00:00:00 968.29 968.76 966.74 966.97 106562
2017-01-01 00:05:00 966.97 967.00 966.89 966.89 13731
2017-01-01 00:10:00 966.89 966.89 964.86 964.86 124137
2017-01-01 00:15:00 964.86 964.99 964.80 964.80 3001
2017-01-01 00:20:00 964.80 964.80 964.80 964.80 0
2017-01-01 00:25:00 964.80 965.09 964.54 964.91 48000
2017-01-01 00:30:00 964.91 965.01 964.91 965.01 2501
2017-01-01 00:35:00 965.01 967.82 965.57 967.82 71501
2017-01-01 00:40:00 967.82 967.82 967.08 967.08 50
2017-01-01 00:45:00 967.08 967.40 967.40 967.40 50
2017-01-01 00:50:00 967.40 968.08 967.40 968.08 14000
2017-01-01 00:55:00 968.08 968.08 966.89 968.00 1008
2017-01-01 01:00:00 968.00 968.10 968.00 968.10 1002
2017-01-01 01:05:00 968.10 968.10 967.62 967.62 5200
2017-01-01 01:10:00 967.62 967.70 966.29 966.29 35476
2017-01-01 01:15:00 966.29 966.29 966.28 966.28 3068
2017-01-01 01:20:00 966.28 966.66 965.00 965.00 30471
2017-01-01 01:25:00 965.00 965.01 964.00 964.00 77884
2017-01-01 01:30:00 964.00 964.76 964.76 964.76 500
2017-01-01 01:35:00 964.76 967.48 964.69 965.00 134129
2017-01-01 01:40:00 965.00 965.00 963.67 963.67 59676
2017-01-01 01:45:00 963.67 963.67 963.67 963.67 0
2017-01-01 01:50:00 963.67 964.56 963.66 964.55 5531
2017-01-01 01:55:00 964.55 963.43 963.40 963.40 3000
2017-01-01 02:00:00 963.40 964.60 963.40 964.60 1301
2017-01-01 02:05:00 964.60 964.60 964.60 964.60 0
2017-01-01 02:10:00 964.60 964.60 964.00 964.11 49954
2017-01-01 02:15:00 964.11 964.60 964.59 964.60 5000
2017-01-01 02:20:00 964.60 964.60 964.60 964.60 0
2017-01-01 02:25:00 964.60 964.60 964.51 964.51 2000
2017-01-01 02:30:00 964.51 964.51 964.51 964.51 0
2017-01-01 02:35:00 964.51 964.51 963.23 963.99 16667
2017-01-01 02:40:00 963.99 963.99 963.65 963.66 10000
2017-01-01 02:45:00 963.66 964.26 963.16 964.26 75500
2017-01-01 02:50:00 964.26 964.26 964.26 964.26 0
2017-01-01 02:55:00 964.26 964.26 964.26 964.26 0
2017-01-01 03:00:00 964.26 964.61 963.98 964.61 13000
2017-01-01 03:05:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:10:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:15:00 964.61 964.61 964.61 964.61 0
2017-01-01 03:20:00 964.61 964.82 964.48 964.82 16666
2017-01-01 03:25:00 964.82 965.00 963.99 964.97 50500
2017-01-01 03:30:00 964.97 964.97 964.02 964.02 56000
2017-01-01 03:35:00 964.02 964.29 964.29 964.29 500
2017-01-01 03:40:00 964.29 963.53 963.52 963.52 24000
2017-01-01 03:45:00 963.52 963.52 963.43 963.43 16500
2017-01-01 03:50:00 963.43 963.67 963.42 963.42 25002
2017-01-01 03:55:00 963.42 963.42 961.69 961.69 84507
2017-01-01 04:00:00 961.69 961.69 960.90 960.93 57909
2017-01-01 04:05:00 960.93 960.93 960.93 960.93 0
2017-01-01 04:10:00 960.93 961.19 961.19 961.19 400
2017-01-01 04:15:00 961.19 962.09 961.19 962.09 7001
