library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.6 v dplyr 1.0.8
## v tidyr 1.2.0 v stringr 1.4.0
## v readr 2.1.2 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(readr)
ETF6_20080101_20200430 <- read_csv("C:/Users/CTY REDSTAR/Downloads/ETF6_20080101-20200430.csv")
## Rows: 14935 Columns: 4
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## chr (2): è‰åˆ¸ä»£ç¢¼, 簡稱
## dbl (2): 年月日, 收盤價(元)
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
rm(list = ls())
etf6 <- read.table('C:/Users/CTY REDSTAR/Downloads/ETF6_20080101-20200430.csv', sep = ',', header = T)
head(etf6)
## è..å..ä..ç.. ç..ç.. å¹.æœ.æ.. æ..ç..價.å.ƒ.
## 1 0050 元大å\217°ç\201£50 20080102 39.6472
## 2 0052 富邦科æŠ\200 20080102 27.0983
## 3 0056 元大é«\230è‚¡æ\201¯ 20080102 14.5739
## 4 0050 元大å\217°ç\201£50 20080103 38.9876
## 5 0052 富邦科æŠ\200 20080103 26.0676
## 6 0056 元大é«\230è‚¡æ\201¯ 20080103 14.3758
etf6 <- etf6[, -2]
colnames(etf6) <- c('id', 'date', 'price')
head(etf6)
## id date price
## 1 0050 20080102 39.6472
## 2 0052 20080102 27.0983
## 3 0056 20080102 14.5739
## 4 0050 20080103 38.9876
## 5 0052 20080103 26.0676
## 6 0056 20080103 14.3758
library(pacman)
p_load(reshape2, xts, quantmod)
etf6.l <- dcast(etf6, date~id)
## Using price as value column: use value.var to override.
head(etf6.l)
## date 0050 0052 0056 0061 006206 00638R
## 1 20080102 39.6472 27.0983 14.5739 NA NA NA
## 2 20080103 38.9876 26.0676 14.3758 NA NA NA
## 3 20080104 38.9876 25.9346 14.4041 NA NA NA
## 4 20080107 37.2064 24.1391 14.1777 NA NA NA
## 5 20080108 37.5692 24.1391 14.3531 NA NA NA
## 6 20080109 38.2619 24.2721 14.4663 NA NA NA
str(etf6.l)
## 'data.frame': 3041 obs. of 7 variables:
## $ date : int 20080102 20080103 20080104 20080107 20080108 20080109 20080110 20080111 20080114 20080115 ...
## $ 0050 : num 39.6 39 39 37.2 37.6 ...
## $ 0052 : num 27.1 26.1 25.9 24.1 24.1 ...
## $ 0056 : num 14.6 14.4 14.4 14.2 14.4 ...
## $ 0061 : num NA NA NA NA NA NA NA NA NA NA ...
## $ 006206 : num NA NA NA NA NA NA NA NA NA NA ...
## $ 00638R : num NA NA NA NA NA NA NA NA NA NA ...
etf6.xts <- xts(etf6.l[, -1], order.by = as.Date(as.character(etf6.l$date), format = '%Y%m%d'))
class(etf6.xts)
## [1] "xts" "zoo"
head(etf6.xts)
## 0050 0052 0056 0061 006206 00638R
## 2008-01-02 39.6472 27.0983 14.5739 NA NA NA
## 2008-01-03 38.9876 26.0676 14.3758 NA NA NA
## 2008-01-04 38.9876 25.9346 14.4041 NA NA NA
## 2008-01-07 37.2064 24.1391 14.1777 NA NA NA
## 2008-01-08 37.5692 24.1391 14.3531 NA NA NA
## 2008-01-09 38.2619 24.2721 14.4663 NA NA NA
library(curl)
## Using libcurl 7.64.1 with Schannel
##
## Attaching package: 'curl'
##
## The following object is masked from 'package:readr':
##
## parse_date
library(SIT)
## Loading required package: SIT.date
##
## Attaching package: 'SIT'
##
## The following object is masked from 'package:TTR':
##
## DVI
##
## The following objects are masked from 'package:dplyr':
##
## count, lst
##
## The following object is masked from 'package:purrr':
##
## cross
##
## The following object is masked from 'package:tibble':
##
## lst
##
## The following object is masked from 'package:base':
##
## close
data <- new.env()
