library(ggplot2)
date <- Sys.Date()
root.dir <- Sys.getenv("BASE_DIR")
dists <- list()
for(deal in c('ares29', 'bally131')) {
    load(file.path(root.dir, "Scenarios", sprintf("Intex Curves_%s", date), "csv", paste(deal, "RData", sep=".")))
    dists[[deal]] <- dist.joint
}

support <- seq(0, 1, length = 201)
##5 year
ggplot() +
    geom_line(aes(x=support, y=rowSums(dists$ares29[17,,]), colour='Ares XXIX CLO')) +
    geom_line(aes(x=support, y=rowSums(dists$bally131[17,,]), colour='Ballyrock CLO 2013-1')) +
    ylab("loss density")+xlab("percentage of loss") +
    geom_vline(aes(colour='Market assumption', xintercept=0.03)) +
    annotate("text", x=0.5, y=0.025, label="T=5 year", size=4) +
    annotate("text", x=0.5, y=0, label="2 CDR, 70% recovery", colour="#F8766D", size=4) +
    labs(colour="") +
    theme(legend.position="bottom")

ggsave("Loss distribution.png", width=4.35, height=4)
##ggsave("Loss distribution.png")

tradedate <- as.Date("2016-10-19")
calibration.date <- as.Date("2016-10-18")
load(file.path(root.dir, "Scenarios", "Calibration", sprintf("marketdata-%s.RData", calibration.date)))
load(file.path(root.dir, "Scenarios", paste0("Prices_", tradedate), "cashflows.RData"))

getcusip.pv <- function(cusip, dealname, cusipdata, date) {
    indic.data <- getcusip_indicdata(cusip, dealname, date)
    cusip.pv <- cusipdata[[cusip]]$fields[,"Cashflow"]/indic.data$curr_balance
    return( cusip.pv )
}

index.pv <- function(indexdist, dealdata) {
    dealweight <- dealdata$weight
    dealprice <- dealdata$price
    nT <- dim(indexdist$L)[2]
    Ngrid <- dim(indexdist$L)[1]
    scenariosl <- matrix(0, length(dealweight), nT)
    scenariosr <- matrix(0, length(dealweight), nT)
    for(t in 1:nT){
        scenariosl[,t] <- interpvalues(indexdist$L[,t], seq(0, 1, length=Ngrid), dealweight)
        ## numerical artefact, but we want scenariosr[i,] + scenariosl[i,] <= 1 at all times
        scenariosr[,t] <- pmin(interpvalues(indexdist$R[,t], seq(0, 1, length=Ngrid), dealweight),
                               1-scenariosl[,t])
    }
    ## we assume the index is fully funded - need to be changed depending
    ## on how we fund the swaps (hence floating coupon instead of fixed)
    indexpv <- c()
    for(i in 1:length(dealweight)){
        indexpv <- c(indexpv, funded.tranche.pv(scenariosl[i,], scenariosr[i,], cs, 0, 1, TRUE))
    }
    return(indexpv)
}

getcusip_indicdata <- function(Cusip, dealname, date){
    sqlstr <- "SELECT DISTINCT isin FROM cusip_universe WHERE cusip=$1"
    r <- tryCatch(dbGetQuery(etdb, sqlstr, params=list(Cusip)),
                  error = function(w) logerror(w$message))
    if(length(r$isin)>1){
        stop("We have a problem")
    }
    isinval <- r$isin[1]
    sqlstr <- "SELECT * FROM historical_dealname_universe($1, $2)"
    r <- tryCatch(dbGetQuery(etdb, sqlstr, params = list(dealname, date)),
                  error = function(w) logerror(w$message))
    if(!is.na(isinval)){
        return(r %>% group_by(isin) %>% slice(1) %>%
               summarize(cusip, curr_balance, orig_balance, spread, curr_attach) %>%
               arrange(desc(curr_attach)) %>%
               mutate(cum_bal = cumsum(curr_balance)) %>% filter(isin==isinval) )
    }else{
        return(r %>% select(cusip, curr_balance, orig_balance, spread, curr_attach) %>%
               arrange(desc(curr_attach)) %>%
               mutate(cum_bal = cumsum(curr_balance)) %>% filter(cusip==Cusip) )
    }
}

tradedate <- as.Date("2016-10-19")
dealname <- "bally131"
hy.pv <- index.pv(dist, cfdata[[dealname]])
bally131 <- data.frame(cusips = c("05874PAA4", "05874PAC0", "05874PAE6", "05874PAG1", "05874QAC8", "05874QAA2"),
                       name = c("A", "B", "C", "D", "E", "SUB"),
                       rating = c("AAA", "AA", "A", "BBB", "BB", "NA"))

support <- cumsum(cfdata[[dealname]]$weight) * 100

df <- data.frame(x =support, pv = hy.pv * 100, name = "HY27 5Y")
for(i in 1:nrow(bally131)) {
    df <- rbind(df,
                data.frame(x =support,
                           pv = getcusip.pv(bally131$cusips[i], dealname, cusipdata, date)*100,
                           name = bally131$rating[i]))
}

ggplot(df) +
    geom_line(aes(x=x, y =pv, group = name, colour = factor(name))) +
    ylab("Expected price")+xlab("scenario percentile") +
    theme(legend.position="bottom")

for(cusip in cusips ) {
    cusip.pv <- getcusip.pv(cusip, dealname, cusipdata, date)
    plot <- plot + geom_line(aes(x=support, y =cusip.pv * 100,
                                 colour=sprintf('Ballyrock CLO 2013-1 %s', cusip)))
}