2017-01-01 04:20:00 962.09 962.09 962.09 962.09 0
2017-01-01 04:25:00 962.09 962.10 961.14 961.14 32000
2017-01-01 04:30:00 961.14 961.14 960.93 960.93 41900
2017-01-01 04:35:00 960.93 961.94 960.93 961.64 640
2017-01-01 04:40:00 961.64 961.71 961.64 961.71 1
2017-01-01 04:45:00 961.71 962.00 961.90 961.99 5499
2017-01-01 04:50:00 961.99 961.99 961.99 961.99 0
2017-01-01 04:55:00 961.99 961.99 961.99 961.99 1
2017-01-01 05:00:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:05:00 961.99 961.99 961.99 961.99 40
2017-01-01 05:10:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:15:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:20:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:25:00 961.99 961.99 961.99 961.99 0
2017-01-01 05:30:00 961.99 962.10 961.99 962.10 1382
2017-01-01 05:35:00 962.10 968.84 962.10 968.84 122909
2017-01-01 05:40:00 968.84 968.86 963.78 965.53 161263
2017-01-01 05:45:00 965.53 964.81 963.11 963.81 18021
2017-01-01 05:50:00 963.81 964.39 963.85 964.39 40006
2017-01-01 05:55:00 964.39 964.47 964.00 964.47 39966
2017-01-01 06:00:00 964.47 964.47 964.47 964.47 0
您可以通过过滤索引的时间然后使用 period.max 和 period.min 函数来实现。这些值将被放入所选小时的最后一条记录中。请参阅下面的示例，其中包含 MSFT 的日内数据，15:00 和 16:00 之间的最大值和最小值。
library(xts)
# max of high values between 15 and 16. (excluding 16:00)
msft$max <- period.max(msft$high[.indexhour(msft) == 15], endpoints(msft$high[.indexhour(msft) == 15], on = "hour"))
# min of low values between 15 and 16. (excluding 16:00)
msft$min <- period.min(msft$low[.indexhour(msft) == 15], endpoints(msft$low[.indexhour(msft) == 15], on = "hour"))
head(msft[8:24], 16)
open high low close volume max min
2020-01-23 14:50:00 166.180 166.2300 166.090 166.1050 87934 NA NA
2020-01-23 14:55:00 166.105 166.2200 166.103 166.1700 92280 NA NA
2020-01-23 15:00:00 166.160 166.3500 166.160 166.3400 114359 NA NA
2020-01-23 15:05:00 166.335 166.3400 166.285 166.2850 102633 NA NA
2020-01-23 15:10:00 166.290 166.3050 166.170 166.2550 125558 NA NA
2020-01-23 15:15:00 166.250 166.2750 166.210 166.2400 103938 NA NA
2020-01-23 15:20:00 166.230 166.2500 166.180 166.2350 99649 NA NA
2020-01-23 15:25:00 166.240 166.3000 166.225 166.2850 93846 NA NA
2020-01-23 15:30:00 166.270 166.4164 166.175 166.3600 183154 NA NA
2020-01-23 15:35:00 166.360 166.5000 166.320 166.4600 177178 NA NA
2020-01-23 15:40:00 166.450 166.4650 166.380 166.3800 112174 NA NA
2020-01-23 15:45:00 166.385 166.4050 166.290 166.3875 152806 NA NA
2020-01-23 15:50:00 166.382 166.5200 166.362 166.4500 205667 NA NA
2020-01-23 15:55:00 166.450 166.6900 166.305 166.6700 508469 166.69 166.16
2020-01-23 16:00:00 166.660 166.7200 166.589 166.7200 934090 NA NA
2020-01-24 09:35:00 167.510 167.5300 166.890 166.8918 1152646 NA NA
数据：
