library(readr)
industry10 <- read_table("10_Industry_Portfolios.txt")
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## Date = col_double(),
## NoDur = col_double(),
## Durbl = col_double(),
## Manuf = col_double(),
## Enrgy = col_double(),
## HiTec = col_double(),
## Telcm = col_double(),
## Shops = col_double(),
## Hlth = col_double(),
## Utils = col_double(),
## Other = col_double()
## )
library(pacman)
library(SIT)
## Loading required package: SIT.date
## Loading required package: quantmod
## Loading required package: xts
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: TTR
## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo
##
## Attaching package: 'SIT'
## The following object is masked from 'package:TTR':
##
## DVI
## The following object is masked from 'package:base':
##
## close
p_load(quantmod, quadprog, lpSolve)
p_load(xts)
p_load(TTR)
a =nrow(industry10)
date <- seq(as.Date('1926-08-01'), length = a, by = '1 month') - 1
industry10 <- xts(industry10[-1]/100, order.by = date)
head(industry10)
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth
## 1926-07-31 0.0145 0.1555 0.0469 -0.0118 0.0290 0.0083 0.0011 0.0177
## 1926-08-31 0.0397 0.0368 0.0281 0.0347 0.0266 0.0217 -0.0071 0.0425
## 1926-09-30 0.0114 0.0480 0.0115 -0.0339 -0.0038 0.0241 0.0021 0.0069
## 1926-10-31 -0.0124 -0.0823 -0.0363 -0.0078 -0.0458 -0.0011 -0.0229 -0.0057
## 1926-11-30 0.0520 -0.0019 0.0410 0.0001 0.0471 0.0163 0.0643 0.0542
## 1926-12-31 0.0082 0.0989 0.0374 0.0282 -0.0002 0.0199 0.0062 0.0011
## Utils Other
## 1926-07-31 0.0704 0.0213
## 1926-08-31 -0.0169 0.0435
## 1926-09-30 0.0204 0.0029
## 1926-10-31 -0.0263 -0.0284
## 1926-11-30 0.0371 0.0211
## 1926-12-31 -0.0017 0.0347
class(industry10)
## [1] "xts" "zoo"
#convert into price
industry.price <- cumprod(industry10 + 1)*100
head(industry.price)
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops
## 1926-07-31 101.4500 115.5500 104.6900 98.82000 102.9000 100.8300 100.11000
## 1926-08-31 105.4776 119.8022 107.6318 102.24905 105.6371 103.0180 99.39922
## 1926-09-30 106.6800 125.5527 108.8696 98.78281 105.2357 105.5007 99.60796
## 1926-10-31 105.3572 115.2198 104.9176 98.01231 100.4159 105.3847 97.32694
## 1926-11-30 110.8358 115.0008 109.2192 98.02211 105.1455 107.1025 103.58506
## 1926-12-31 111.7446 126.3744 113.3040 100.78633 105.1245 109.2338 104.22728
## Hlth Utils Other
## 1926-07-31 101.7700 107.0400 102.1300
## 1926-08-31 106.0952 105.2310 106.5727
## 1926-09-30 106.8273 107.3777 106.8817
## 1926-10-31 106.2184 104.5537 103.8463
## 1926-11-30 111.9754 108.4326 106.0374
## 1926-12-31 112.0986 108.2483 109.7169
industry.price.sample <- industry.price['1999-12/2020-03']
head(industry.price.sample)
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops
## 1999-12-31 237045.7 313469.8 199837.8 230036.3 612042.5 290291.0 258041.7
## 2000-01-31 225714.9 310742.6 182511.9 232129.6 583276.5 278737.4 228728.2
## 2000-02-29 211743.2 285821.0 175320.9 218991.1 689374.4 269065.2 220173.7
## 2000-03-31 228174.4 315975.1 188434.9 245467.1 716535.8 289298.9 249500.9
## 2000-04-30 224021.6 345329.2 191167.2 240754.1 639938.1 266531.1 238348.2
## 2000-05-31 240196.0 299607.6 187993.9 263722.1 570568.8 238731.9 231745.9
## Hlth Utils Other
## 1999-12-31 648236.9 55331.60 88226.97
## 2000-01-31 697114.0 58684.70 84089.12
## 2000-02-29 676758.2 54424.19 78101.98
## 2000-03-31 678788.5 57564.47 89098.73
## 2000-04-30 714628.6 61939.37 86318.85
## 2000-05-31 742856.4 64355.00 89253.69
#create required input parameters in using SIT package
