#*****************************************************************
# Load historical data
#*****************************************************************
library(SIT)
load.packages('quantmod')

ticker = 'IBM'
data = env()

getSymbols.extra(ticker, src = 'yahoo', from = '1970-01-01', env = data,  set.symbolnames = T, auto.assign = T)
	for(i in data$symbolnames) data[[i]] = adjustOHLC(data[[i]], use.Adjusted=T)
bt.prep(data, align='remove.na', fill.gaps = T)

data = data[[ticker]]
	
#*****************************************************************
# Load events
#*****************************************************************
events = zacks.info(ticker)

#*****************************************************************
# Highlight each quarterly earnings
#*****************************************************************
highlight = match(index(data), as.Date(events$earnings$Date, '%m/%d/%Y'))
	highlight = make.xts(highlight, index(data))	
	dates = highlight[!is.na(highlight)]
	highlight[] = ifna.prev(highlight)
	

x = data['2012::']
highlight = highlight['2012::']

plota(x)
	col = col.add.alpha(highlight, 20)
	plota.x.highlight(highlight, highlight != 0, col)
	plota.lines(x)
	plota.legend(ticker)

#*****************************************************************
# Compute daily volatility for each quarter
#*****************************************************************
ret = diff(log(Cl(x)))

hist.vol = 100 * sqrt(252) * tapply(ret, highlight, sd, na.rm=T)

temp = data.frame(date = index(dates), col=coredata(dates), hist.vol = hist.vol[match(dates, names(hist.vol))])
	temp = na.omit(temp)

par(mar = c(6, 4, 2, 2))
barplot(temp$hist.vol, col=col.add.alpha(temp$col, 20), names.arg=temp$date, las=2)

#*****************************************************************
# Compare daily volatility from 2 days prior to earnings date to 2 days after 
# the earnings date versus all other days
#*****************************************************************
index = which(!is.na( match(index(ret), index(dates)) ))
index = c(index - 1, index -2, index, index + 1, index + 2)
index = index[index > 0 & index <= len(ret)]

temp = list(
	'Around Earnings' = sd(ret[index], na.rm=T),
	'All Other Days' = sd(ret[-index], na.rm=T)
)

temp = t(data.frame(temp))
	colnames(temp) = 'Historical Volatility'
	print(to.nice(100 * sqrt(252) * temp))

#*****************************************************************
# Print info downloaded from Zacks
#*****************************************************************
for(n in ls(events)) {
	print('#', n)
	print(apply(events[[n]],2,remove.tags))
}