1.Download 10 industry portfolio returns
rm(list=ls())industry10 <- read.table('10_Industry_Portfolios_Wout_Div01.txt', header = TRUE )str(industry10)## 'data.frame': 1126 obs. of 11 variables:
## $ Date : int 192607 192608 192609 192610 192611 192612 192701 192702 192703 192704 ...
## $ NoDur: num 1.31 3.7 0.43 -1.47 4.92 0.04 -0.9 3.01 1.86 3.2 ...
## $ Durbl: num 15.07 3.09 4.48 -8.47 -2.37 ...
## $ Manuf: num 4.39 2.46 0.56 -3.92 3.71 2.72 -0.37 5.48 0.79 0.49 ...
## $ Enrgy: num -1.3 3.13 -3.86 -0.9 -0.62 2.21 1.08 0.98 -6.52 -5.39 ...
## $ HiTec: num 2.84 2.37 -1.17 -4.64 4.46 -0.83 -1.2 4.2 0.64 5.4 ...
## $ Telcm: num 0.83 2.17 0.89 -0.11 1.63 0.51 1.88 3.97 4.16 -2.13 ...
## $ Shops: num -0.13 -0.95 0.02 -2.54 5.9 0.43 -2.79 3.11 -0.58 3.82 ...
## $ Hlth : num 1.11 3.59 0.52 -1.2 4.78 -0.68 4.43 1.08 0.81 2.16 ...
## $ Utils: num 6.89 -2.07 1.65 -2.88 3.31 -0.67 -1.99 4.13 -0.06 1.46 ...
## $ Other: num 1.88 4 -0.15 -3.11 1.65 2.52 1.19 4.68 0.75 0.73 ...con = gzcon(url('http://github.com/systematicinvestor/SIT/raw/master/sit.gz', 'rb'))source(con)close(con)Install packmanagement library
library(pacman)p_load(quantmod, quadprog, lpSolve)p_load(xts)date <- seq(as.Date("1926-08-01"), length = 1126, by = '1 month')-1head(date)## [1] "1926-07-31" "1926-08-31" "1926-09-30" "1926-10-31" "1926-11-30"
## [6] "1926-12-31"tail(date)## [1] "2019-11-30" "2019-12-31" "2020-01-31" "2020-02-29" "2020-03-31"
## [6] "2020-04-30"industry10.xts <- xts(coredata(industry10[, -1])/100, order.by = date)head(industry10.xts)## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth
## 1926-07-31 0.0131 0.1507 0.0439 -0.0130 0.0284 0.0083 -0.0013 0.0111
## 1926-08-31 0.0370 0.0309 0.0246 0.0313 0.0237 0.0217 -0.0095 0.0359
## 1926-09-30 0.0043 0.0448 0.0056 -0.0386 -0.0117 0.0089 0.0002 0.0052
## 1926-10-31 -0.0147 -0.0847 -0.0392 -0.0090 -0.0464 -0.0011 -0.0254 -0.0120
## 1926-11-30 0.0492 -0.0237 0.0371 -0.0062 0.0446 0.0163 0.0590 0.0478
## 1926-12-31 0.0004 0.0956 0.0272 0.0221 -0.0083 0.0051 0.0043 -0.0068
## Utils Other
## 1926-07-31 0.0689 0.0188
## 1926-08-31 -0.0207 0.0400
## 1926-09-30 0.0165 -0.0015
## 1926-10-31 -0.0288 -0.0311
## 1926-11-30 0.0331 0.0165
## 1926-12-31 -0.0067 0.0252convert into prices
industry.price <- cumprod(industry10.xts + 1 ) * 100industry.price.sample <- industry10.xts['199912/202003']models.tw<-list()set up input of SiT
data <- new.env()data$prices <- industry.price.sampledata$wieght <- industry.price.sample * NAdata$execution.price <- industry.price.sampledata\(prices = data\)weight = data$execution.price = industry.price.sample
data$execution.price[] <- NAdata$symbolnames <- colnames(data$prices)prices <- data$pricesn <- ncol(prices)2.Compute equal weight portfolio returns Each month from 2000/01 to 2020/03. Denote this strategy as the Benchmark portfolio and create its backtesting report using SIT.
