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LOOKING_AT3_RB0.Rmd
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---
title: "RB RELATED"
#author: "CHENRUJIE"
#date: "2022年7月13日"
output:
flexdashboard::flex_dashboard:
orientation: rows
vertical_layout: scroll
theme: spacelab
---
```{r setup, include=FALSE}
#Rmarkdown Global Setup--------------------------------------------------
library(knitr)
library(readr)
library(readxl)
library(mFilter)
library(zoo)
library(xts)
library(ggplot2)
library(ggpubr)
library(timeDate)
library(timeSeries)
library(ghyp)
library(fBasics)
library(fExtremes)
library(lubridate)
knitr::opts_chunk$set(echo = FALSE,
warning = FALSE,
fig.width = 14,
fig.height = 10)
#Global Enviornment Setup---------------------------------------------------
library(knitr)
knitr::opts_chunk$set(fig.width = 14,
fig.height = 6)
H <- new.env() #Enviornment to store variabels.
H$ref_TD.hour <- c("RB0_60", "HC0_60", "I0_60", "V0_60", "BU0_60", "JM0_60", "UR0_60", "FG0_60", "MA0_60", "SA0_60", "SR0_60")
H$ref_TD.hour.text <- c("SHFE REBAR Major 60 min", "SHFE HOTCOIL Major 60 min", "DCE IRONORE Major 60 min", "DCE PVC Major 60 min", "SHFE BITUMAN Major 60", "DCE J M Major 60", "CZCE UREA Major 60", "SHFE LASTIC Major 60 min", "CZCE FIBER GLASS Major 60 min", "CZCE Mathano Major 60 min", "CZCE Sodium Acid Major 60 min", "CZCE SUGAR Major 60 min")
H$ref_TD.hour.bk <- H$ref_TD.hour
H$ref_TD <- c("RB0", "HC0", "I0", "V0", "BU0", "JM0", "UR0", "FG0", "MA0", "SA0", "SR0")
H$ref_TD.text <- c("SHFE REBAR Futures", "SHFE HOTCOIL Futures", "DCE IRONORE Futures", "DCE PVC Futures", "SHFE BITUMAN Futures", "DCE J M Futures", "CZCE UREA Futures", "CZCE FIBER GLASS Futures", "CZCE Mathano Futures", "CZCE Sodium Acid Futures", "CZCE SUGAR Futures")
H$wd_ind <- paste(format(Sys.Date() - months(1), "%Y-%m"), format(Sys.Date(), "%Y-%m"), sep = "/")
H$globalWD <- file.path("~/PRO80")
H$globalWD.DATA <- file.path(H$globalWD, "DATA")
```
TRADING SCORES
==================================================
```{r General Preparation Process}
#Functions.--------------------------------------
#Function to read from excel file and process with hodrick prescott filter.
H$read_xls_hpf <- function(contract_name){
#Read Excel File Data.
TD.read <- read_excel(file.path(H$globalWD.DATA, paste0(contract_name, ".xls")),
col_types = c("text", "text", "date",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric",
"numeric", "numeric", "numeric"),
col_names = TRUE
)
colnames(TD.read) <- c(paste0(contract_name, "Code"),
paste0(contract_name, "Contract"),
paste0(contract_name, "Date"),
paste0(contract_name, "Open"),
paste0(contract_name, "High"),
paste0(contract_name, "Low"),
paste0(contract_name, "Close"),
paste0(contract_name, "Chg"),
paste0(contract_name, "RateofChg"),
paste0(contract_name, "Vol"),
paste0(contract_name, "Amn"),
paste0(contract_name, "Settle"))
TD.read <- TD.read[, -12]
TD.read.na <- na.omit(TD.read)
#Return Dataframe without NA.
close.HPF <- hpfilter(TD.read.na[[paste0(contract_name, "Close")]], freq=2419200, type=c("lambda"), drift = FALSE)
return.output <- as.data.frame(TD.read.na[, 3])
return.output <- cbind(return.output,
TD.read.na[, 7],
close.HPF$trend,
close.HPF$cycle)
colnames(return.output) <- c(paste0(contract_name, "Date"),
paste0(contract_name, "Close"),
paste0(contract_name, "_hpft"),
paste0(contract_name, "_hpfc"))
return(return.output)
}
#Functions to Reading Daily Trade Data
H$read_daily <- function(ref_names){
return.list <- list(NULL)
for (i in 1:length(ref_names)){
TD.read <- read.csv(paste0(H$globalWD, "/DailyTDs/", ref_names[i], "DailyTD.csv"))
TD.read.xts <- xts(TD.read[, -1], order.by = as.Date(TD.read[, 1], format = "%Y-%m-%d"))
return.list[i] <- list(TD.read.xts)
names(return.list)[i] <- c(paste0(ref_names[i], "_hpf.xts"))
}
return(return.list)
}
#Processing.-------------------------------------
#Read Hourly Trade Data.