model <- list()
last <- xts::last
etf3 <- etf6.xts[, 1:3]
head(etf3)
## 0050 0052 0056
## 2008-01-02 39.6472 27.0983 14.5739
## 2008-01-03 38.9876 26.0676 14.3758
## 2008-01-04 38.9876 25.9346 14.4041
## 2008-01-07 37.2064 24.1391 14.1777
## 2008-01-08 37.5692 24.1391 14.3531
## 2008-01-09 38.2619 24.2721 14.4663
names(etf3)
## [1] "0050 " "0052 " "0056 "
colnames(etf3) <- c('e50', 'e52', 'e56')
names(etf3)
## [1] "e50" "e52" "e56"
md = 50
i = 'e50'
for (i in names(etf3)) {
data$prices = data$weight = data$execution.price = etf3[, i]
data$weight[] <- 1
data$execution.price[] <- NA
model[[i]] <- bt.run(data)
sma <- SMA(data$prices, md)
data$weight[] <- iif(data$prices >= sma, 1, 0)
i <- paste(i, '.sma.cross', sep = '')
model[[i]] <- bt.run(data)
}
## Latest weights :
## e50
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 6.4 8 -7
##
## Latest weights :
## e50
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 5.3 7 -6.4
##
## Latest weights :
## e52
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 7.3 10 -9.2
##
## Latest weights :
## e52
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 0.9 10 -9.2
##
## Latest weights :
## e56
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 5.3 7 -6.9
##
## Latest weights :
## e56
## 2020-04-30 100
##
## Performance summary :
## CAGR Best Worst
## 9.9 7 -5.3
strategy.performance.snapshoot(model, T)
## NULL
plotbt(model, plotX = T, log = 'y', LeftMargin = 3)
mtext('Cumulative Performance', side = 2, line = 1)
plotbt.strategy.sidebyside(model, return.table=T, make.plot = F)
## e50 e50.sma.cross e52
## Period "Jan2008 - Apr2020" "Jan2008 - Apr2020" "Jan2008 - Apr2020"
## Cagr "6.43" "5.3" "7.32"
## Sharpe "0.42" "0.49" "0.41"
## DVR "0.37" "0.39" "0.33"
## Volatility "20.06" "12.22" "26.2"
## MaxDD "-52.38" "-23.13" "-59.41"
## AvgDD "-3.57" "-3.08" "-5.19"
## VaR "-1.93" "-1.17" "-2.64"
## CVaR "-3.07" "-1.94" "-3.94"
## Exposure "99.97" "62.08" "99.97"
## e52.sma.cross e56 e56.sma.cross
## Period "Jan2008 - Apr2020" "Jan2008 - Apr2020" "Jan2008 - Apr2020"
## Cagr "0.93" "5.27" "9.94"
## Sharpe "0.14" "0.39" "0.99"
## DVR "0.03" "0.33" "0.93"
## Volatility "17.63" "17.17" "10.38"
## MaxDD "-56.14" "-54.36" "-14.95"
## AvgDD "-7.84" "-3.42" "-2.15"
## VaR "-1.74" "-1.61" "-0.93"
## CVaR "-2.93" "-2.86" "-1.62"
## Exposure "60.77" "99.97" "61.82"
data$prices = data$weight = data$execution.price = etf3
data$execution.price[] <- NA
prices <- data$prices
n <- ncol(prices)
data$weight <- ntop(prices, n)
model$etf3bh <- bt.run(data)
## Latest weights :
## e50 e52 e56
## 2020-04-30 33.33 33.33 33.33
##
## Performance summary :
## CAGR Best Worst
## 6.9 7.5 -7
etf3.m <- to.monthly(etf3, indexAt = 'lastof', OHLC = FALSE)
head(etf3.m)
## e50 e52 e56
## 2008-01-31 36.2499 23.2680 12.8646
## 2008-02-29 40.1420 25.5356 14.0079
## 2008-03-31 39.8781 25.1366 14.4324
## 2008-04-30 42.1541 26.7326 14.8851
## 2008-05-31 41.0326 26.1008 14.5625
## 2008-06-30 36.2828 23.1416 12.9608
etf3.w <- to.weekly(etf3, indexAt = 'lastof', OHLC = FALSE)
