Bitcoin Analysis as of 11-24-2020

 

/* Generated Code (IMPORT) */
/* Source File: DATA.csv */
/* Source Path: /folders/myfolders/Stock Market */
/* Code generated on: 11/25/20, 10:12 AM */

%web_drop_table(WORK.IMPORT);


FILENAME REFFILE '/folders/myfolders/Bitcoin.csv';

PROC IMPORT DATAFILE=REFFILE
	DBMS=CSV
	OUT=WORK.Bitcoin;
	GETNAMES=YES;
RUN;

proc sort data=bitcoin out=bitcoin;
 by date;
run;

proc sgplot data=Bitcoin;
	title height=14pt "Bitcoin";
	footnote2 justify=left height=8pt "Source: Coinbase";
	series x=Date y=Last /;
	xaxis grid;
	yaxis grid;
run;

ods graphics / reset;
title;
footnote2;

DATA Bitcoin.Bitcoin;
 set Bitcoin (drop=VAR1);
 rename Last=Bitcoin;
 
 Year=Year(Date);
 Month=Month(Date);
 Day=Day(Date);
  
  pctchng = dif(Last) / lag( Last ) * 100;
      label pctchng = "Percent Change";
      
  pctchng_month = ( ( Last / lag( Last ) ) ** 30 - 1 ) * 100;
      label pctchng_month = "Monthly Percent Change, At Annual Rates";
      
  pctchng_yr = dif365( Last ) / lag365( Last ) * 100;
      label pctchng_yr = "Percent Change from One Year Ago";
run;


proc sgplot data=Bitcoin.Bitcoin;
	title height=14pt "Percent changes from previous year";
	footnote2 justify=left height=8pt "Source: Coinbase";
	series x=Date y=pctchng_yr /;
	xaxis grid;
	yaxis grid;
run;

proc sgplot data=Bitcoin.Bitcoin;
title height=12pt "Bitcoin";
	vbar Year / response=pctchng fillattrs=(color=CXcad5e5) datalabel;
	yaxis grid;
run;

proc sgplot data=Bitcoin.Bitcoin;
	vbar Month / response=pctchng fillattrs=(color=CXcad5e5) datalabel stat=mean;
	yaxis grid;
run;

proc sgplot data=Bitcoin.Bitcoin;
	vbar Day / response=pctchng fillattrs=(color=CXcad5e5) datalabel stat=mean;
	yaxis grid;
run;

SAS codes

FILENAME REFFILE '/folders/myfolders/Bitcoin.csv';

PROC IMPORT DATAFILE=REFFILE

DBMS=CSV

OUT=WORK.Bitcoin;

GETNAMES=YES;

RUN;

proc sort data=bitcoin out=bitcoin;

by date;

run;

proc sgplot data=Bitcoin;

title height=14pt "Bitcoin";

footnote2 justify=left height=8pt "Source: Coinbase";

series x=Date y=Last /;

xaxis grid;

yaxis grid;

run;

ods graphics / reset;

title;

footnote2;

DATA Bitcoin.Bitcoin;

set Bitcoin (drop=VAR1);

rename Last=Bitcoin;

Year=Year(Date);

Month=Month(Date);

Day=Day(Date);

pctchng = dif(Last) / lag( Last ) * 100;

label pctchng = "Percent Change";

pctchng_month = ( ( Last / lag( Last ) ) ** 30 - 1 ) * 100;

label pctchng_month = "Monthly Percent Change, At Annual Rates";

pctchng_yr = dif365( Last ) / lag365( Last ) * 100;

label pctchng_yr = "Percent Change from One Year Ago";

run;

proc sgplot data=Bitcoin.Bitcoin;

title height=14pt "Percent changes from previous year";

footnote2 justify=left height=8pt "Source: Coinbase";

series x=Date y=pctchng_yr /;

xaxis grid;

yaxis grid;

run;

proc sgplot data=Bitcoin.Bitcoin;

title height=12pt "Bitcoin";

vbar Year / response=pctchng fillattrs=(color=CXcad5e5) datalabel;

yaxis grid;

run;

proc sgplot data=Bitcoin.Bitcoin;

vbar Month / response=pctchng fillattrs=(color=CXcad5e5) datalabel stat=mean;

yaxis grid;

run;

proc sgplot data=Bitcoin.Bitcoin;

vbar Day / response=pctchng fillattrs=(color=CXcad5e5) datalabel stat=mean;

yaxis grid;

run;

Bitcoin and Gold prices

Bitcoin 

Gold prices

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))