H$HourlyTD.List <- list(NULL)
for(i in 1:length(H$ref_TD.hour)){
H$HourlyTD.List[i] <- list(H$read_xls_hpf(H$ref_TD.hour[i]))
names(H$HourlyTD.List)[i] <- paste0(H$ref_TD.hour[i], "_hpf")
}
#For XTS output.
H$HourlyTD.xts.List <- list(NULL)
H$HourlyTD.xts.List.wd <- list(NULL)
for(i in 1:length(H$ref_TD.hour)){
H$HourlyTD.xts.List[i] <- list(xts(H$HourlyTD.List[[paste0(H$ref_TD.hour[i], "_hpf")]][, -1],
order.by = H$HourlyTD.List[[paste0(H$ref_TD.hour[i], "_hpf")]][, 1]))
names(H$HourlyTD.xts.List)[i] <- paste0(H$ref_TD.hour[i], "_hpf.xts")
##Windowing the Hourly Trade Data
H$xh <- xts(H$HourlyTD.List[[paste0(H$ref_TD.hour[i], "_hpf")]][, -1],
order.by = H$HourlyTD.List[[paste0(H$ref_TD.hour[i], "_hpf")]][, 1])
H$HourlyTD.xts.List.wd[i] <- list(H$xh[H$wd_ind])
names(H$HourlyTD.xts.List.wd)[i] <- paste0(H$ref_TD.hour[i], "_wd_hpf.xts")
}
#Read Daily Trade Data
H$DailyTD.xts.List <- list(NULL)
H$DailyTD.xts.List <- H$read_daily(H$ref_TD)
#SAVE for Hourly TD_All.
H$HourlyTD.xts.List.bk <- H$HourlyTD.xts.List
```
BUILD AT: `r Sys.time()`
Abbreviation:
GHD --> Generalized Hyperbolic Distribution
GPD --> Generalized Pareto Distribution
```{r Generate the FLAGs}
# Generate the flag according to results of hpfilter.===========================
library(formattable)
flagPlot <- function(dataset.df){
sign_formatter <- formatter("span", style = x ~ ifelse(x > 0, style("background-color" = "black", color = "white"),
style("background-color" = "white", color = "black")))
sign_buildin <- color_bar("lightblue")
sign_result <- color_tile("transparent", "orange")
formattable(dataset.df, list(
"RB0_60" = sign_buildin,
"HC0_60" = sign_buildin,
"I0_60" = sign_buildin,
"V0_60" = sign_buildin,
"BU0_60" = sign_buildin,
"PP0_60" = sign_buildin,
"L0_60" = sign_buildin,
"TA0_60" = sign_buildin,
"MA0_60" = sign_buildin,
"SA0_60" = sign_buildin,
"SR0_60" = sign_buildin,
"TOTAL_COUNT" = sign_result
))
}
flagPlotarea <- function(dataset.df){
above_formatter = color_bar("lightpink")
below_formatter = color_bar("lightgreen")
result_formatter = color_tile("transparent", "lightblue")
formattable(dataset.df, list(
area(row = 1, col = 1:11) ~ above_formatter,
area(row = 2, col = 1:11) ~ below_formatter,
area(col = 12) ~ result_formatter
))
}
# Creating the dataframe to contain ABOVEorBELOW FLAG.--------------------------
ABOVEorBELOW.df <- data.frame(matrix(nrow = 2, ncol = length(H$ref_TD)+1))
colnames(ABOVEorBELOW.df) <- c(H$ref_TD.hour, "TOTAL_COUNT")
rownames(ABOVEorBELOW.df) <- c("ABOVE_TREND", "BELOW_TREND")
ABOVEorBELOW.df[1, dim(ABOVEorBELOW.df)[2]] <- 0
ABOVEorBELOW.df[2, dim(ABOVEorBELOW.df)[2]] <- 0
# Counting the results.
for(i in 1:length(H$ref_TD.hour)){
ind = H$ref_TD.hour[i]
singleTD <- H$HourlyTD.xts.List[[i]]
singleLast <- last(singleTD)
singleX <- singleLast[dim(singleLast)[1], dim(singleLast)[2]]
if(singleX >= 0){
ABOVEorBELOW.df[1, ind] <- 1
ABOVEorBELOW.df[2, ind] <- 0
ABOVEorBELOW.df[1, dim(ABOVEorBELOW.df)[2]] <- ABOVEorBELOW.df[1, dim(ABOVEorBELOW.df)[2]] + 1
}else if(singleX < 0){
ABOVEorBELOW.df[1, ind] <- 0
ABOVEorBELOW.df[2, ind] <- 1
ABOVEorBELOW.df[2, dim(ABOVEorBELOW.df)[2]] <- ABOVEorBELOW.df[2, dim(ABOVEorBELOW.df)[2]] + 1
}
}
# Creating the dataframe to contain UPorDOWN FLAG.------------------------------
UPorDOWN.df <- data.frame(matrix(nrow = 2, ncol = length(H$ref_TD)+1))
colnames(UPorDOWN.df) <- c(H$ref_TD.hour, "TOTAL_COUNT")
rownames(UPorDOWN.df) <- c("TREND_UP", "TREND_DOWN")