msft <- structure(c(166.224, 166.29, 166.29, 166.2456, 166.165, 166.1446,
166.1601, 166.18, 166.105, 166.16, 166.335, 166.29, 166.25, 166.23,
166.24, 166.27, 166.36, 166.45, 166.385, 166.382, 166.45, 166.66,
167.51, 167.03, 167.265, 167.325, 167.37, 167.16, 167.405, 167.35,
167.31, 167.39, 167.17, 167.1, 166.845, 167.03, 167.1223, 167.125,
167.21, 167.34, 167.235, 167.3, 167.37, 167.1977, 166.9814, 166.8499,
166.99, 166.93, 166.83, 166.64, 166.775, 166.85, 166.71, 166.6838,
166.46, 166.35, 165.765, 166.2269, 166.01, 166.19, 166.13, 166.31,
166.36, 166.42, 166.3682, 165.99, 166.1328, 165.85, 165.74, 165.8439,
165.655, 165.5434, 165.47, 165.3227, 165.0627, 165.03, 165.2546,
165.14, 165.1, 164.91, 164.75, 164.65, 164.53, 164.81, 164.8979,
164.6, 164.89, 164.94, 165.03, 165.12, 165.17, 165.24, 165.4,
165.335, 165.2734, 164.985, 164.9, 164.61, 164.93, 165.18, 166.315,
166.29, 166.3, 166.265, 166.22, 166.2201, 166.2, 166.23, 166.22,
166.35, 166.34, 166.305, 166.275, 166.25, 166.3, 166.4164, 166.5,
166.465, 166.405, 166.52, 166.69, 166.72, 167.53, 167.34, 167.39,
167.495, 167.47, 167.48, 167.4251, 167.3699, 167.42, 167.41,
167.2, 167.1, 167.03, 167.21, 167.23, 167.255, 167.35, 167.35,
167.33, 167.405, 167.38, 167.25, 167.01, 167, 167.02, 167.02,
166.8384, 166.9056, 166.86, 166.94, 166.75, 166.6844, 166.47,
166.42, 166.22, 166.4049, 166.221, 166.2003, 166.3749, 166.3999,
166.43, 166.43, 166.375, 166.175, 166.16, 165.96, 165.93, 165.86,
165.671, 165.64, 165.49, 165.4, 165.08, 165.27, 165.26, 165.34,
165.12, 165, 164.825, 164.765, 164.82, 164.89, 165, 164.89, 164.99,
165.041, 165.293, 165.23, 165.27, 165.44, 165.6046, 165.37, 165.295,
165.18, 164.93, 164.945, 165.185, 165.24, 166.22, 166.225, 166.25,
166.15, 166.145, 166.13, 166.1015, 166.09, 166.103, 166.16, 166.285,
166.17, 166.21, 166.18, 166.225, 166.175, 166.32, 166.38, 166.29,
166.362, 166.305, 166.589, 166.89, 167.03, 167.22, 167.32, 167.225,
167.16, 167.2, 167.23, 167.2801, 167.145, 167.05, 166.84, 166.77,
167, 167.1, 167.02, 167.18, 167.23, 167.223, 167.28, 167.1843,
166.862, 166.85, 166.821, 166.8121, 166.85, 166.55, 166.6303,
166.69, 166.7, 166.54, 166.4, 166.31, 165.76, 165.74, 165.8966,
165.91, 166.07, 166.09, 166.171, 166.32, 166.22, 165.96, 165.97,
165.82, 165.73, 165.72, 165.64, 165.49, 165.45, 165.32, 165.045,
164.89, 164.91, 165.09, 165.1, 164.91, 164.74, 164.53, 164.529,
164.53, 164.735, 164.59, 164.54, 164.88, 164.938, 165.01, 165.0792,
165.12, 165.22, 165.335, 165.263, 164.88, 164.89, 164.58, 164.58,
164.87, 164.87, 166.29, 166.275, 166.26, 166.16, 166.145, 166.155,
166.19, 166.105, 166.17, 166.34, 166.285, 166.255, 166.24, 166.235,
166.285, 166.36, 166.46, 166.38, 166.3875, 166.45, 166.67, 166.72,
166.8918, 167.27, 167.325, 167.371, 167.2251, 167.4, 167.34,
