A Basic Logical Invest Global Market Rotation Strategy

This may be one of the simplest strategies I’ve ever presented on this blog, but nevertheless, it works, for some definition of “works”.

Here’s the strategy: take five global market ETFs (MDY, ILF, FEZ, EEM, and EPP), along with a treasury ETF (TLT), and every month, fully invest in the security that had the best momentum. While I’ve tried various other tweaks, none have given the intended high return performance that the original variant has.

Here’s the link to the original strategy.

While I’m not quite certain of how to best go about programming the variable lookback period, this is the code for the three month lookback.

require(quantmod)
require(PerformanceAnalytics)

symbols <- c("MDY", "TLT", "EEM", "ILF", "EPP", "FEZ")
getSymbols(symbols, from="1990-01-01")
prices <- list()
for(i in 1:length(symbols)) {
  prices[[i]] <- Ad(get(symbols[i]))
}
prices <- do.call(cbind, prices)
colnames(prices) <- gsub("\.[A-z]*", "", colnames(prices))
returns <- Return.calculate(prices)
returns <- na.omit(returns)

logicInvestGMR <- function(returns, lookback = 3) {
  ep <- endpoints(returns, on = "months") 
  weights <- list()
  for(i in 2:(length(ep) - lookback)) {
    retSubset <- returns[ep[i]:ep[i+lookback],]
    cumRets <- Return.cumulative(retSubset)
    rankCum <- rank(cumRets)
    weight <- rep(0, ncol(retSubset))
    weight[which.max(cumRets)] <- 1
    weight <- xts(t(weight), order.by=index(last(retSubset)))
    weights[[i]] <- weight
  }
  weights <- do.call(rbind, weights)
  stratRets <- Return.portfolio(R = returns, weights = weights)
  return(stratRets)
}

gmr <- logicInvestGMR(returns)
charts.PerformanceSummary(gmr)

And here’s the performance:

> rbind(table.AnnualizedReturns(gmr), maxDrawdown(gmr), CalmarRatio(gmr))
                          portfolio.returns
Annualized Return                  0.287700
Annualized Std Dev                 0.220700
Annualized Sharpe (Rf=0%)          1.303500
Worst Drawdown                     0.222537
Calmar Ratio                       1.292991

With the resultant equity curve:

While I don’t get the 34% advertised, nevertheless, the risk to reward ratio over the duration of the backtest is fairly solid for something so simple, and I just wanted to put this out there.

Thanks for reading.