UPorDOWN.df[1, dim(UPorDOWN.df)[2]] <- 0
UPorDOWN.df[2, dim(UPorDOWN.df)[2]] <- 0
# Counting the results.
for(i in 1:length(H$ref_TD.hour)){
ind = H$ref_TD.hour[i]
singleTD <- H$HourlyTD.xts.List[[i]]
singleLast_1 <- singleTD[dim(singleTD)[1]-1, ]
singleLast <- last(singleTD)
singleX <- as.numeric(singleLast[1, 2]) - as.numeric(singleLast_1[1, 2])
if(singleX >= 0){
UPorDOWN.df[1, ind] <- 1
UPorDOWN.df[2, ind] <- 0
UPorDOWN.df[1, dim(UPorDOWN.df)[2]] <- UPorDOWN.df[1, dim(UPorDOWN.df)[2]] + 1
}else if(singleX < 0){
UPorDOWN.df[1, ind] <- 0
UPorDOWN.df[2, ind] <- 1
UPorDOWN.df[2, dim(UPorDOWN.df)[2]] <- UPorDOWN.df[2, dim(UPorDOWN.df)[2]] + 1
}
}
# Creating the dataframe to contain STRONGorWEAK FLAG.--------------------------
STRONGorWEAK.df <- data.frame(matrix(nrow = 2, ncol = length(H$ref_TD)+1))
colnames(STRONGorWEAK.df) <- c(H$ref_TD.hour, "TOTAL_COUNT")
rownames(STRONGorWEAK.df) <- c("STRONG", "WEAK")
STRONGorWEAK.df[1, dim(STRONGorWEAK.df)[2]] <- 0
STRONGorWEAK.df[2, dim(STRONGorWEAK.df)[2]] <- 0
# Counting the results.
for(i in 1:length(H$ref_TD.hour)){
ind = H$ref_TD.hour[i]
singleTD <- H$HourlyTD.xts.List[[i]]
singleLast <- last(singleTD)
singleDTD <- H$DailyTD.xts.List[[i]]
singleDLast <- last(singleDTD)
singleX <- as.numeric(singleLast[1, 2]) - as.numeric(singleDLast[1, 2])
if(singleX >= 0){
STRONGorWEAK.df[1, ind] <- 1
STRONGorWEAK.df[2, ind] <- 0
STRONGorWEAK.df[1, dim(STRONGorWEAK.df)[2]] <- STRONGorWEAK.df[1, dim(STRONGorWEAK.df)[2]] + 1
}else if(singleX < 0){
STRONGorWEAK.df[1, ind] <- 0
STRONGorWEAK.df[2, ind] <- 1
STRONGorWEAK.df[2, dim(STRONGorWEAK.df)[2]] <- STRONGorWEAK.df[2, dim(STRONGorWEAK.df)[2]] + 1
}
}
# Creating the dataframe to contain TRADE FLAG.---------------------------------
FLAG.df <- data.frame(matrix(nrow = 2, ncol = length(H$ref_TD)+1))
colnames(FLAG.df) <- c(H$ref_TD.hour, "TOTAL_COUNT")
rownames(FLAG.df) <- c("LONG_FLAG", "SHORT_FLAG")
FLAG.df[1, dim(FLAG.df)[2]] <- 0
FLAG.df[2, dim(FLAG.df)[2]] <- 0
# Counting the results.
for(i in 1:length(H$ref_TD.hour)){
ind = H$ref_TD.hour[i]
singleX_long <- ABOVEorBELOW.df[1, i] + UPorDOWN.df[1, i] + STRONGorWEAK.df[1, i]
singleX_short <- ABOVEorBELOW.df[2, i] + UPorDOWN.df[2, i] + STRONGorWEAK.df[2, i]
if(singleX_long >= 2){
FLAG.df[1, ind] <- 1
FLAG.df[2, ind] <- 0
FLAG.df[1, dim(FLAG.df)[2]] <- FLAG.df[1, dim(FLAG.df)[2]] + 1
}else if(singleX_long < 2){
FLAG.df[1, ind] <- 0
FLAG.df[2, ind] <- 1
FLAG.df[2, dim(FLAG.df)[2]] <- FLAG.df[2, dim(FLAG.df)[2]] + 1
}
}
```
TRADING SCORES {.tabset .tabset-fade}
-------------------------------------
### ABOVE_or_BELOW
```{r}
flagPlotarea(ABOVEorBELOW.df)
```
### UP_or_DOWN
```{r}
flagPlotarea(UPorDOWN.df)
```
### STRONG_or_WEAK
```{r}
flagPlotarea(STRONGorWEAK.df)
```
### SHOW_FLAG
```{r}
flagPlotarea(FLAG.df)
```
REBAR
==================================================
STATUS and DIST {.tabset .tabset-fade}
-------------------------------------
### STATUS
```{r, include=FALSE}
H$ind <- 1
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, -3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, 3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### 30 DAYS
```{r, include = FALSE}
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, -3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, 3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### HvD STATUS
```{r}
#Changed into xts file.
