args <- commandArgs(trailingOnly=TRUE)

if(.Platform$OS.type == "unix"){
  root.dir <- "/home/share/CorpCDOs"
}else{
  root.dir <- "//WDSENTINEL/share/CorpCDOs"
}

if(length(args) >= 1){
  workdate <- as.Date(args[1])
}else{
  workdate <- Sys.Date()
}

if(length(args) >=2){
  dealnames <- args[2:length(args)]
}else{
  dealnames <- read.table(file.path(root.dir, "scripts", "scenarios.txt"))$V1
  unlink(file.path(root.dir, "scripts", "scenarios.txt"))
}

source(file.path(root.dir, "code", "R", "intex_deal_functions.R"))
source(file.path(root.dir, "code", "R", "index_definitions.R"))

prevBusDay <- function(workdate = Sys.Date()){
    i <- 1
    while(!isBusinessDay(calendar = "UnitedStates/GovernmentBond",
                         workdate - i)){
        i <- i+1
    }
    return( workdate - i )
}

compute.reinvprices <- function(DC, cdrmonthly, recoverymonthly, spread, fixedrate, rollingmat){
    floatbp <- c()
    fixedbp <- c()
    for(i in 0:(ncol(cdrmonthly)-1)){
        index <- (i+1):min(i+rollingmat, ncol(cdrmonthly))
        if(i==0){
            df <- DC$discounts[index]

        }else{
            df <- DC$discounts[index]/DC$discounts[i]
        }
        floatcoupon <- (DC$forwards[index]+ spread)/12
        fixedcoupon <- fixedrate/12
        currbalance <- 1 - cdrmonthly[,index]/100/12
        browser()
        if(i < ncol(cdrmonthly)-1){
            currbalance <- t(apply(currbalance, 1, cumprod))
            recov <- -t(apply(cbind(1, currbalance), 1, diff)) *
                recoverymonthly[,index]
            fixedcl <- cbind(1,currbalance[,-length(index)])%*%(fixedcoupon*df)
            floatcl <- cbind(1,currbalance[,-length(index)])%*%(floatcoupon*df)
            pl <- currbalance[,dim(currbalance)[2]]*df[length(df)] +
                recov %*% df
        }else{
            fixedcl <- currbalance*fixedcoupon*df
            floatcl <- currbalance*floatcoupon*df
            pl <- currbalance*df
        }
        floatbp <- cbind(floatbp, pl+floatcl)
        fixedbp <- cbind(fixedbp, pl+fixedcl)
    }
    return( list(loan=floatbp, bond=fixedbp) )
}

calibration.date <- prevBusDay(workdate)
calibration <- read.table(file.path(root.dir, "Scenarios", "Calibration",
                                    paste0("calibration-", calibration.date,".csv")), sep=",", header=T)
Z <- calibration$Z
w <- calibration$w

rho <- 0.45
Ngrid <- 201
## dealnames <- list.files(file.path(root.dir, "Scenarios", paste0("Portfolios_", calibration.date)),
##                         pattern="*.RData")
## dealnames <- sapply(strsplit(dealnames, "\\."), function(x)x[1])
MarkitData <- getMarkitIRData(calibration.date)
L1m <- buildMarkitYC(MarkitData, dt = 1/12)
L2m <- buildMarkitYC(MarkitData, dt = 1/6)
L3m <-  buildMarkitYC(MarkitData)
L6m <- buildMarkitYC(MarkitData, dt = 1/2)
L12m <- buildMarkitYC(MarkitData, dt = 1)
setEvaluationDate(as.Date(MarkitData$effectiveasof))

support <- seq(0, 1, length = Ngrid)
for(deal.name in dealnames){
    load(file.path(root.dir, "Scenarios", paste("Portfolios", workdate, sep="_"),
                   paste(deal.name, "RData", sep=".")))

