Resubmit hwfive
2022-04-10
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library(readxl)
X10_Industry_Portfolios <- read_excel("10_Industry_Portfolios.xlsx", range = "A1:K1149")## New names:
## * `` -> ...1library(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':
##
## closelibrary(pacman)
library(quantmod)
library(quadprog)
library(lpSolve)
library(xts)
str(X10_Industry_Portfolios)## tibble [1,148 × 11] (S3: tbl_df/tbl/data.frame)
## $ ...1 : num [1:1148] 192607 192608 192609 192610 192611 ...
## $ NoDur: num [1:1148] 1.45 3.97 1.14 -1.24 5.2 0.82 -0.67 3.37 2.73 3.35 ...
## $ Durbl: num [1:1148] 15.55 3.68 4.8 -8.23 -0.19 ...
## $ Manuf: num [1:1148] 4.69 2.81 1.15 -3.63 4.1 3.74 -0.08 5.81 1.43 0.77 ...
## $ Enrgy: num [1:1148] -1.18 3.47 -3.39 -0.78 0.01 2.82 1.29 1.47 -6.01 -5.17 ...
## $ HiTec: num [1:1148] 2.9 2.66 -0.38 -4.58 4.71 -0.02 -1.13 4.45 1.45 5.4 ...
## $ Telcm: num [1:1148] 0.83 2.17 2.41 -0.11 1.63 1.99 1.88 3.97 5.56 -2.13 ...
## $ Shops: num [1:1148] 0.11 -0.71 0.21 -2.29 6.43 0.62 -2.55 3.61 -0.41 4.46 ...
## $ Hlth : num [1:1148] 1.77 4.25 0.69 -0.57 5.42 0.11 5.05 1.71 1.01 2.74 ...
## $ Utils: num [1:1148] 7.04 -1.69 2.04 -2.63 3.71 -0.17 -1.79 4.53 0.37 1.71 ...
## $ Other: num [1:1148] 2.13 4.35 0.29 -2.84 2.11 3.47 1.5 5.05 1.22 0.83 ...a= nrow(X10_Industry_Portfolios)
a## [1] 1148date <- seq(as.Date("1926-08-01"), length = a, by = "1 month") -1
head(date)## [1] "1926-07-31" "1926-08-31" "1926-09-30" "1926-10-31" "1926-11-30"
## [6] "1926-12-31"tail(date)## [1] "2021-09-30" "2021-10-31" "2021-11-30" "2021-12-31" "2022-01-31"
## [6] "2022-02-28"class(date)## [1] "Date"industry10 <- xts(coredata(X10_Industry_Portfolios[, -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 0.0347industry.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 109.7169industry.price.sample <- industry.price['2000-01/2020-03']
head(industry.price.sample)## NoDur Durbl Manuf Enrgy HiTec Telcm Shops
## 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
## 2000-06-30 245936.7 271174.9 185625.1 249612.9 644971.0 248734.8 226392.6
## Hlth Utils Other
## 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
## 2000-06-30 828433.4 61291.70 86629.64data <- new.env()
data$prices = data$weight = data$execution.price = industry.price.sample
data$execution.price[] <- NA
data$symbolnames <- colnames(data$prices)
prices <- data$prices
n <- ncol(prices)
names(data)## [1] "prices" "weight" "symbolnames" "execution.price"data$weight <-ntop(prices, n)
head(data$weight)## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2000-01-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-02-29 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-03-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-04-30 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-05-31 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1
## 2000-06-30 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:SIT':
##
## count, lst## The following objects are masked from 'package:xts':
##
## first, last## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tidyquant)## Loading required package: lubridate##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union## Loading required package: PerformanceAnalytics##
## Attaching package: 'PerformanceAnalytics'## The following object is masked _by_ '.GlobalEnv':
##
## prices## The following object is masked from 'package:graphics':
##
## legend## ══ Need to Learn tidyquant? ════════════════════════════════════════════════════
## Business Science offers a 1-hour course - Learning Lab #9: Performance Analysis & Portfolio Optimization with tidyquant!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>last<-xts::last
model <- list()
model$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
## 6.4 12.2 -17.4 bt.detail.summary(model$equal.weight)## $System
## $System$Period
## [1] "Jan2000 - Mar2020"
##
## $System$Cagr
## [1] 6.44
##
## $System$Sharpe
## [1] 0.5
##
## $System$DVR
## [,1]
## NoDur 0.43
##
## $System$Volatility
## [1] 14.48
##
## $System$MaxDD
## [1] -48.19
##
## $System$AvgDD
## [1] -6.74
##
## $System$VaR
## 5%
## -7.18
##
## $System$CVaR
## [1] -9.78
##
## $System$Exposure
## [1] 99.59
##
##
## $Trade
## $Trade$Win.Percent
## [1] 100
##
## $Trade$Avg.Trade
## [1] 24.5
##
## $Trade$Avg.Win
## [1] 24.5
##
## $Trade$Avg.Loss
## [1] NaN
##
## $Trade$Best.Trade
## [1] 45.54
##
## $Trade$Worst.Trade
## [1] 3.96
##
## $Trade$WinLoss.Ratio
## [1] NaN
##
## $Trade$Avg.Len
## [1] 242
##
## $Trade$Num.Trades
## [1] 10
##
##
## $Period
## $Period$Win.Percent.Day
## [1] 64.2
##
## $Period$Best.Day
## [1] 12.2
##
## $Period$Worst.Day
## [1] -17.4
##
## $Period$Win.Percent.Month
## [1] 64.2
##
## $Period$Best.Month
## [1] 12.2
##
## $Period$Worst.Month
## [1] -17.4
##
## $Period$Win.Percent.Year
## [1] 66.7
##
## $Period$Best.Year
## [1] 33.7
##
## $Period$Worst.Year
## [1] -35.4capital = 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
## 6.4 12.2 -17.4 head(equal.weight$ret)## NoDur
## 2000-01-31 0.00000
## 2000-02-29 -0.03013
## 2000-03-31 0.08281
## 2000-04-30 -0.00627
## 2000-05-31 -0.01093
## 2000-06-30 0.00503plotbt.monthly.table(model$equal.weight$equity)
## Jan Feb Mar Apr May Jun Jul Aug Sep
## 2000 " NA" " -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" " 1.4" " 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
## 2000 " 0.1" " -5.7" " 3.2" " 5.0" " -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" " 7.8" "-10.5"plotbt.transition.map(model$equal.weight$weight)
strategy.performance.snapshoot(model, T)
## NULLindustry.price.sample <- industry.price['1997-01/2020-03']
data$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] 279hist <- 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$cov = cor(coredata(hist), use = 'complete.obs', method = 'pearson') * (s0%*%t(s0))
weight[36,] = min.risk.portfolio(ia, constraints)
weight[36,]## NoDur Durbl Manuf Enrgy HiTec Telcm
## 0.15197372 -0.20732075 -0.16919479 -0.19652168 0.14949635 0.21743951
## Shops Hlth Utils Other
## -0.05734666 0.53390386 0.50403534 0.07353509sum(weight[36,])## [1] 1model$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] 0.06560644model$min.var.monthly$ret[37, ]## NoDur
## 2000-01-31 0.00983317plotbt.custom.report.part1(model$min.var.monthly, model$equal.weight)
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