H$DailyTD.RB0.xts <- H[["DailyTD.xts.List"]][["RB0_hpf.xts"]]
H$HourlyTD.xts.List$RB0_60_hpf.2.xts <- align.time(H$HourlyTD.xts.List$RB0_60_hpf.xts,
n = 9 * 60*60)
#Align daily trade data with hourly trade data.
H$MixFreTD.RB0.xts <- merge.xts(H$HourlyTD.xts.List$RB0_60_hpf.2.xts,
H$DailyTD.RB0.xts,
join = "left")
index(H$MixFreTD.RB0.xts) <- index(H$HourlyTD.xts.List$RB0_60_hpf.xts)
#Try Using ggplot2 to show up the result.
H$x_label <- seq.POSIXt(from = index(H$MixFreTD.RB0.xts)[1],
to = index(H$MixFreTD.RB0.xts)[dim(H$MixFreTD.RB0.xts)[1]], by = "week")
H$MixFreTD.RB0.gg <- ggplot(H$MixFreTD.RB0.xts, aes(x = as.factor(index(H$MixFreTD.RB0.xts)), group = 1)) +
geom_line(aes(y = RB0_60Close)) +
geom_line(aes(y = RB0_60_hpft), colour = "red") +
geom_point(aes(y = RB0_hpft), colour = "blue", na.rm = TRUE) +
scale_x_discrete(breaks = c(as.character(H$x_label)), labels = format(x = H$x_label, format = "%m%d")) +
ggtitle("RB0 Hourly and Daily Trend.") + xlab("Trading Date")
H$MixFreTD.RB0.gg
```
### H_GHD
```{r, include=FALSE}
#Functions.======================================
#Functions to obtain timeSeries dataset.
H$DataSet_Fetch <- function(ref, suffix){
attach(c(H$HourlyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$HourlyTD.xts.List))
return(Return.get.ts)
}
#Preparing Dataset.==============================
H$DIST.Dataset.H <- H$DataSet_Fetch(H$ref_TD.hour[H$ind], "_hpf.xts")
H$DIST.Dataset.H.cycle <- na.omit(H$DIST.Dataset.H[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.H.ycycle <- H$DIST.Dataset.H.cycle[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))]
H$DIST.H.ef <- density(H$DIST.H.ycycle)
#Density
H$DIST.H.ghdfit <- fit.ghypuv(H$DIST.H.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.H.ghddense <- dghyp(H$DIST.H.ef$x, H$DIST.H.ghdfit)
H$DIST.H.col.def <- c("black", "blue")
#VaR and ES
H$DIST.H.p <- seq(0.001, 0.05, 0.001)
H$DIST.H.VaR <- abs(qghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.emp.VaR <- abs(quantile(x = H$DIST.H.ycycle, probs = H$DIST.H.p))
H$DIST.H.ES <- abs(ESghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.obs.p <- ceiling(H$DIST.H.p * length(H$DIST.H.ycycle))
H$DIST.H.emp.ES <- sapply(H$DIST.H.obs.p, function(x) abs(mean(sort(c(H$DIST.H.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.H.ef, xlab = paste0(H$ref_TD.hour[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.H.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD.hour[H$ind], " Cycle"))
lines(H$DIST.H.ef$x, H$DIST.H.ghddense, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#VaR
plot(H$DIST.H.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.H.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.VaR)))
lines(seq(along = H$DIST.H.p), H$DIST.H.VaR, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#ES
plot(H$DIST.H.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.H.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.ES)))
lines(1:length(H$DIST.H.p), H$DIST.H.ES, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
par(mfcol = c(1,1))
```
### D_GHD
```{r, include=FALSE}
##Functions.=====================================
H$DataSet_Fetch_D <- function(ref, suffix){
attach(c(H$DailyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$DailyTD.xts.List))
return(Return.get.ts)
}
#Fetching Dataset.