create portfolio equal weight
data$weight <- ntop(prices, n)using backtesting function: bt.run()
model <- list()library(zoo)model$equal.weight <- bt.run(data, trade.summary = T)## Latest weights :
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2020-03-31 10 10 10 10 10 10 10 10 10 10
##
## Performance summary :
## CAGR Best Worst
## -100 0 -100 here is a list of functions showing trade summary report
bt.detail.summary(model$equal.weight)## $System
## $System$Period
## [1] "Dec1999 - Mar2020"
##
## $System$Cagr
## [1] -100
##
## $System$Sharpe
## [1] -53.89
##
## $System$DVR
## [,1]
## NoDur -0.66
##
## $System$Volatility
## [1] 22.18
##
## $System$MaxDD
## [1] -100
##
## $System$AvgDD
## [1] -100
##
## $System$VaR
## 5%
## -100
##
## $System$CVaR
## [1] NaN
##
## $System$Exposure
## [1] 99.59
##
##
## $Trade
## $Trade$Win.Percent
## [1] 20
##
## $Trade$Avg.Trade
## [1] -84.1
##
## $Trade$Avg.Win
## [1] 35.7
##
## $Trade$Avg.Loss
## [1] -114.1
##
## $Trade$Best.Trade
## [1] 37.87
##
## $Trade$Worst.Trade
## [1] -552.81
##
## $Trade$WinLoss.Ratio
## [1] 0.31
##
## $Trade$Avg.Len
## [1] 243
##
## $Trade$Num.Trades
## [1] 10
##
##
## $Period
## $Period$Win.Percent.Day
## [1] 0
##
## $Period$Best.Day
## [1] 0
##
## $Period$Worst.Day
## [1] -100
##
## $Period$Win.Percent.Month
## [1] 0
##
## $Period$Best.Month
## [1] -100
##
## $Period$Worst.Month
## [1] -100
##
## $Period$Win.Percent.Year
## [1] 0
##
## $Period$Best.Year
## [1] -100
##
## $Period$Worst.Year
## [1] -100plotbt.monthly.table(model$equal.weight$equity)## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Warning in min(iequity/cummax(iequity) - 1, na.rm = T): no non-missing arguments
## to min; returning Inf
## Jan Feb Mar Apr May Jun Jul Aug Sep Oct
## 1999 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2000 "-100" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2001 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2002 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2003 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2004 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2005 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2006 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2007 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2008 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2009 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2010 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2011 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2012 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2013 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2014 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2015 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2016 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2017 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2018 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2019 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## 2020 " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA" " NA"
## Avg "-100" " NaN" " NaN" " NaN" " NaN" " NaN" " NaN" " NaN" " NaN" " NaN"
## Nov Dec Year MaxDD
## 1999 " NA" " NA" " 0" " 0"
## 2000 " NA" " NA" "-100" "-100"
## 2001 " NA" " NA" " NA" " NA"
## 2002 " NA" " NA" " NA" " NA"
## 2003 " NA" " NA" " NA" " NA"
## 2004 " NA" " NA" " NA" " NA"
## 2005 " NA" " NA" " NA" " NA"
## 2006 " NA" " NA" " NA" " NA"
## 2007 " NA" " NA" " NA" " NA"
## 2008 " NA" " NA" " NA" " NA"
## 2009 " NA" " NA" " NA" " NA"
## 2010 " NA" " NA" " NA" " NA"
## 2011 " NA" " NA" " NA" " NA"
## 2012 " NA" " NA" " NA" " NA"
## 2013 " NA" " NA" " NA" " NA"
## 2014 " NA" " NA" " NA" " NA"
## 2015 " NA" " NA" " NA" " NA"
## 2016 " NA" " NA" " NA" " NA"
## 2017 " NA" " NA" " NA" " NA"
## 2018 " NA" " NA" " NA" " NA"
## 2019 " NA" " NA" " NA" " NA"
## 2020 " NA" " NA" " NA" " NA"
## Avg " NaN" " NaN" " -50" " -50"plotbt.transition.map(model$equal.weight$weight)
strategy.performance.snapshoot(model, T)## Warning in xy.coords(x, y, xlabel, ylabel, log): 243 y values <= 0 omitted from
## logarithmic plot## Warning in plot.window(...): nonfinite axis limits [GScale(-inf,0,2, .); log=1]
## NULL3.Compute MVP portfolio returns by rebalancing EACH month starting from 2000/01 to 2020/03. Use in-sample data range of 36 months to compute covariance matrix.
Denote this strategy as the MVP portfolio and create its backtesting report using SIT.
create input for MVp portfolio
reset sample data range
industry.price.sample <- industry.price['199701/202003']data$prices <- industry.price.sampledata$weight <- industry.price.sampledata$execution.price <- industry.price.sampledata$execution.price <- NAprices <- data$pricescreate constraints
if lb = 0 it means no short sales
constraints = new.constraints(n, lb = -Inf, ub = +Inf)SUM x.i = 1
constraints = add.constraints(rep(1, n), 1, type = '=', constraints)ret = prices / mlag(prices) -1 compute MVp weight for each month
weight <- coredata(prices)weight[] <- NAi = 36i = 245for( i in 36 : (dim(weight)[1]) ) {
# using 36 historical monthly returns
hist = ret[ (i- 36 +1):i, ]
hist = na.omit(hist)
# create historical input assumptions
ia = create.historical.ia(hist, 12)
s0 = apply(coredata(hist),2,sd)
ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weight[i,] = min.risk.portfolio(ia, constraints)
}apply(weight, 1, sum)
data$weight[] = weight #capital = 100000 #data\(weight[] = (capital / prices) * data\)weight
models.tw$min.var.monthly = bt.run(data)## Latest weights :
## NoDur Durbl Manuf Enrgy HiTec Telcm Shops Hlth Utils Other
## 2020-03-31 -1.64 -16.74 -15.26 -12.81 11.98 31.93 -15.02 37.17 56.55 23.83
##
## Performance summary :
## CAGR Best Worst
## 3.8 8.9 -15