data <- new.env()
#create 4 required input elements in data
#data$prices <- industry.price.sample
#data$weight <- industry.price.sample
#data$execution.price <- industry.price.sample
#data$symbolnames : asset names
data$prices = data$weight = data$execution.price = industry.price.sample
data$execution.price[] <- NA
data$symbolnames <- colnames(data$prices)
prices <- data$prices
n <- ncol(prices)
names(data)
## [1] "prices" "weight" "symbolnames" "execution.price"
#assign equal weights to 10 assets
data$weight <-ntop(prices, n)
head(data$weight)
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 1999-12-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-01-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-02-29 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-03-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-04-30 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-05-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
#create a list model
model <- list()
model$equal.weight <-bt.run(data, trade.summary = T)
## Latest weights :
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2020-03-31 10 10 10 10 10 10 10 10 10 10
##
## Performance summary :
## CAGR Best Worst
## 6.3 12.2 -17.4
capital = 100000
data$weight[] = (capital / prices) * data$weight
equal.weight = bt.run(data, type='share')
## Latest weights :
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2020-03-31 10 10 10 10 10 10 10 10 10 10
##
## Performance summary :
## CAGR Best Worst
## 6.3 12.2 -17.4
plotbt.monthly.table(model$equal.weight$equity)
## Jan Feb Mar Apr May Jun Jul Aug Sep
## 1999 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2000 " -2.5" " -3.0" " 8.3" " -0.6" " -1.1" " 0.5" " -0.9" " 5.2" " -0.4"
## 2001 " 2.5" " -4.5" " -4.7" " 6.8" " 0.7" " -2.3" " -0.6" " -5.1" " -8.4"
## 2002 " -1.0" " 0.1" " 4.8" " -3.8" " -1.1" " -6.8" " -9.3" " 0.8" " -9.8"
## 2003 " -3.0" " -2.3" " 1.0" " 7.8" " 6.4" " 1.5" " 1.3" " 3.3" " -1.6"
## 2004 " 1.3" " 1.8" " -1.0" " -0.6" " 0.3" " 2.4" " -3.2" " 0.2" " 2.0"
## 2005 " -2.2" " 2.9" " -1.9" " -3.0" " 3.9" " 1.2" " 4.4" " -0.7" " 0.4"
## 2006 " 4.0" " 0.0" " 1.5" " 1.1" " -1.7" " 0.8" " 0.4" " 2.2" " 1.8"
## 2007 " 2.0" " -1.0" " 1.7" " 4.3" " 3.7" " -1.3" " -3.4" " 1.0" " 3.4"
## 2008 " -6.1" " -2.3" " -0.6" " 4.8" " 2.7" " -8.0" " -1.3" " 1.9" " -9.3"
## 2009 " -7.2" " -9.8" " 8.1" " 12.2" " 4.1" " 1.0" " 8.2" " 2.5" " 4.0"
## 2010 " -3.4" " 3.3" " 6.1" " 2.5" " -7.5" " -5.4" " 7.9" " -4.1" " 9.3"
## 2011 " 1.4" " 3.5" " 1.2" " 3.4" " -0.7" " -1.4" " -2.7" " -5.4" " -7.3"
## 2012 " 4.4" " 4.1" " 2.4" " -0.7" " -5.3" " 3.2" " 1.5" " 1.9" " 2.9"
## 2013 " 5.5" " 1.4" " 4.3" " 2.3" " 1.9" " -0.7" " 5.6" " -2.8" " 3.7"
## 2014 " -3.2" " 4.9" " 0.6" " 0.9" " 1.8" " 2.7" " -2.7" " 4.3" " -2.9"
## 2015 " -2.5" " 5.3" " -1.2" " 0.8" " 0.8" " -1.8" " 0.9" " -5.8" " -3.0"
## 2016 " -4.6" " 0.6" " 7.2" " 1.6" " 1.0" " 1.0" " 3.3" " -0.4" " 0.2"
## 2017 " 1.6" " 2.9" " 0.3" " 0.8" " 0.7" " 0.5" " 1.7" " -0.3" " 2.6"
## 2018 " 4.1" " -5.0" " -1.6" " 0.7" " 1.9" " 1.7" " 2.7" " 2.1" " 0.4"
## 2019 " 8.1" " 3.2" " 1.0" " 3.2" " -6.7" " 7.2" " 0.6" " -2.0" " 2.1"
## 2020 " -0.6" " -8.5" "-15.3" " NA" " NA" " NA" " NA" " NA" " NA"