H$DIST.Dataset.D <- H$DataSet_Fetch_D(H$ref_TD[H$ind], "_hpf.xts")
H$DIST.Dataset.D.cycle <- na.omit(H$DIST.Dataset.D[, c(paste0(H$ref_TD[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.D.ycycle <- H$DIST.Dataset.D.cycle[, c(paste0(H$ref_TD[H$ind], "_hpfc"))]
H$DIST.D.ef <- density(H$DIST.D.ycycle)
#Density
H$DIST.D.ghdfit <- fit.ghypuv(H$DIST.D.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.D.ghddense <- dghyp(H$DIST.D.ef$x, H$DIST.D.ghdfit)
H$DIST.D.col.def <- c("black", "red")
#VaR and ES
H$DIST.D.p <- seq(0.001, 0.05, 0.001)
H$DIST.D.VaR <- abs(qghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.emp.VaR <- abs(quantile(x = H$DIST.D.ycycle, probs = H$DIST.D.p))
H$DIST.D.ES <- abs(ESghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.obs.p <- ceiling(H$DIST.D.p * length(H$DIST.D.ycycle))
H$DIST.D.emp.ES <- sapply(H$DIST.D.obs.p, function(x) abs(mean(sort(c(H$DIST.D.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.D.ef, xlab = paste0(H$ref_TD[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.D.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD[H$ind], " Cycle"))
lines(H$DIST.D.ef$x, H$DIST.D.ghddense, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#VaR
plot(H$DIST.D.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.D.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.VaR)))
lines(seq(along = H$DIST.D.p), H$DIST.D.VaR, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#ES
plot(H$DIST.D.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.D.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.ES)))
lines(1:length(H$DIST.D.p), H$DIST.D.ES, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
par(mfcol = c(1,1))
```
### GPD_VaRnES
```{r, paged.print=TRUE}
H$GPD.H.cycle.tg1 <- H$DIST.H.ycycle
H$GPD.D.cycle.tg1 <- H$DIST.D.ycycle
##Defining probability parameters.
H$prob.ind <- c(0.25, 0.50, 0.75, 0.85, 0.95, 0.99)
##GPD
H$GPD.H.fit.tg1 <- gpdFit(H$GPD.H.cycle.tg1, u = 100)
H$GPD.H.VaRES.tg1 <- round(gpdRiskMeasures(H$GPD.H.fit.tg1, prob = H$prob.ind),
digits = 3)
H$GPD.D.fit.tg1 <- gpdFit(H$GPD.D.cycle.tg1, u = 300)
H$GPD.D.VaRES.tg1 <- round(gpdRiskMeasures(H$GPD.D.fit.tg1, prob = H$prob.ind),
digits = 3)
##Output Table
H$GPD.H.VaRES.tg1.gg <- ggtexttable(H$GPD.H.VaRES.tg1,
cols = c("Prob", "VaR", "ES"),
theme = ttheme(colnames.style = colnames_style(color = "pink",
fill = "yellow",
size = 20),
tbody.style = tbody_style(size = 20))
)
H$GPD.D.VaRES.tg1.gg <- ggtexttable(H$GPD.D.VaRES.tg1,
cols = c("Prob", "VaR", "ES"),
theme = ttheme(colnames.style = colnames_style(color = "white",
fill = "orange",
size = 20),
tbody.style = tbody_style(size = 20))
)
H$GPD.H.VaRES.tg1.gg.output <- ggarrange(H$GPD.H.VaRES.tg1.gg,
H$GPD.D.VaRES.tg1.gg,
ncol = 2, nrow = 1)
annotate_figure(H$GPD.H.VaRES.tg1.gg.output, top = text_grob(
paste0("VaR and ES under GPD of ", H$ref_TD.hour[H$ind], " and ", H$ref_TD[H$ind], "."),
size = 20, vjust = 5
))
```
HOTCOIL
==================================================
STATUS and DIST {.tabset .tabset-fade}
-------------------------------------
### STATUS
```{r, include=FALSE}
H$ind <- 2
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, -3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, 3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### 30 DAYS
```{r, include = FALSE}
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, -3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, 3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### HvD STATUS
```{r}
#Changed into xts file.
H$DailyTD.HC0.xts <- H[["DailyTD.xts.List"]][["HC0_hpf.xts"]]
H$HourlyTD.xts.List$HC0_60_hpf.2.xts <- align.time(H$HourlyTD.xts.List$HC0_60_hpf.xts,
n = 9 * 60*60)
#Align daily trade data with hourly trade data.
H$MixFreTD.HC0.xts <- merge.xts(H$HourlyTD.xts.List$HC0_60_hpf.2.xts,
H$DailyTD.HC0.xts,
join = "left")
index(H$MixFreTD.HC0.xts) <- index(H$HourlyTD.xts.List$HC0_60_hpf.xts)
#Try Using ggplot2 to show up the result.
H$x_label <- seq.POSIXt(from = index(H$MixFreTD.HC0.xts)[1],
to = index(H$MixFreTD.HC0.xts)[dim(H$MixFreTD.HC0.xts)[1]], by = "week")
H$MixFreTD.HC0.gg <- ggplot(H$MixFreTD.HC0.xts, aes(x = as.factor(index(H$MixFreTD.HC0.xts)), group = 1)) +
geom_line(aes(y = HC0_60Close)) +
geom_line(aes(y = HC0_60_hpft), colour = "red") +
geom_point(aes(y = HC0_hpft), colour = "blue", na.rm = TRUE) +
scale_x_discrete(breaks = c(as.character(H$x_label)), labels = format(x = H$x_label, format = "%m%d")) +
ggtitle("HC0 Hourly and Daily Trend.") + xlab("Trading Date")
H$MixFreTD.HC0.gg
```
### H_GHD
```{r, include=FALSE}
#Functions.======================================
#Functions to obtain timeSeries dataset.
