A simple strategy on a single Lyxor ETF BB generated thanks to other data

Alphathon
#1

Hello!

could you please show a simple example of code that creates this kind of strategy and back-test it ?

for example a strategy on the benchmark with macro data

Thanks!

#2

Hi,

You can select macro series and compute indicators. In the example below, I have chosen the V2X (implied volatility on Eurostoxx 50) and EURUSD exchange rate. I first reduce the noise in the two series by using an exponentially weighted moving average, then I compute the 2-month (~40 working days) change in the series. My simple rule is that when volatility increase over 2-month and at the same time, the EURUSD exchange rate is depreciating over the same period, I cut positions (0), otherwise, I am long (1).

Please note that the series I use in my payout to generate the signals are independent from the building block on which I invest (in my algoEngine).


Please see the code below:

#create macro payout

macroPayout = function(volatilitySeries, exchangeRate, fxReturnPeriod=60, volChange=60, smoothing=10){

  # get rid of short term noise with exponential moving average and compute discrete rate of change

  # Volatility

  vol = ROC(EMA(volatilitySeries, smoothing), volChange, type=‘discrete’)

  # FX

  eurChg = ROC(EMA(exchangeRate, smoothing), fxReturnPeriod , type=‘discrete’)

  # Compute simple indicator: if vol increases over the period and FX depreciates, flat out of positions.

  indicator = na.omit(ifelse(volatilitySeries>0&eurChg<0.0, 0,1))

  return(indicator)

}


#Create algoEngine / strategy: BB is MEUDa but signals are generated from macro series

algoEngine(‘MEUDa’) %>%

  payout(macroPayout, 

      volatilitySeries = getTickersLyxorMacro(‘V2X’, asPrice=T),

      exchangeRate = getTickersLyxorMacro(‘EURUSD’, asPrice=T),

      fxReturnPeriod = 40,

      volChange=40,

      smoothing = 10) %>%

  evaluate() -> strat

#backtest

backtest(strat)