#======================================================
# Use systematic investors toolbox (SIT)
# Load Systematic Investor Toolbox (SIT)
# http://www.r-bloggers.com/backtesting-minimum-variance-portfolios/
# https://systematicinvestor.wordpress.com/2011/12/13/backtesting-minimum-variance-portfolios/
# https://systematicinvestor.wordpress.com/2013/03/22/maximum-sharpe-portfolio/
#=========================================================
#setInternet2(TRUE)
# Load Systematic Investor Toolbox (SIT)
rm(list=ls())
#setInternet2(TRUE)
con = gzcon(url('https://github.com/systematicinvestor/SIT/raw/master/sit.gz', 'rb'))
source(con)
close(con)
#*****************************************************************
# Load historical data
#******************************************************************
load.packages('quantmod,quadprog,lpSolve')
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
##
## Attaching package: 'TTR'
## The following object is masked _by_ '.GlobalEnv':
##
## DVI
## Version 0.4-0 included new data defaults. See ?getSymbols.
tickers = spl('EA,LMT,TSLA')
data <- new.env()
getSymbols(tickers, src = 'yahoo', from = '1980-01-01', env = data, auto.assign = T)
## 'getSymbols' currently uses auto.assign=TRUE by default, but will
## use auto.assign=FALSE in 0.5-0. You will still be able to use
## 'loadSymbols' to automatically load data. getOption("getSymbols.env")
## and getOption("getSymbols.auto.assign") will still be checked for
## alternate defaults.
##
## This message is shown once per session and may be disabled by setting
## options("getSymbols.warning4.0"=FALSE). See ?getSymbols for details.
## [1] "EA" "LMT" "TSLA"
for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
names(data)
## [1] "TSLA" "LMT" "EA" ".getSymbols"
data.weekly <- new.env()
for(i in tickers) data.weekly[[i]] = to.weekly(data[[i]], indexAt='endof')
data.monthly <- new.env()
for(i in tickers) data.monthly[[i]] = to.monthly(data[[i]], indexAt='endof')
bt.prep(data, align='remove.na', dates='2010::2018')
bt.prep(data.monthly, align='remove.na', dates='2010::2018')
names(data)
## [1] "prices" "TSLA" "LMT" "dates"
## [5] "EA" "weight" ".getSymbols" "symbolnames"
## [9] "execution.price"
#*****************************************************************
# Code Strategies
#******************************************************************
prices = data$prices
n = ncol(prices)
n
## [1] 3
# find week ends
week.ends = endpoints(prices, 'weeks')
week.ends = week.ends[week.ends > 0]
month.ends = endpoints(prices, 'months')
month.ends = month.ends[month.ends > 0]
# Equal Weight 1/N Benchmark
data$weight[] = NA
data$weight[week.ends,] = ntop(prices[week.ends,], n)
data$weight[month.ends,] = ntop(prices[month.ends,], n)
#capital = 100000
#data$weight[] = (capital / prices) * data$weight
equal.weight = bt.run(data)
## Latest weights :
## EA LMT TSLA
## 2018-12-31 08:00:00 33.33 33.33 33.33
##
## Performance summary :
## CAGR Best Worst
## 30.7 8.1 -9
names(equal.weight)
## [1] "weight" "type" "ret" "best" "worst"
## [6] "equity" "cagr" "dates.index"
head(equal.weight$equity)
## EA
## 2010-06-29 08:00:00 1.0000000
## 2010-06-30 08:00:00 1.0000000
## 2010-07-01 08:00:00 0.9720431
## 2010-07-02 08:00:00 0.9373379
## 2010-07-06 08:00:00 0.8829540
## 2010-07-07 08:00:00 0.8921729
#*****************************************************************
# Create Constraints
#*****************************************************************
constraints = new.constraints(n, lb = -Inf, ub = +Inf)
# SUM x.i = 1
constraints = add.constraints(rep(1, n), 1, type = '=', constraints)
ret = prices / mlag(prices) - 1
weight = coredata(prices)
weight[] = NA
for( i in month.ends[month.ends >= (756 + 1)] ) {
# one quarter is 63 days
hist = ret[ (i- 756 +1):i, ]
# create historical input assumptions
ia = create.historical.ia(hist, 756)
#s0 = apply(coredata(hist),2,sd)
#ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weight[i,] = min.risk.portfolio(ia, constraints)
}
tail(weight,10)
## EA LMT TSLA
## [2133,] NA NA NA
## [2134,] NA NA NA
## [2135,] NA NA NA
## [2136,] NA NA NA
## [2137,] NA NA NA
## [2138,] NA NA NA
## [2139,] NA NA NA
## [2140,] NA NA NA
## [2141,] NA NA NA
## [2142,] 0.1918519 0.7383225 0.06982564
# Minimum Variance
data$weight[] = weight