H$DataSet_Fetch <- function(ref, suffix){
attach(c(H$HourlyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$HourlyTD.xts.List))
return(Return.get.ts)
}
#Preparing Dataset.==============================
H$DIST.Dataset.H <- H$DataSet_Fetch(H$ref_TD.hour[H$ind], "_hpf.xts")
H$DIST.Dataset.H.cycle <- na.omit(H$DIST.Dataset.H[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.H.ycycle <- H$DIST.Dataset.H.cycle[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))]
H$DIST.H.ef <- density(H$DIST.H.ycycle)
#Density
H$DIST.H.ghdfit <- fit.ghypuv(H$DIST.H.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.H.ghddense <- dghyp(H$DIST.H.ef$x, H$DIST.H.ghdfit)
H$DIST.H.col.def <- c("black", "blue")
#VaR and ES
H$DIST.H.p <- seq(0.001, 0.05, 0.001)
H$DIST.H.VaR <- abs(qghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.emp.VaR <- abs(quantile(x = H$DIST.H.ycycle, probs = H$DIST.H.p))
H$DIST.H.ES <- abs(ESghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.obs.p <- ceiling(H$DIST.H.p * length(H$DIST.H.ycycle))
H$DIST.H.emp.ES <- sapply(H$DIST.H.obs.p, function(x) abs(mean(sort(c(H$DIST.H.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.H.ef, xlab = paste0(H$ref_TD.hour[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.H.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD.hour[H$ind], " Cycle"))
lines(H$DIST.H.ef$x, H$DIST.H.ghddense, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#VaR
plot(H$DIST.H.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.H.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.VaR)))
lines(seq(along = H$DIST.H.p), H$DIST.H.VaR, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#ES
plot(H$DIST.H.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.H.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.ES)))
lines(1:length(H$DIST.H.p), H$DIST.H.ES, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
par(mfcol = c(1,1))
```
### D_GHD
```{r, include=FALSE}
##Functions.=====================================
H$DataSet_Fetch_D <- function(ref, suffix){
attach(c(H$DailyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$DailyTD.xts.List))
return(Return.get.ts)
}
#Fetching Dataset.
H$DIST.Dataset.D <- H$DataSet_Fetch_D(H$ref_TD[H$ind], "_hpf.xts")
H$DIST.Dataset.D.cycle <- na.omit(H$DIST.Dataset.D[, c(paste0(H$ref_TD[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.D.ycycle <- H$DIST.Dataset.D.cycle[, c(paste0(H$ref_TD[H$ind], "_hpfc"))]
H$DIST.D.ef <- density(H$DIST.D.ycycle)
#Density
H$DIST.D.ghdfit <- fit.ghypuv(H$DIST.D.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.D.ghddense <- dghyp(H$DIST.D.ef$x, H$DIST.D.ghdfit)
H$DIST.D.col.def <- c("black", "red")
#VaR and ES
H$DIST.D.p <- seq(0.001, 0.05, 0.001)
H$DIST.D.VaR <- abs(qghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.emp.VaR <- abs(quantile(x = H$DIST.D.ycycle, probs = H$DIST.D.p))
H$DIST.D.ES <- abs(ESghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.obs.p <- ceiling(H$DIST.D.p * length(H$DIST.D.ycycle))
H$DIST.D.emp.ES <- sapply(H$DIST.D.obs.p, function(x) abs(mean(sort(c(H$DIST.D.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.D.ef, xlab = paste0(H$ref_TD[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.D.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD[H$ind], " Cycle"))
lines(H$DIST.D.ef$x, H$DIST.D.ghddense, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#VaR
plot(H$DIST.D.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.D.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.VaR)))
lines(seq(along = H$DIST.D.p), H$DIST.D.VaR, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#ES
plot(H$DIST.D.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.D.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.ES)))
lines(1:length(H$DIST.D.p), H$DIST.D.ES, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
par(mfcol = c(1,1))
```
### GPD_VaRnES
```{r, paged.print=TRUE}
H$GPD.H.cycle.tg1 <- H$DIST.H.ycycle
H$GPD.D.cycle.tg1 <- H$DIST.D.ycycle
##Defining probability parameters.
