This post is in response to Michael Harris’ Price Action Lab post, where he uses some simple R code to evaluate the asymmetry of returns from the day’s close to the following day’s open.  I’d like to respond to his 3 notes, which I’ve included below.

1. The R backtest assumes fractional shares. This means that equity is fully invested at each new position. This is important because it affects drawdown calculations.
2. When calculating the Sharpe ratio, the “geometric = FALSE” option must be used otherwise the result may not be correct. It took some time to figure that out.
3. The profit factor result in R does not reconcile with results from other platforms or even from excel. PF in R is shown as 1.23 but the correct value is 1.17. Actually, the profit factor is calculated on a per share basis in R, although returns are geometric.

I completely agree with the first point.  I’m not sure Mike considers the output of  SharpeRatio.annualized with geometric=TRUE to be suspect (he doesn’t elaborate).  The overnightRets are calculated as arithmetic returns, so it’s proper to aggregate them using geometric chaining (i.e. multiplication).

I also agree with the third point, because the R code used to calculate profit factor is wrong.  My main impetus to write this post was to provide a corrected profit factor calculation.  The calculation (with slightly modified syntax) in Mike’s post is:

require(quantmod)
getSymbols(‘SPY’, from = ‘1900-01-01’)
SPY <- adjustOHLC(SPY, use.Adjusted=TRUE)
overnightRets <- na.omit(Op(SPY)/lag(Cl(SPY)) - 1)
posRet <- overnightRets > 0
profitFactor <- -sum(overnightRets[posRet])/sum(overnightRets[!posRet])
grossEquity <- cumprod(overnightRets+1)
grossPnL <- diff(grossEquity)
grossProfit <- sum(grossPnL[grossPnL > 0])
grossLoss <- sum(grossPnL[grossPnL < 0])
profitFactor <- grossProfit / abs(grossLoss)