
Hi everyone!

I am reading the BLCOP manual, about its 
implementation with fPortfolio package. I wonder if you might know if is possible in 
some way to use the backtest function in fPortfolio with BLCOP package?
My code is this (fPortfolio package):


MarkDatab = datatsp
MarkSpecb=portfolioSpec(
model = list(type = "MV", optimize = "minRisk",
estimator = "covEstimator", tailRisk = list(),
params = list(alpha = 0.05)),


portfolio = list(weights = NULL,
targetReturn = NULL, targetRisk = NULL,
riskFreeRate = 0, nFrontierPoints = 50,
status = 0),
optim = list(solver = "solveRquadprog", objective = NULL,
params = list(), control = list(), trace = FALSE))




MarkConstraintsb <- "LongOnly"
MarkBacktestb <- portfolioBacktest()

setSmootherDoubleSmoothing(MarkBacktestb) <- FALSE

setWindowsHorizon(MarkBacktestb) <- "36m"




setSmootherLambda(MarkBacktestb) <- "6m"

setWindowsHorizon(MarkBacktestb) <- "36m"

MarkFormulab <- ACCI ~ MSCI.EAFE+MSCI.USA




MarkPortfoliosb <- portfolioBacktesting(formula = MarkFormulab,
data = MarkDatab, spec = optPorts , constraints = MarkConstraintsb,


backtest = MarkBacktestb, trace = FALSE)

MarkSmoothPortfoliosb <- portfolioSmoothing(object = MarkPortfoliosb,
backtest = MarkBacktestb, trace = FALSE)

backtestPlot(MarkSmoothPortfoliosb, cex = 0.6, font = 1)




The BLCOP code is:

pickMatrix <- matrix(c(1/2, -1, 1/2, rep(0, 3)), nrow = 1, ncol = 6 )
views <- BLViews(P = pickMatrix, q = 0.06,confidences = 100,
assetNames = colnames(datatsp))
views




priorMeans <- rep(0, 6)
priorVarcov <- cov.mcd(datatsp)$cov

 marketPosterior <- posteriorEst(views = views, sigma = priorVarcov,
 mu = priorMeans, tau = 1/2)

optPorts <- optimalPortfolios.fPort(marketPosterior, optimizer = "minvariancePortfolio")





If it is possible to backtest Black-Litterman posterior in fPortfolio, how should I do it?


Thank you very much in advance, if you could lead me in the right direction!

Best Regards and thanks for any comments!

Paul Kushch 		 	   		  
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