H$prob.ind <- c(0.25, 0.50, 0.75, 0.85, 0.95, 0.99)
##GPD
H$GPD.H.fit.tg1 <- gpdFit(H$GPD.H.cycle.tg1, u = 180)
H$GPD.H.VaRES.tg1 <- round(gpdRiskMeasures(H$GPD.H.fit.tg1, prob = H$prob.ind),
digits = 3)
H$GPD.D.fit.tg1 <- gpdFit(H$GPD.D.cycle.tg1, u = 350)
H$GPD.D.VaRES.tg1 <- round(gpdRiskMeasures(H$GPD.D.fit.tg1, prob = H$prob.ind),
digits = 3)
##Output Table
H$GPD.H.VaRES.tg1.gg <- ggtexttable(H$GPD.H.VaRES.tg1,
cols = c("Prob", "VaR", "ES"),
theme = ttheme(colnames.style = colnames_style(color = "pink",
fill = "yellow",
size = 20),
tbody.style = tbody_style(size = 20))
)
H$GPD.D.VaRES.tg1.gg <- ggtexttable(H$GPD.D.VaRES.tg1,
cols = c("Prob", "VaR", "ES"),
theme = ttheme(colnames.style = colnames_style(color = "white",
fill = "orange",
size = 20),
tbody.style = tbody_style(size = 20))
)
H$GPD.H.VaRES.tg1.gg.output <- ggarrange(H$GPD.H.VaRES.tg1.gg,
H$GPD.D.VaRES.tg1.gg,
ncol = 2, nrow = 1)
annotate_figure(H$GPD.H.VaRES.tg1.gg.output, top = text_grob(
paste0("VaR and ES under GPD of ", H$ref_TD.hour[H$ind], " and ", H$ref_TD[H$ind], "."),
size = 20, vjust = 5
))
```
IRONORE
==================================================
STATUS and DIST {.tabset .tabset-fade}
-------------------------------------
### STATUS
```{r, include=FALSE}
H$ind <- 3
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, -3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List[[paste0(H$ref_TD.hour[H$ind], '_hpf.xts')]][, 3],
main=paste0("Target 1: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### 30 DAYS
```{r, include = FALSE}
#xts output.
par(mai=c(0.5,0.5,0.5,0.5))
par(mfcol=c(1,1))
plot(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, -3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " Hodrict Presccot Filter"),observation.based = TRUE)
```
```{r}
addSeries(H$HourlyTD.xts.List.wd[[paste0(H$ref_TD.hour[H$ind], '_wd_hpf.xts')]][, 3],
main=paste0("Target: ", H$ref_TD.hour.text[H$ind], " HPFC"))
```
### HvD STATUS
```{r}
#Changed into xts file.
H$DailyTD.I0.xts <- H[["DailyTD.xts.List"]][["I0_hpf.xts"]]
H$HourlyTD.xts.List$I0_60_hpf.2.xts <- align.time(H$HourlyTD.xts.List$I0_60_hpf.xts,
n = 9 * 60*60)
#Align daily trade data with hourly trade data.
H$MixFreTD.I0.xts <- merge.xts(H$HourlyTD.xts.List$I0_60_hpf.2.xts,
H$DailyTD.I0.xts,
join = "left")
index(H$MixFreTD.I0.xts) <- index(H$HourlyTD.xts.List$I0_60_hpf.xts)
#Try Using ggplot2 to show up the result.
H$x_label <- seq.POSIXt(from = index(H$MixFreTD.I0.xts)[1],
to = index(H$MixFreTD.I0.xts)[dim(H$MixFreTD.I0.xts)[1]], by = "week")
H$MixFreTD.I0.gg <- ggplot(H$MixFreTD.I0.xts, aes(x = as.factor(index(H$MixFreTD.I0.xts)), group = 1)) +
geom_line(aes(y = I0_60Close)) +
geom_line(aes(y = I0_60_hpft), colour = "red") +
geom_point(aes(y = I0_hpft), colour = "blue", na.rm = TRUE) +
scale_x_discrete(breaks = c(as.character(H$x_label)), labels = format(x = H$x_label, format = "%m%d")) +
ggtitle("I0 Hourly and Daily Trend.") + xlab("Trading Date")
H$MixFreTD.I0.gg
```
### H_GHD
```{r, include=FALSE}
#Functions.======================================
#Functions to obtain timeSeries dataset.