#capital = 100000
#data$weight[] = (capital / prices) * data$weight
min.var.daily = bt.run(data)
## Latest weights :
## EA LMT TSLA
## 2018-12-31 08:00:00 17.15 75.51 7.34
##
## Performance summary :
## CAGR Best Worst
## 12.4 6.1 -5.4
names(min.var.daily)
## [1] "weight" "type" "ret" "best" "worst"
## [6] "equity" "cagr" "dates.index"
#min.var.daily$equity
#min.var.daily$ret
#min.var.daily$weight
#
#*****************************************************************
# Code Strategies: Weekly
#******************************************************************
retw = data.weekly$prices / mlag(data.weekly$prices) - 1
weightw = coredata(prices)
weightw[] = NA
week.ends<-week.ends[-length(week.ends)]
for( i in month.ends[month.ends >= (756 + 1)] ) {
# map
j = which(index(ret[i,]) == index(retw))
# one quarter = 13 weeks
#hist = retw[ (j- 156 +1):j, ]
# create historical input assumptions
ia = create.historical.ia(hist, n)
s0 = apply(coredata(hist),2,sd)
ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weightw[i,] = min.risk.portfolio(ia, constraints)
}
data$weight[] = weightw
capital = 100000
data$weight[] = (capital / prices) * data$weight
min.var.weekly = bt.run(data, type='share', capital=capital)
## Latest weights :
## EA LMT TSLA
## 2018-12-31 08:00:00 20.34 72.18 7.48
##
## Performance summary :
## CAGR Best Worst
## 13.1 5.1 -5.2
#*****************************************************************
# Code Strategies: Monthly
#******************************************************************
retm = data.monthly$prices / mlag(data.monthly$prices) - 1
weightm = coredata(prices)
weightm[] = NA
month.ends<-month.ends[-length(month.ends)]
for( i in month.ends[month.ends >= (756 + 1)] ) {
# map
j = which(index(ret[i,]) == index(retm))
# one quarter = 13 weeks
hist = retm[ (j- 36 +1):j, ]
# create historical input assumptions
ia = create.historical.ia(hist, 36)
s0 = apply(coredata(hist),2,sd)
ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weightm[i,] = min.risk.portfolio(ia, constraints)
}
data$weight[] = weightm
capital = 100000
data$weight[] = (capital / prices) * data$weight
min.var.monthly = bt.run(data, type='share', capital=capital)
## Latest weights :
## EA LMT TSLA
## 2018-12-31 08:00:00 3.47 72.78 23.76
##
## Performance summary :
## CAGR Best Worst
## 13.2 5.7 -5.1
#*****************************************************************
# Create Report
#******************************************************************
plotbt.custom.report.part3(min.var.daily, min.var.weekly, min.var.monthly, equal.weight)
#
models<-list("Min.var.daily" = min.var.daily,
"Min.var.weekly" = min.var.weekly,
"Min.var.monthly" = min.var.monthly,
"Equal.weight" = equal.weight)
#
strategy.performance.snapshoot(models, T)
## NULL
strategy.performance.snapshoot(models, control=list(comparison=T),
sort.performance=T)
plotbt.strategy.sidebyside(models, return.table=T)
## Min.var.daily Min.var.weekly Min.var.monthly
## Period "Jun2010 - Dec2018" "Jun2010 - Dec2018" "Jun2010 - Dec2018"
## Cagr "12.43" "13.14" "13.23"
## Sharpe "0.95" "1" "0.97"
## DVR "0.84" "0.89" "0.86"
## Volatility "13.27" "13.25" "13.75"
## MaxDD "-30.59" "-30.05" "-28.22"
## AvgDD "-2.46" "-2.34" "-2.57"
## VaR "-1.33" "-1.33" "-1.41"
## CVaR "-2.13" "-2.11" "-2.18"
## Exposure "63.68" "63.68" "63.68"
## Equal.weight
## Period "Jun2010 - Dec2018"
## Cagr "30.71"
## Sharpe "1.2"
## DVR "1.13"
## Volatility "24.93"
## MaxDD "-28.83"
## AvgDD "-3.6"
## VaR "-2.42"
## CVaR "-3.39"
## Exposure "99.91"
plotbt.strategy.sidebyside(min.var.daily, return.table=T)
## min.var.daily
## Period "Jun2010 - Dec2018"
## Cagr "12.43"
## Sharpe "0.95"
## DVR "0.84"
## Volatility "13.27"
## MaxDD "-30.59"
## AvgDD "-2.46"
## VaR "-1.33"
## CVaR "-2.13"
## Exposure "63.68"
# plot Daily and Weekly transition maps
layout(1:3)
plotbt.transition.map(min.var.daily$weight)
legend('topright', legend = 'min.var.daily', bty = 'n')
plotbt.transition.map(min.var.weekly$weight)
legend('topright', legend = 'min.var.weekly', bty = 'n')
plotbt.transition.map(min.var.monthly$weight)
legend('topright', legend = 'min.var.monthy', bty = 'n')