167.29, 167.3988, 167.2, 167.1047, 166.86, 167.025, 167.11, 167.12,
167.2, 167.345, 167.23, 167.29, 167.37, 167.1916, 167.0027, 166.85,
167, 166.94, 166.85, 166.64, 166.7738, 166.85, 166.72, 166.68,
166.4672, 166.3512, 165.79, 166.21, 165.9969, 166.18, 166.14,
166.2968, 166.36, 166.43, 166.36, 165.99, 166.13, 165.83, 165.73,
165.8405, 165.65, 165.545, 165.48, 165.33, 165.05, 165.0227,
165.26, 165.1425, 165.101, 164.91, 164.74, 164.6581, 164.5292,
164.805, 164.89, 164.59, 164.8801, 164.9498, 165.04, 165.12,
165.16, 165.2302, 165.4, 165.34, 165.28, 164.987, 164.89, 164.605,
164.94, 165.185, 165.04, 158120, 165333, 101115, 78491, 123999,
76037, 82733, 87934, 92280, 114359, 102633, 125558, 103938, 99649,
93846, 183154, 177178, 112174, 152806, 205667, 508469, 934090,
1152646, 558627, 277325, 321651, 255494, 333848, 272126, 395463,
194593, 211910, 193131, 242112, 210240, 193265, 139617, 204182,
179146, 159259, 237888, 410982, 213787, 233082, 188071, 193742,
132377, 118994, 264247, 182490, 109514, 138164, 221052, 194127,
169059, 458214, 247712, 169523, 115531, 161259, 263230, 155536,
82474, 87549, 109057, 101772, 130642, 171988, 117235, 134507,
236662, 219303, 217698, 219808, 420288, 208087, 149358, 197435,
218090, 267667, 320279, 422434, 340478, 273866, 258938, 212451,
268017, 323657, 267686, 214060, 222314, 293731, 288867, 219687,
304733, 251063, 425450, 455311, 741208, 1429645),
.Dim = c(100L, 5L),
.Dimnames = list(NULL, c("open", "high", "low", "close", "volume")),
index = structure(c(1579785300, 1579785600, 1579785900, 1579786200, 1579786500,
1579786800, 1579787100, 1579787400, 1579787700, 1579788000,
1579788300, 1579788600, 1579788900, 1579789200, 1579789500,
1579789800, 1579790100, 1579790400, 1579790700, 1579791000,
1579791300, 1579791600, 1579854900, 1579855200, 1579855500,
1579855800, 1579856100, 1579856400, 1579856700, 1579857000,
1579857300, 1579857600, 1579857900, 1579858200, 1579858500,
1579858800, 1579859100, 1579859400, 1579859700, 1579860000,
1579860300, 1579860600, 1579860900, 1579861200, 1579861500,
1579861800, 1579862100, 1579862400, 1579862700, 1579863000,
1579863300, 1579863600, 1579863900, 1579864200, 1579864500,
1579864800, 1579865100, 1579865400, 1579865700, 1579866000,
1579866300, 1579866600, 1579866900, 1579867200, 1579867500,
1579867800, 1579868100, 1579868400, 1579868700, 1579869000,
1579869300, 1579869600, 1579869900, 1579870200, 1579870500,
1579870800, 1579871100, 1579871400, 1579871700, 1579872000,
1579872300, 1579872600, 1579872900, 1579873200, 1579873500,
1579873800, 1579874100, 1579874400, 1579874700, 1579875000,
1579875300, 1579875600, 1579875900, 1579876200, 1579876500,
1579876800, 1579877100, 1579877400, 1579877700, 1579878000),
tzone = "",
tclass = c("POSIXct", "POSIXt")),
class = c("xts", "zoo"))