head(etf3.w)
## e50 e52 e56
## 2008-01-04 38.9876 25.9346 14.4041
## 2008-01-11 38.0310 24.3319 14.6022
## 2008-01-18 38.5917 23.8598 14.1777
## 2008-01-25 37.2064 23.6071 13.4702
## 2008-02-01 37.2064 23.4475 13.0457
## 2008-02-15 37.8001 23.9396 13.3004
data$prices = data$weight = data$execution.price = etf3.m
data$execution.price[] <- NA
prices <- data$prices
n <- ncol(prices)
constraints = new.constraints(n, lb = 0, ub = +Inf)
constraints = add.constraints(rep(1, n), 1, type = '=', constraints)
ret = prices / mlag(prices) - 1
weight = coredata(prices)
weight[] = NA
i = 36
for (i in 36:dim(weight)[1]) {
hist = ret[ (i- 36 +1):i, ]
hist = na.omit(hist)
ia = create.historical.ia(hist, 12)
ia$cov = cov(coredata(hist))
weight[i,] = min.risk.portfolio(ia, constraints)
}
## Loading required package: kernlab
##
## Attaching package: 'kernlab'
##
## The following object is masked from 'package:SIT':
##
## cross
##
## The following object is masked from 'package:purrr':
##
## cross
##
## The following object is masked from 'package:ggplot2':
##
## alpha
weight <- round(weight, digits = 2)
data$weight[] = weight
model$mvp.month = bt.run(data, type = "weight")
## Latest weights :
## e50 e52 e56
## 2020-04-30 0 0 100
##
## Performance summary :
## CAGR Best Worst
## 5.1 12.1 -12.2
weight
## e50 e52 e56
## [1,] NA NA NA
## [2,] NA NA NA
## [3,] NA NA NA
## [4,] NA NA NA
## [5,] NA NA NA
## [6,] NA NA NA
## [7,] NA NA NA
## [8,] NA NA NA
## [9,] NA NA NA
## [10,] NA NA NA
## [11,] NA NA NA
## [12,] NA NA NA
## [13,] NA NA NA
## [14,] NA NA NA
## [15,] NA NA NA
## [16,] NA NA NA
## [17,] NA NA NA
## [18,] NA NA NA
## [19,] NA NA NA
## [20,] NA NA NA
## [21,] NA NA NA
## [22,] NA NA NA
## [23,] NA NA NA
## [24,] NA NA NA
## [25,] NA NA NA
## [26,] NA NA NA
## [27,] NA NA NA
## [28,] NA NA NA
## [29,] NA NA NA
## [30,] NA NA NA
## [31,] NA NA NA
## [32,] NA NA NA
## [33,] NA NA NA
## [34,] NA NA NA
## [35,] NA NA NA
## [36,] 0.35 0.00 0.65
## [37,] 0.35 0.00 0.65
## [38,] 0.40 0.00 0.60
## [39,] 0.42 0.00 0.58
## [40,] 0.45 0.00 0.55
## [41,] 0.47 0.00 0.53
## [42,] 0.41 0.00 0.59
## [43,] 0.36 0.00 0.64
## [44,] 0.42 0.00 0.58
## [45,] 0.00 0.00 1.00
## [46,] 0.00 0.00 1.00
## [47,] 0.00 0.00 1.00
## [48,] 0.00 0.00 1.00
## [49,] 0.00 0.00 1.00
## [50,] 0.00 0.00 1.00
## [51,] 0.00 0.13 0.87
## [52,] 0.00 0.00 1.00
## [53,] 0.11 0.00 0.89
## [54,] 0.17 0.00 0.83
## [55,] 0.00 0.10 0.90
## [56,] 0.00 0.05 0.95
## [57,] 0.00 0.04 0.96
## [58,] 0.05 0.23 0.72
## [59,] 0.00 0.26 0.74
## [60,] 0.00 0.30 0.70
## [61,] 0.00 0.35 0.65
## [62,] 0.00 0.36 0.64
## [63,] 0.00 0.38 0.62
## [64,] 0.05 0.31 0.64
## [65,] 0.18 0.29 0.53
## [66,] 0.24 0.24 0.52
## [67,] 0.22 0.25 0.53
## [68,] 0.21 0.29 0.50
## [69,] 0.29 0.30 0.40
## [70,] 0.21 0.31 0.48
## [71,] 0.21 0.32 0.46
## [72,] 0.42 0.23 0.35
## [73,] 0.39 0.26 0.35
## [74,] 0.39 0.33 0.28
## [75,] 0.37 0.30 0.33
## [76,] 0.44 0.26 0.30
## [77,] 0.56 0.17 0.27
## [78,] 0.59 0.28 0.13
## [79,] 0.59 0.28 0.13
## [80,] 0.62 0.22 