bt_mvp
107035161 Javza
2019年4月11日
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Load Systematic Investor Toolbox (SIT)
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')## Warning: package 'quantmod' was built under R version 3.5.3## Warning: package 'xts' was built under R version 3.5.3## Warning: package 'zoo' was built under R version 3.5.3##
## Attaching package: 'zoo'## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric## Warning: package 'TTR' was built under R version 3.5.3##
## Attaching package: 'TTR'## The following object is masked _by_ '.GlobalEnv':
##
## DVI## Version 0.4-0 included new data defaults. See ?getSymbols.tickers = spl('SPY,QQQ,EEM,IWM,EFA,TLT,IYR,GLD')
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.## pausing 1 second between requests for more than 5 symbols
## pausing 1 second between requests for more than 5 symbols
## pausing 1 second between requests for more than 5 symbols
## pausing 1 second between requests for more than 5 symbols## [1] "SPY" "QQQ" "EEM" "IWM" "EFA" "TLT" "IYR" "GLD"for(i in ls(data)) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
data.weekly <- new.env()
for(i in tickers) data.weekly[[i]] = to.weekly(data[[i]], indexAt='endof')
bt.prep(data, align='remove.na', dates='1990::2018')
bt.prep(data.weekly, align='remove.na', dates='1990::2018')*****************************************************************
Code Strategies
******************************************************************
prices = data$prices
n = ncol(prices)
n## [1] 8# find week ends
week.ends = endpoints(prices, 'weeks')
week.ends = week.ends[week.ends > 0]
# Equal Weight 1/N Benchmark
data$weight[] = NA
data$weight[week.ends,] = ntop(prices[week.ends,], n)
capital = 100000
data$weight[] = (capital / prices) * data$weight
equal.weight = bt.run(data, type='share')## Latest weights :
## EEM EFA GLD IWM IYR QQQ SPY TLT
## 2018-12-31 08:00:00 12.5 12.5 12.5 12.5 12.5 12.5 12.5 12.5
##
## Performance summary :
## CAGR Best Worst
## 8.1 10.2 -8.6 *****************************************************************
Create Constraints
*****************************************************************
constraints = new.constraints(n, lb = -Inf, ub = +Inf)
# SUM x.i = 1
constraints = add.constraints(rep(1, n), 1, type = '=', constraints)
ret = prices / mlag(prices) - 1
weight = coredata(prices)
weight[] = NA
for( i in week.ends[week.ends >= (63 + 1)] ) {
# one quarter is 63 days
hist = ret[ (i- 63 +1):i, ]
# create historical input assumptions
ia = create.historical.ia(hist, 252)
s0 = apply(coredata(hist),2,sd)
ia$cov = cor(coredata(hist), use='complete.obs',method='pearson') * (s0 %*% t(s0))
weight[i,] = min.risk.portfolio(ia, constraints)
}
# Minimum Variance
data$weight[] = weight
capital = 100000
data$weight[] = (capital / prices) * data$weight
min.var.daily = bt.run(data, type='share', capital=capital)## Latest weights :
## EEM EFA GLD IWM IYR QQQ SPY TLT
## 2018-12-31 08:00:00 -2.79 16.47 32.64 -6.75 2.35 -24.88 44.89 38.07
##
## Performance summary :
## CAGR Best Worst
## 8.8 2.8 -2.6 *****************************************************************
Code Strategies: Weekly
******************************************************************
retw = data.weekly$prices / mlag(data.weekly$prices) - 1
weightw = coredata(prices)
weightw[] = NA
week.ends<-week.ends[-length(week.ends)]
for( i in week.ends[week.ends >= (63 + 1)] ) {
# map
j = which(index(ret[i,]) == index(retw))
# one quarter = 13 weeks
hist = retw[ (j- 13 +1):j, ]
# create historical input assumptions
ia = create.historical.ia(hist, 52)
s0 = apply(coredata(hist),2,sd)
ia$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$weight
min.var.weekly = bt.run(data, type='share', capital=capital)## Latest weights :
## EEM EFA GLD IWM IYR QQQ SPY TLT
## 2018-12-31 08:00:00 -6.45 50.97 44.24 -26.24 5.46 -10.94 22.15 20.82
##
## Performance summary :
## CAGR Best Worst
## 10.8 4.3 -4.8 *****************************************************************
Create Report
******************************************************************
plotbt.custom.report.part1(min.var.weekly, min.var.daily, equal.weight)
# plot Daily and Weekly transition maps
layout(1:2)
plotbt.transition.map(min.var.daily$weight)
legend('topright', legend = 'min.var.daily', bty = 'n')
plotbt.transition.map(min.var.weekly$weight)
legend('topright', legend = 'min.var.weekly', bty = 'n')