Quarterly Tactical Strategy

#*****************************************************************
# Load historical data
#*****************************************************************
library(SIT)
load.packages('quantmod')

tickers = '
US.SC = VB + NAESX # U.S. Small Cap
EM.BOND = VWOB + PREMX # Emerging Market Government Bond
EM.EQ = VWO + VEIEX # Emerging Markets
US.CORP.BOND = VCIT + VFICX # Intermediate Corporate Bond
US.MBS = VMBS + VFIIX # Mortgage-Backed Bonds
US.LC = SPY + VFINX # S&P 500
US.REIT = VNQ +  VGSIX # MSCI U.S. REIT
INTL.EQ = VEU + VGTSX # FTSE All-World ex-U.S. 
CASH = TLT + VUSTX # Long-Tern Treasury
'
           
data <- new.env()
getSymbols.extra(tickers, src = 'yahoo', from = '1970-01-01', env = data, set.symbolnames = T, auto.assign = T)
for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)

#print(bt.start.dates(data))
bt.prep(data, align='remove.na', fill.gaps = T)

# Check data
plota.matplot(scale.one(data$prices),main='Asset Perfromance')

#*****************************************************************
# Setup
#*****************************************************************
data$universe = data$prices > 0
  # do not allocate to CASH, or BENCH
  data$universe$CASH = NA

prices = data$prices * data$universe
  n = ncol(prices)
  nperiods = nrow(prices)


frequency = 'quarters'
# find period ends, can be 'weeks', 'months', 'quarters', 'years'
period.ends = endpoints(prices, frequency)
  period.ends = period.ends[period.ends > 0]

models = list()

commission = list(cps = 0.01, fixed = 10.0, percentage = 0.0)

# lag prices by 1 day
#prices = mlag(prices)
#*****************************************************************
# Equal Weight each re-balancing period
#******************************************************************
data$weight[] = NA
  data$weight[period.ends,] = ntop(prices[period.ends,], n)
models$ew = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)

#*****************************************************************
# Strategy:
#
# Select the top-ranked asset each quarter based on 
# 5-month and 20-day total returns weighted 50% each.
#
# A cash filter must be passed for the top-ranked mutual fund to be selected 
# in any given period. The cash filter is the 3-month moving average. 
#******************************************************************
mom.5m = prices / mlag(prices, 5*21) -1
mom.20d = prices / mlag(prices, 20) -1 

# compute 3 month moving average
sma = bt.apply.matrix(prices, SMA, 3*21)

# go to cash if prices falls below 3 month moving average
go2cash = prices <= sma
  go2cash.d = ifna(go2cash, T)


# compute moving average in months
sma = bt.apply.matrix(prices, SMA, 3, periodicity='months')

go2cash = prices <= sma
  go2cash.m = ifna(go2cash, T)


# all logic below is done at period.ends
mom.5m = mom.5m[period.ends,]
mom.20d = mom.20d[period.ends,]
go2cash.d = go2cash.d[period.ends,]
go2cash.m = go2cash.m[period.ends,]

#*****************************************************************
# Rank total score
#*****************************************************************
# target allocation
target.allocation = ntop(0.5 * mom.5m + 0.5 * mom.20d ,1)

# If asset is above it's 3 month moving average it gets allocation
weight = iif(go2cash.d, 0, target.allocation)

# otherwise, it's weight is allocated to cash
weight$CASH = 1 - rowSums(weight)

data$weight[] = NA
  data$weight[period.ends,] = weight
models$QTS.d = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)


# same but using monthly moving average to trigger go to cash
weight = iif(go2cash.m, 0, target.allocation)
weight$CASH = 1 - rowSums(weight)

data$weight[] = NA
  data$weight[period.ends,] = weight
models$QTS.m = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)

#*****************************************************************
# Rank each component of total score first
#*****************************************************************
# target allocation
target.allocation[] = ntop(1.01 * bt.rank(mom.5m, F,T) + bt.rank(mom.20d, F,T) ,1)

# If asset is above it's 3 month moving average it gets allocation
weight = iif(go2cash.d, 0, target.allocation)

# otherwise, it's weight is allocated to cash
weight$CASH = 1 - rowSums(weight)

data$weight[] = NA
  data$weight[period.ends,] = weight
models$QTS.RANK.d = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)


# same but using monthly moving average to trigger go to cash
weight = iif(go2cash.m, 0, target.allocation)
weight$CASH = 1 - rowSums(weight)

data$weight[] = NA
  data$weight[period.ends,] = weight
models$QTS.RANK.m = bt.run.share(data, clean.signal=F, commission = commission, trade.summary=T, silent=T)

#*****************************************************************
# Report
#*****************************************************************
#strategy.performance.snapshoot(models, T)
plotbt(models, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
	mtext('Cumulative Performance', side = 2, line = 1)

print(plotbt.strategy.sidebyside(models, make.plot=F, return.table=T))

dates.range = c('2002-12-31::2014-08-15', '::2002-12-31', '2014-08-15::')

for(dates in dates.range) {
models1 = bt.trim(models, dates=dates)
plotbt(models1, plotX = T, log = 'y', LeftMargin = 3, main = NULL)
	mtext('Cumulative Performance', side = 2, line = 1)
print(plotbt.strategy.sidebyside(models1, make.plot=F, return.table=T))
}