0.16
## [81,] 0.92 0.01 0.07
## [82,] 0.89 0.00 0.11
## [83,] 0.98 0.00 0.02
## [84,] 0.93 0.00 0.07
## [85,] 0.84 0.00 0.16
## [86,] 0.81 0.00 0.19
## [87,] 0.75 0.00 0.25
## [88,] 0.84 0.00 0.16
## [89,] 0.89 0.00 0.11
## [90,] 0.93 0.00 0.06
## [91,] 0.99 0.00 0.01
## [92,] 0.94 0.00 0.06
## [93,] 0.99 0.00 0.01
## [94,] 0.90 0.00 0.10
## [95,] 0.85 0.00 0.15
## [96,] 0.83 0.00 0.17
## [97,] 0.78 0.00 0.22
## [98,] 0.82 0.00 0.18
## [99,] 0.67 0.00 0.33
## [100,] 0.61 0.00 0.39
## [101,] 0.60 0.00 0.40
## [102,] 0.62 0.00 0.38
## [103,] 0.68 0.00 0.32
## [104,] 0.66 0.00 0.34
## [105,] 0.66 0.00 0.34
## [106,] 0.69 0.00 0.31
## [107,] 0.68 0.00 0.32
## [108,] 0.69 0.00 0.31
## [109,] 0.73 0.00 0.27
## [110,] 0.75 0.00 0.25
## [111,] 0.76 0.00 0.24
## [112,] 0.77 0.00 0.23
## [113,] 0.73 0.00 0.27
## [114,] 0.75 0.00 0.25
## [115,] 0.75 0.00 0.25
## [116,] 0.72 0.00 0.28
## [117,] 0.63 0.00 0.37
## [118,] 0.59 0.00 0.41
## [119,] 0.58 0.00 0.42
## [120,] 0.57 0.00 0.43
## [121,] 0.59 0.00 0.41
## [122,] 0.58 0.00 0.42
## [123,] 0.59 0.00 0.41
## [124,] 0.58 0.00 0.42
## [125,] 0.59 0.00 0.41
## [126,] 0.57 0.00 0.43
## [127,] 0.43 0.00 0.57
## [128,] 0.46 0.00 0.54
## [129,] 0.50 0.00 0.50
## [130,] 0.09 0.00 0.91
## [131,] 0.16 0.00 0.84
## [132,] 0.12 0.00 0.88
## [133,] 0.20 0.00 0.80
## [134,] 0.15 0.00 0.85
## [135,] 0.14 0.00 0.86
## [136,] 0.16 0.00 0.84
## [137,] 0.16 0.00 0.84
## [138,] 0.16 0.00 0.84
## [139,] 0.02 0.00 0.98
## [140,] 0.02 0.00 0.98
## [141,] 0.01 0.00 0.99
## [142,] 0.00 0.00 1.00
## [143,] 0.00 0.00 1.00
## [144,] 0.00 0.00 1.00
## [145,] 0.00 0.00 1.00
## [146,] 0.00 0.00 1.00
## [147,] 0.00 0.00 1.00
## [148,] 0.00 0.00 1.00
weight <- round(weight, digits = 2)
data$weight[] = weight
model$mvp.month = bt.run(data, type = "weight")
## Latest weights :
## e50 e52 e56
## 2020-04-30 0 0 100
##
## Performance summary :
## CAGR Best Worst
## 5.1 12.1 -12.2
plotbt.strategy.sidebyside(model, return.table=T, make.plot = T)
## e50 e50.sma.cross e52
## Period "Jan2008 - Apr2020" "Jan2008 - Apr2020" "Jan2008 - Apr2020"
## Cagr "6.43" "5.3" "7.32"
## Sharpe "0.42" "0.49" "0.41"
## DVR "0.37" "0.39" "0.33"
## Volatility "20.06" "12.22" "26.2"
## MaxDD "-52.38" "-23.13" "-59.41"
## AvgDD "-3.57" "-3.08" "-5.19"
## VaR "-1.93" "-1.17" "-2.64"
## CVaR "-3.07" "-1.94" "-3.94"
## Exposure "99.97" "62.08" "99.97"
## e52.sma.cross e56 e56.sma.cross
## Period "Jan2008 - Apr2020" "Jan2008 - Apr2020" "Jan2008 - Apr2020"
## Cagr "0.93" "5.27" "9.94"
## Sharpe "0.14" "0.39" "0.99"
## DVR "0.03" "0.33" "0.93"
## Volatility "17.63" "17.17" "10.38"
## MaxDD "-56.14" "-54.36" "-14.95"
## AvgDD "-7.84" "-3.42" "-2.15"
## VaR "-1.74" "-1.61" "-0.93"
## CVaR "-2.93" "-2.86" "-1.62"
## Exposure "60.77" "99.97" "61.82"
## etf3bh mvp.month
## Period "Jan2008 - Apr2020" "Jan2008 - Apr2020"
## Cagr "6.86" "5.14"
## Sharpe "0.45" "0.49"
## DVR "0.4" "0.37"
## Volatility "19" "11.6"
## MaxDD "-54.89" "-17.84"
## AvgDD "-2.99" "-6.42"
## VaR "-1.88" "-5.38"
## CVaR "-2.97" "-8.1"
## Exposure "99.97" "75.68"
plotbt(model)
