The ratio of Bitcoin to Gold prices - The average of ratio is 5.
library(Quandl)
library(dplyr)
library(forecast)
bit_coin=Quandl("BITSTAMP/USD", start_date="2017-01-01" )[,c("Date","Last")]
Gold_Price=Quandl("LBMA/GOLD", start_date="2017-01-01" )[,c("Date","USD (PM)")]
head(bit_coin)
ratio=Bit/Gold
avg_ratio=mean(ratio)
plot(ratio, main="Ratio of Bitcoin Prices to Gold Prices")
hist(ratio)
candleChart( Bit, up.col = "black", dn.col = "red", theme = "white", subset = "2019-01-04/")
addSMA(n = c(20, 50, 200))
addBBands()
addMACD()
candleChart( Gold, up.col = "black", dn.col = "red", theme = "white", subset = "2019-01-04/")
addSMA(n = c(20, 50, 200))
addBBands()
addMACD()
head(Gold_Price)
class(bit_coin)
mergedata<-merge(bit_coin, Gold_Price, by="Date")
Gold_Price=Quandl("LBMA/GOLD.2", start_date="2017-01-01" )
head(Gold_Price)
#Coverting bitcoin to timeseries
Bit <- xts(bit_coin[,-1], order.by=as.Date(bit_coin[,1], "%m/%d/%Y"))
autoplot(Bit)
Gold <- xts(Gold_Price[,-1], order.by=as.Date(Gold_Price[,1], "%m/%d/%Y"))
autoplot.zoo(Gold)
fit<-arima(coin, order=c(1,0,1))
accuracy(fit)
forecast(fit,50)
plot(forecast(fit,50))
d.arima <- forecast::auto.arima(coin)
autoplot(forecast::forecast(d.arima, h = 50))
autoplot(forecast::forecast(d.arima, level = c(85), h = 50))
autoplot(forecast::forecast(d.arima, h = 5), conf.int = FALSE, is.date = FALSE)
autoplot(forecast::forecast(stats::HoltWinters(UKgas), h = 10))
d.arima <- forecast::auto.arima(Gold)
autoplot(forecast::forecast(d.arima, h = 50))
d.arima <- forecast::auto.arima(ratio)
autoplot(forecast::forecast(d.arima, h = 50))
