#Modelización Garch##JesusBZerpa#
#Gráfico interactivo Plotly#

library(plotly)
library(quantmod)
portfolio = c("BTC-USD")
getSymbols(portfolio, src="yahoo", from="2014-01-01")

df <- na.omit(data.frame(Date=index(`BTC-USD`),coredata(`BTC-USD`)))

bbands <- BBands(na.omit(`BTC-USD`[,c("BTC-USD.High","BTC-USD.Low","BTC-USD.Close")]))

df <- subset(cbind(df, data.frame(bbands[,1:3])), Date >= "2014-01-01")

for (i in 1:length(df[,1])) {
  if (df$BTC.USD.Close[i] >= df$BTC.USD.Open[i]) {
      df$direction[i] = 'Increasing'
  } else {
      df$direction[i] = 'Decreasing'
  }
}

i <- list(line = list(color = '#17BECF'))
d <- list(line = list(color = '#7F7F7F'))


fig <- df %>% plot_ly(x = ~Date, type="candlestick",
          open = ~BTC.USD.Open, close = ~BTC.USD.Close,
          high = ~BTC.USD.High, low = ~BTC.USD.Low, name = "BTC-USD",
          increasing = i, decreasing = d) 
fig <- fig %>% add_lines(x = ~Date, y = ~up , name = "B Bands",
            line = list(color = '#ccc', width = 0.5),
            legendgroup = "Bollinger Bands",
            hoverinfo = "none", inherit = F) 
fig <- fig %>% add_lines(x = ~Date, y = ~dn, name = "B Bands",
            line = list(color = '#ccc', width = 0.5),
            legendgroup = "Bollinger Bands", inherit = F,
            showlegend = FALSE, hoverinfo = "none") 
fig <- fig %>% add_lines(x = ~Date, y = ~mavg, name = "Mv Avg",
            line = list(color = '#E377C2', width = 0.5),
            hoverinfo = "none", inherit = F) 
fig <- fig %>% layout(yaxis = list(title = "Precio"))

fig2 <- df 
fig2 <- fig2 %>% plot_ly(x=~Date, y=~BTC.USD.Volume, type='bar', name = "BTC.USD Volume",
          color = ~direction, colors = c('#17BECF','#7F7F7F')) 
fig2 <- fig2 %>% layout(yaxis = list(title = "Volumen"))

rs <- list(visible = TRUE, x = 0.5, y = -0.055,
           xanchor = 'center', yref = 'paper',
           font = list(size = 9),
           buttons = list(
             list(count=1,
                  label='RESET',
                  step='all'),
             list(count=1,
                  label='1 YR',
                  step='year',
                  stepmode='backward'),
             list(count=3,
                  label='3 MO',
                  step='month',
                  stepmode='backward'),
             list(count=1,
                  label='1 MO',
                  step='month',
                  stepmode='backward')
           ))

fig <- subplot(fig, fig2, heights = c(0.7,0.2), nrows=2,
             shareX = TRUE, titleY = TRUE)
fig <- fig %>% layout(title = paste("Bitcoin - USD: 2014-01-01 -",Sys.Date()),
         xaxis = list(rangeselector = rs),
         legend = list(orientation = 'h', x = 0.5, y = 1,
                       xanchor = 'center', yref = 'paper',
                       font = list(size = 10),
                       bgcolor = 'transparent'))

fig

#Preparación de data y librerías#
BTCSP <- read.csv("C:/Users/Personal/Desktop/BTCSP.csv", sep=";")
precio<-BTCSP
library(zoo)
precio.zoo=zoo(precio[,-1], order.by=as.Date(strptime(as.character(precio[,1]), "%d.%m.%Y")))
library(rmgarch)

#Modelo Garch y especificaciones#
library(PerformanceAnalytics)
model<- window(precio.zoo [,"BTC.Close"],  start="2014-01-01")
model<- na.approx(na.trim(CalculateReturns(model), side="both"))
spec = ugarchspec()
print(spec)

def.fit = ugarchfit(spec = spec, data = model)
print(def.fit)

#preparacion del modelo egarch#
garch112e2.2.2.spec = ugarchspec(mean.model = list(armaOrder = c(0,0)), 
                               variance.model = list(garchOrder = c(2,1),
                                                     model = "eGARCH"), distribution.model = "std")
garch.fit112e2.2.2= ugarchfit(garch112e2.2.2.spec, data = model, fit.control=list(scale=TRUE))
print(garch.fit112e2.2.2)

#graficas##
garch.fit = ugarchfit(garch112e2.2.2.spec, data = model, fit.control=list(scale=TRUE))
plot(garch.fit, which=3)
plot(garch.fit, which=2)

#VaR#
garchroll <- ugarchroll(garch112e2.2.2.spec, data = model, n.start =500,
                        refit.window = "moving"
                        , refit.every = 100)
garchVaR <- quantile(garchroll, probs=0.01)
actual <- xts(as.data.frame(garchroll)$Realized, time(garchVaR))

mean(actual < garchVaR)
VaRplot(alpha = 0.01, actual = actual, VaR = garchVaR)
report(garchroll, type = "VaR", VaR.alpha = 0.01, conf.level = 0.99)
plot(garch.fit, which=1)
plot(garch.fit, which=8)
plot(garch.fit, which=9)
plot(garch.fit, which=12)
plot(garch.fit, which = "all")

#Pronósticos#
forecast1<-ugarchforecast(garch.fit,n.ahead = 3) 
forecast1
plot(forecast1,which=1)
plot(forecast1,which=3)


###############################
modelfit=ugarchfit(garch112e2.2.2.spec,data=model, solver="hybrid", out.sample=20)
modelforecast<-ugarchforecast(modelfit, data = NULL, n.ahead = 30, n.roll = 20, external.forecasts = list(mregfor = NULL, vregfor = NULL))
modelforecast
garch112.1.1.spec = ugarchspec(mean.model = list(armaOrder = c(1,1)), 
                               variance.model = list(garchOrder = c(1,1),
                                                     model = "sGARCH"), distribution.model = "norm")
garch.fit112.1.1= ugarchfit(garch112.1.1.spec, data = model, fit.control=list(scale=TRUE))
garchroll2 <- ugarchroll(garch112.1.1.spec, data = model, n.start =500,
                        refit.window = "moving"
                        , refit.every = 100)
garch112.1.1.spec = ugarchspec(mean.model = list(armaOrder = c(1,1)), 
                               variance.model = list(garchOrder = c(1,1),
                                                     model = "sGARCH"), distribution.model = "norm")
garch.fit112.1.1= ugarchfit(garch112.1.1.spec, data = model, fit.control=list(scale=TRUE))
garchroll2 <- ugarchroll(garch112.1.1.spec, data = model, n.start =500,
                        refit.window = "moving"
                        , refit.every = 100)

###############################
library(rugarch)
report(garchroll, type="fpm")
report(garchroll2, type="fpm")
plot(modelforecast,which=2)
plot(modelforecast,which=4)

###############################
bootp=ugarchboot(garch.fit,method=c("Partial","Full")[1],n.ahead = 12,n.bootpred=1000,n.bootfit=1000)
bootp
plot(bootp,which=2)
plot(bootp,which=3)