H$DataSet_Fetch <- function(ref, suffix){
attach(c(H$HourlyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$HourlyTD.xts.List))
return(Return.get.ts)
}
#Preparing Dataset.==============================
H$DIST.Dataset.H <- H$DataSet_Fetch(H$ref_TD.hour[H$ind], "_hpf.xts")
H$DIST.Dataset.H.cycle <- na.omit(H$DIST.Dataset.H[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.H.ycycle <- H$DIST.Dataset.H.cycle[, c(paste0(H$ref_TD.hour[H$ind], "_hpfc"))]
H$DIST.H.ef <- density(H$DIST.H.ycycle)
#Density
H$DIST.H.ghdfit <- fit.ghypuv(H$DIST.H.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.H.ghddense <- dghyp(H$DIST.H.ef$x, H$DIST.H.ghdfit)
H$DIST.H.col.def <- c("black", "blue")
#VaR and ES
H$DIST.H.p <- seq(0.001, 0.05, 0.001)
H$DIST.H.VaR <- abs(qghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.emp.VaR <- abs(quantile(x = H$DIST.H.ycycle, probs = H$DIST.H.p))
H$DIST.H.ES <- abs(ESghyp(H$DIST.H.p, H$DIST.H.ghdfit))
H$DIST.H.obs.p <- ceiling(H$DIST.H.p * length(H$DIST.H.ycycle))
H$DIST.H.emp.ES <- sapply(H$DIST.H.obs.p, function(x) abs(mean(sort(c(H$DIST.H.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.H.ef, xlab = paste0(H$ref_TD.hour[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.H.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD.hour[H$ind], " Cycle"))
lines(H$DIST.H.ef$x, H$DIST.H.ghddense, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#VaR
plot(H$DIST.H.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.H.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.VaR)))
lines(seq(along = H$DIST.H.p), H$DIST.H.VaR, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
#ES
plot(H$DIST.H.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.H.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD.hour[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.H.p), labels = names(H$DIST.H.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.H.emp.ES)))
lines(1:length(H$DIST.H.p), H$DIST.H.ES, col = "blue")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.H.col.def, lty = 1)
par(mfcol = c(1,1))
```
### D_GHD
```{r, include=FALSE}
##Functions.=====================================
H$DataSet_Fetch_D <- function(ref, suffix){
attach(c(H$DailyTD.xts.List))
for(i in 1:min(length(ref), length(suffix))){
Fetch.get <- get(paste0(ref[i], suffix[i]))
if(i == 1){
Return.get <- Fetch.get
}else{
Return.get <- merge.xts(Return.get, Fetch.get, join = "left")
}
}
Return.get.ts <- timeSeries(Return.get, index(Return.get), format = "%Y-%m-%d %H:%M:%S")
#Return desired objects
detach(c(H$DailyTD.xts.List))
return(Return.get.ts)
}
#Fetching Dataset.
H$DIST.Dataset.D <- H$DataSet_Fetch_D(H$ref_TD[H$ind], "_hpf.xts")
H$DIST.Dataset.D.cycle <- na.omit(H$DIST.Dataset.D[, c(paste0(H$ref_TD[H$ind], "_hpfc"))])
#Fitting Cycle to distribution function.
H$DIST.D.ycycle <- H$DIST.Dataset.D.cycle[, c(paste0(H$ref_TD[H$ind], "_hpfc"))]
H$DIST.D.ef <- density(H$DIST.D.ycycle)
#Density
H$DIST.D.ghdfit <- fit.ghypuv(H$DIST.D.ycycle, symmetric = FALSE, control = list(maxit = 1000))
H$DIST.D.ghddense <- dghyp(H$DIST.D.ef$x, H$DIST.D.ghdfit)
H$DIST.D.col.def <- c("black", "red")
#VaR and ES
H$DIST.D.p <- seq(0.001, 0.05, 0.001)
H$DIST.D.VaR <- abs(qghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.emp.VaR <- abs(quantile(x = H$DIST.D.ycycle, probs = H$DIST.D.p))
H$DIST.D.ES <- abs(ESghyp(H$DIST.D.p, H$DIST.D.ghdfit))
H$DIST.D.obs.p <- ceiling(H$DIST.D.p * length(H$DIST.D.ycycle))
H$DIST.D.emp.ES <- sapply(H$DIST.D.obs.p, function(x) abs(mean(sort(c(H$DIST.D.ycycle))[1:x])))
```
```{r}
par(mfcol = c(1, 3))
#Cycle
plot(H$DIST.D.ef, xlab = paste0(H$ref_TD[H$ind], " Cycle"), ylab = expression(f(x)),
ylim = c(0, max(H$DIST.D.ef$y*1.2)), main = paste0("Distribution of ", H$ref_TD[H$ind], " Cycle"))
lines(H$DIST.D.ef$x, H$DIST.D.ghddense, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#VaR
plot(H$DIST.D.emp.VaR, type = "l", xlab = "", ylab = "VaR", axes = FALSE, ylim = range(c(H$DIST.D.emp.VaR)),
main = paste0("Value at Risk of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = seq(along = H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.VaR)))
lines(seq(along = H$DIST.D.p), H$DIST.D.VaR, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
#ES
plot(H$DIST.D.emp.ES, type = "l", xlab = "", ylab = "ES", axes = FALSE, ylim = range(c(H$DIST.D.emp.ES)),
main = paste0("Expected Shortfall of ", H$ref_TD[H$ind], " Cycle"))
box()
axis(1, at = 1:length(H$DIST.D.p), labels = names(H$DIST.D.emp.VaR), tick = FALSE)
axis(2, at = pretty(range(H$DIST.D.emp.ES)))
lines(1:length(H$DIST.D.p), H$DIST.D.ES, col = "red")
legend("topleft", legend = c("empirical", "GHD"), col = H$DIST.D.col.def, lty = 1)
par(mfcol = c(1,1))
```
### GPD_VaRnES
```{r, paged.print=TRUE}
H$GPD.H.cycle.tg1 <- H$DIST.H.ycycle
H$GPD.D.cycle.tg1 <- H$DIST.D.ycycle
##Defining probability parameters.