Strategy Diversification in R – follow up

June 25, 2012

(This article was first published on rbresearch » R, and kindly contributed to R-bloggers)

The strategies used in Strategy Diversification in R were labeled as Strategy1 and Strategy2.


  • Indicator: 52 week Simple Moving Average
  • Entry Rule: Buy 1000 shares when price crosses and closes above 52 week Simple Moving Average
  • Exit Rule: Exit all positions when prices crosses and closes below 52 week Simple Moving Average
  • Classification: Long term trend following strategy

Strategy 2

  • Indicator: RSI(2) on weekly data
  • Entry Rule: Buy 100 shares when RSI(2) is less than 20 (Note that if RSI(2) is below 20 for N days, then you will have accumulated N * 100 shares)
  • Exit Rule: Exit all positions when RSI(2) is greater than 50
  • Classification: Short-Medium term reversal (dip buying) strategy

What did we diversify?

  1. Symbols? – No, the exact same instruments were used in the strategy.
  2. Markets? – No, see #1.
  3. Timeframe? Sort of, Strategy1 is a long term strategy and Strategy2 is a shorter term strategy, but both are on the weekly timeframe. We could diversify further by trading even shorter timeframes (i.e. Daily, Hourly, minute, tick, etc.)
  4. Strategy? Yes, Strategy1 is a trend following strategy and Strategy2 is a reversal strategy.
  5. Risk Levels? Yes, Strategy2 trades more often, but in smaller increments.

We achieved fairly low correlations by achieving only three “levels” of diversification. Think what we could do by using a “kitchen sink” portfolio with grains, softs, metals, currencies, stocks, fixed income, international stocks, international fixed income, style ETFs, etc.

Three R script files were used in the last post.

strategy1.R, strategy2.R, and correlation chart.R

The R scripts are pretty self explanatory so I won’t go into much detail. However, I do want to call attention to 2 lines of code from strategy1.R. The code for strategy2.R is virtually identical.

# logarithmic returns of the equity curve of strategy1.
strategy1_eclogret <- ec$logret

# write the logarithmic returns of strategy 1 to a csv file with the filename "strategy1.csv"
# you will have to change the file where you want to save it
write.zoo(strategy1_eclogret, file = "~/R/strats_for_cor/strategy1.csv", sep=",")

Here is the code to make the correlation chart.

#Load the packages used

# load the strategy 1 returns
strat1 <- as.xts(read.zoo(file = "~/R/strats_for_cor/strategy1.csv", header = TRUE, sep=","))
colnames(strat1) <- "strat1"

# load the strategy 2 returns
strat2 <- as.xts(read.zoo(file = "~/R/strats_for_cor/strategy2.csv", header = TRUE, sep=","))
colnames(strat2) <- "strat2"

suppressWarnings(chart.RollingCorrelation(strat1, strat2, width = 52, xaxis = TRUE, 
                                          colorset = rich8equal, legend.loc = "bottomright",
                                          main = "Rolling 52 Week Correlation"))

And that is all there is to it. (run strategy1.R, run strategy2.R, then run correlation chart.R – don’t forget to change the file directory!)

I listed 5 “levels” or ways to achieve diversification… what are other ways we can diversify? – post your ideas in the comments

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