    dp <- A$DP
    pp <- A$PP
    dpmod <- MFupdate.probC(Z, w, rho, dp)
    ppmod <- MFupdate.probC(-Z, w, rho, pp)
    dist.joint <- MFlossdist.prepay.joint2(w, Z, rho, dp, dpmod, pp, ppmod,
                                           deal.weights, 1-S, Ngrid)

    distDR <- dist.transform(dist.joint)
    ## compute E(R|D)
    R <- matrix(0, Ngrid, dim(distDR)[1])
    for(t in 1:dim(distDR)[1]){
        R[,t] <- sweep(distDR[t,,], 1, rowSums(distDR[t,,]), "/") %*% support
        R[1,t] <- 0
        if(t >= 2){
            R[,t] <- pmax(R[,t], R[,t-1])
        }
    }

    n.scenarios <- 100
    ## compute scenariosd
    scenariosd <- matrix(0, n.scenarios, dim(distDR)[1])
    scenariosr <- matrix(0, n.scenarios, dim(distDR)[1])
    percentiles <- seq(0, 1, 0.01)
    for(t in 1:dim(distDR)[1]){
        D <- rowSums(distDR[t,,])
        Dfun <- splinefun(c(0, cumsum(D)), c(0, support), "monoH.FC")
        ## dvallow <- floor(Dfun(percentiles)*(Ngrid-1))
        ## dvalup <- ceil(Dfun(percentiles)*(Ngrid-1))
        Rfun <- approxfun(support, R[,t], rule=2)
        for(i in 1:n.scenarios){
            scenariosd[i,t] <- 0.5 * (Dfun((i-1)*0.01)+Dfun(i*0.01))
            if(t>=2 && scenariosd[i,t] < scenariosd[i,t-1]){
                scenariosd[i,t] <- scenariosd[i,t-1]
            }
            scenariosr[i,t] <- Rfun(scenariosd[i,t])
            if(t>=2 && scenariosr[i,t] < scenariosr[i,t-1]){
                scenariosr[i,t] <- scenariosr[i,t-1]
            }
        }
    }
    intexrecov <- matrix(0, n.scenarios, dim(distDR)[1])
    for(i in 1:dim(distDR)[1]){
        if(i==1){
            intexrecov[,i] <- (scenariosr[,i]/scenariosd[,1])
        }else{
            intexrecov[,i] <- (scenariosr[,i]-scenariosr[,i-1])/(scenariosd[,i]-scenariosd[,i-1])
        }
    }

    cdr <- cdrfromscenarios(scenariosd, deal.dates)
    intexrecov <- recoveryfromscenarios(scenariosd, scenariosr)
    ## linear approximation for monthly scenarios
    deal.data <- getdealdata(deal.name)
    deal.datesmonthly <- getdealschedule(deal.data, "1 month")
    cdrmonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
    recoverymonthly <- matrix(0, n.scenarios, length(deal.datesmonthly))
    for(i in 1:n.scenarios){
        cdrmonthly[i,] <- approx(deal.dates, cdr[i,], deal.datesmonthly, rule=2)$y
        recoverymonthly[i,] <- approx(deal.dates, intexrecov[i,], deal.datesmonthly, rule=2)$y
    }

    ## compute reinvestment price
    DC <- DiscountCurve(L3m$params, L3m$tsQuotes, yearFrac(L3m$params$tradeDate, deal.datesmonthly))
    reinvprices <- compute.reinvprices(DC, cdrmonthly, recoverymonthly, 0.025, 0.07, 84)

    save.dir <- file.path(root.dir, "Scenarios", paste("Intex curves", workdate, sep="_"), "csv")
    if(!file.exists(save.dir)){
        dir.create(save.dir, recursive = T)
    }
    recoverymonthly <- pmin(recoverymonthly,1)
    write.table(cdrmonthly,
                file= file.path(save.dir, paste0(deal.name,"-cdr.csv")),
                row.names=F, col.names=F, sep=",")
    write.table(100 * recoverymonthly,
                file=file.path(save.dir, paste0(deal.name,"-recovery.csv")),
                row.names=F, col.names=F, sep=",")
    write.table(100 * reinvprices$loan,
                file = file.path(save.dir, paste0(deal.name, "-floatreinvprices.csv")),
                row.names=F, col.names=F, sep=",")
    write.table(100 * reinvprices$bond,
                file = file.path(save.dir, paste0(deal.name, "-fixedreinvprices.csv")),
                row.names=F, col.names=F, sep=",")
    save(scenariosd, scenariosr, distjoint, file=file.path(save.dir, paste0(deal.name, ".RData")))
    cat("generated scenarios for:", deal.name, "\n")
}