## Avg " -0.1" " -0.1" " 1.1" " 2.2" " 0.3" " -0.2" " 0.7" " -0.1" " -0.5"
## Oct Nov Dec Year MaxDD
## 1999 " NA" " NA" " NA" " 0.0" " 0.0"
## 2000 " 0.1" " -5.7" " 3.2" " 2.4" " -6.0"
## 2001 " 2.0" " 6.5" " 2.1" " -6.1" "-17.3"
## 2002 " 6.7" " 6.0" " -4.4" "-18.0" "-26.9"
## 2003 " 5.6" " 1.5" " 5.9" " 30.2" " -5.3"
## 2004 " 1.4" " 4.9" " 3.3" " 13.3" " -3.2"
## 2005 " -3.1" " 2.9" " 0.1" " 4.8" " -4.8"
## 2006 " 4.1" " 2.3" " 0.8" " 18.6" " -1.7"
## 2007 " 2.1" " -4.0" " -0.3" " 8.0" " -4.6"
## 2008 "-17.4" " -6.4" " 1.6" "-35.4" "-36.4"
## 2009 " -1.8" " 5.9" " 3.4" " 32.6" "-16.3"
## 2010 " 4.2" " 1.2" " 6.4" " 20.4" "-12.5"
## 2011 " 11.5" " -0.3" " 1.0" " 2.9" "-16.5"
## 2012 " -0.8" " 0.9" " 1.3" " 16.5" " -5.9"
## 2013 " 4.1" " 2.2" " 2.3" " 33.7" " -2.8"
## 2014 " 2.6" " 2.2" " -0.1" " 11.3" " -3.2"
## 2015 " 7.5" " 0.1" " -2.5" " -2.1" " -9.4"
## 2016 " -2.4" " 4.2" " 2.2" " 14.1" " -4.6"
## 2017 " 1.1" " 3.2" " 1.2" " 17.4" " -0.3"
## 2018 " -5.8" " 2.4" " -9.4" " -6.5" "-12.6"
## 2019 " 1.8" " 2.7" " 3.3" " 26.3" " -6.7"
## 2020 " NA" " NA" " NA" "-22.9" "-22.9"
## Avg " 1.2" " 1.6" " 1.1" " 7.3" "-10.0"
plotbt.transition.map(model$equal.weight$weight)
strategy.performance.snapshoot(model, T)
## NULL
# reset sample data range
industry.price.sample <- industry.price['1997-01/2020-03']
data$prices <- industry.price.sample
data$weight <- industry.price.sample
data$execution.price <- industry.price.sample
data$execution.price[] <- NA
prices <- data$prices
#create constraints
constraints = new.constraints(n, lb=-Inf, ub=+Inf)
constraints = add.constraints(rep(1,n), 1, type = '=', constraints)
ret = prices / mlag(prices) -1
#compute MVA weight for each month
weight = coredata(prices)
weight[] = NA
nrow(prices)
## [1] 279
hist <- na.omit(ret[1:36,])
for( i in 36 : (dim(weight)[1]) ) {
#using 36 historical monthly returns
hist = ret[ (i- 36 +1):i, ]
hist = na.omit(hist)
#create historical input assumptions
ia = create.historical.ia(hist, 12)
s0 = apply(coredata(hist),2,sd)
ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weight[i,] = min.risk.portfolio(ia, constraints)
}
data$weight[] = weight
ia = create.historical.ia(hist, 12)
s0 = apply(coredata(hist), 2, sd)
ia$cov = cor(coredata(hist), use = 'complete.obs', method = 'pearson') * (s0%*%t(s0))
weight[36,] = min.risk.portfolio(ia, constraints)
weight[36,]
## NoDur Durbl Manuf Enrgy HiTec Telcm
## 0.15197372 -0.20732075 -0.16919479 -0.19652168 0.14949635 0.21743951
## Shops Hlth Utils Other
## -0.05734666 0.53390386 0.50403534 0.07353509
sum(weight[36,])
## [1] 1
model$min.var.monthly <- bt.run(data, trade.summary = T)
## Latest weights :
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2020-03-31 -0.72 -16.3 -16.07 -12.18 13.28 31.46 -16.08 39.1 56.35 21.16
##
## Performance summary :
## CAGR Best Worst
## 6.1 8.8 -15.6
sum(as.numeric(weight[36,])*as.numeric(ret[37,]))
## [1] 0.06560644
model$min.var.monthly$ret[37, ]
## NoDur
## 2000-01-31 0.00983317
plotbt.custom.report.part1(model$min.var.monthly, model$equal.weight)
layout(1:2)
plotbt.transition.map(model$min.var.monthly$weight)
legend('topright', legend = 'min.var.monthly', bty = 'n')
plotbt.transition.map(model$equal.weight$weight)
legend('topright', legend = 'equal weight', bty = 'n')
strategy.performance.snapshoot(model, T)
## NULL
model <- rev(model)
plotbt.custom.report(model)
