Simple Moving Average Strategy with a Volatility Filter

require(PerformanceAnalytics)
require(quantstrat)

sym.st = "GSPC"
currency("USD")
stock(sym.st, currency="USD",multiplier=1)
getSymbols("^GSPC", src='yahoo', index.class=c("POSIXt","POSIXct"), from='1990-01-01')
GSPC <- to.weekly(GSPC,indexAt='lastof',drop.time=TRUE)

#Custom Order Sizing Function to trade percent of equity based on a stopsize
osPCTEQ <- function(timestamp, orderqty, portfolio, symbol, ruletype, ...){
  tempPortfolio <- getPortfolio(portfolio.st)
  dummy <- updatePortf(Portfolio=portfolio.st, Dates=paste('::',as.Date(timestamp),sep=''))
  trading.pl <- sum(getPortfolio(portfolio.st)$summary$Realized.PL) #change to ..$summary$Net.Trading.PL for Total Equity Position Sizing
  assign(paste("portfolio.",portfolio.st,sep=""),tempPortfolio,pos=.blotter)
  total.equity <- initEq+trading.pl
  DollarRisk <- total.equity * trade.percent
  ClosePrice <- as.numeric(Cl(mktdata[timestamp,]))
  mavg <- as.numeric(mktdata$SMA52[timestamp,])
  sign1 <- ifelse(ClosePrice > mavg, 1, -1)
  sign1[is.na(sign1)] <- 1
  Posn = getPosQty(Portfolio = portfolio.st, Symbol = sym.st, Date = timestamp)
  StopSize <- as.numeric(mktdata$SDEV[timestamp,]*StopMult) #Stop = SDAVG * StopMult !Must have SDAVG or other indictor to determine stop size
  orderqty <- ifelse(Posn == 0, sign1*round(DollarRisk/StopSize), 0) # number contracts traded is equal to DollarRisk/StopSize
  return(orderqty)
}

#Function that calculates the n period standard deviation of close prices.
#This is used in place of ATR so that I can use only close prices.
SDEV <- function(x, n){
  sdev <- runSD(x, n, sample = FALSE)
  colnames(sdev) <- "SDEV"
  reclass(sdev,x)
}

#Custom indicator function 
RB <- function(x,n){
  x <- x
  roc <- ROC(x, n=1, type="discrete")
  sd <- runSD(roc,n, sample= FALSE)
  sd[is.na(sd)] <- 0
  med <- runMedian(sd,n)
  med[is.na(med)] <- 0
  mavg <- SMA(x,n)
  signal <- ifelse(sd < med & x > mavg,1,0)
  colnames(signal) <- "RB"
  #ret <- cbind(x,roc,sd,med,mavg,signal)
  #colnames(ret) <- c("close","roc","sd","med","mavg","lowvol")
  reclass(signal,x)
  }

initDate='1900-01-01'
initEq <- 100000

trade.percent <- .05 #percent risk used in sizing function
StopMult = 1 #stop size used in sizing function

#Name the portfolio and account
portfolio.st='RBtest'
account.st='RBtest'

#Initialization
initPortf(portfolio.st, symbols=sym.st, initPosQty=0, initDate=initDate, currency="USD")
initAcct(account.st,portfolios=portfolio.st, initDate=initDate, initEq=initEq)
initOrders(portfolio=portfolio.st,initDate=initDate)

#Name the strategy
stratRB <- strategy('RBtest')

#Add indicators
#The first indicator is the 52 period SMA
#The second indicator is the RB indicator. The RB indicator returns a value of 1 when close > SMA & volatility < runMedian(volatility, n = 52)
stratRB <- add.indicator(strategy = stratRB, name = "SMA", arguments = list(x = quote(Cl(mktdata)), n=52), label="SMA52")
stratRB <- add.indicator(strategy = stratRB, name = "RB", arguments = list(x = quote(Cl(mktdata)), n=52), label="RB")
stratRB <- add.indicator(strategy = stratRB, name = "SDEV", arguments = list(x = quote(Cl(mktdata)), n=52), label="SDEV")

#Add signals
#The buy signal is when the RB indicator crosses from 0 to 1
#The exit signal is when the close crosses below the SMA
stratRB <- add.signal(strategy = stratRB, name="sigThreshold", arguments = list(threshold=1, column="RB",relationship="gte", cross=TRUE),label="RB.gte.1")
stratRB <- add.signal(strategy = stratRB, name="sigCrossover", arguments = list(columns=c("Close","SMA52"),relationship="lt"),label="Cl.lt.SMA")

#Add rules
stratRB <- add.rule(strategy = stratRB, name='ruleSignal', arguments = list(sigcol="RB.gte.1", sigval=TRUE, orderqty=1000, ordertype='market', orderside='long', osFUN = 'osPCTEQ', pricemethod='market', replace=FALSE), type='enter', path.dep=TRUE)
stratRB <- add.rule(strategy = stratRB, name='ruleSignal', arguments = list(sigcol="Cl.lt.SMA", sigval=TRUE, orderqty='all', ordertype='market', orderside='long', pricemethod='market',TxnFees=0), type='exit', path.dep=TRUE)

# Process the indicators and generate trades
start_t<-Sys.time()
out<-try(applyStrategy(strategy=stratRB , portfolios=portfolio.st))
end_t<-Sys.time()
print("Strategy Loop:")
print(end_t-start_t)

start_t<-Sys.time()
updatePortf(Portfolio=portfolio.st,Dates=paste('::',as.Date(Sys.time()),sep=''))
end_t<-Sys.time()
print("updatePortf execution time:")
print(end_t-start_t)

chart.Posn(Portfolio=portfolio.st,Symbol=sym.st)

#Update Account
updateAcct(account.st)

#Update Ending Equity
updateEndEq(account.st)

#ending equity
getEndEq(account.st, Sys.Date()) + initEq

tstats <- tradeStats(Portfolio=portfolio.st, Symbol=sym.st)

#View order book to confirm trades
#getOrderBook(portfolio.st)

#Trade Statistics for CAGR, Max DD, and MAR
#calculate total equity curve performance Statistics
ec <- tail(cumsum(getPortfolio(portfolio.st)$summary$Net.Trading.PL),-1)
ec$initEq <- initEq
ec$totalEq <- ec$Net.Trading.PL + ec$initEq
ec$maxDD <- ec$totalEq/cummax(ec$totalEq)-1
ec$logret <- ROC(ec$totalEq, n=1, type="continuous")
ec$logret[is.na(ec$logret)] <- 0

Strat.Wealth.Index <- exp(cumsum(ec$logret)) #growth of $1

period.count <- NROW(ec)-104 #Use 104 because there is a 104 week lag for the 52 week SD and 52 week median of SD
year.count <- period.count/52
maxDD <- min(ec$maxDD)*100
totret <- as.numeric(last(ec$totalEq))/as.numeric(first(ec$totalEq))
CAGR <- (totret^(1/year.count)-1)*100
MAR <- CAGR/abs(maxDD)

Perf.Stats <- c(CAGR, maxDD, MAR)
names(Perf.Stats) <- c("CAGR", "maxDD", "MAR")
Perf.Stats
#write.zoo(mktdata, file = "E:\\a.csv")

charts.PerformanceSummary(ec$logret, wealth.index = TRUE, colorset = "steelblue2", main = "SMA with Volatility Filter System Performance")