The document describes a data science project conducted by a student at NYC Data Science Academy focusing on analyzing equity securities using R. It outlines the process of connecting to the Bloomberg API to retrieve various data points, performing analyses, and generating visualizations related to market capitalization, prices, shares outstanding, and trading volume over the past three years. The project also highlights the student's learning experience, challenges faced, and methods used to overcome them.

1. NYC Data Science Academy
Student Demo day 07-21-2014
R005: Data Science by R(Beginner level)

2. Previous work on these factors
How this is useful?
Difficulties I faced during the process
› How I overcame them?
What I learned throughout the class and the
project?

3. Sys.setenv(JAVA_HOME='C:Program FilesJavajre7')
install.packages("Rbbg", repos="http://r.findata.org")
#establishing connecting to Bloomberg API
conn <- blpConnect()
securities <- c("ALK US Equity", "DAL US Equity", "JBLU US Equity", "LUV US Equity",
"SAVE US Equity", "UAL US Equity", "CHRW US Equity", "EXPD US Equity",
"FDX US Equity", "HUBG US Equity", "UPS US Equity", "UTIW US Equity",
"XPO US Equity", "CSX US Equity", "KSU US Equity", "NSC US Equity",
"UNP US Equity", "CAR US Equity", "CNW US Equity", "HTZ US Equity",
"JBHT US Equity")
fields <- c("PX_LAST", "TOT_MKT_VAL", "VOLATILITY_90D", "EQY_SH_OUT", "VOLUME")
allsecurities <- bdh(conn, securities, fields, Sys.Date()-730,
always.display.tickers=TRUE, nclude.non.trading.days=FALSE,
dates.as.row.names=FALSE)

4. Returns – c(NA, diff(log(maindata$PX_LAST)))
Examine data:
› Head(maindata)
› Tail(maindata)
› Dim(maindata) #13440 by 8
› Summary(maindata)
› Str(maindata)
› Sapply(maindata) – had to fix for date
› Names(maindata)
”ticker”, “date”, “PX_LAST”, “TOT_MKT_VAL”, “VOLATILITY_90D”, “EQY_SH_OUT”,
“VOLUME”, “returns”

5. freight_logistics <- c("CHRW US Equity", "EXPD US
Equity", "FDX US Equity", "HUBG US
Equity", "UPS US Equity", "UTIW US Equity",
"XPO US Equity")
frlo <- bdh(conn, freight_logistics, fields, Sys.Date()- 730,
always.display.tickers=TRUE,
include.non.trading.days=FALSE,
dates.as.row.names=FALSE)
frloreturns <- c(NA,diff(log(frlo$PX_LAST)))
freightlogistics <- transform(frlo, returns=frloreturns)
Head(freightlogistics)
fl.data <- melt(freightlogistics,id=c("ticker","date"))
unique(fl.data$variable)
rfl.data <- cast(subset(fl.data,variable=="returns"),
date~ticker, sum)
summary(rfl.data)
chart.Correlation(rfl.data)

6. Freight and Logistics
Railroads
Trucking
Airlines

7. mktcap <- ggplot(data=maindata, aes(x=ticker, y=TOT_MKT_VAL, colour=ticker)) +
geom_point() +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.background = element_blank(),
legend.background=element_rect(fill="white", colour="white") ) +
labs(title="Total Market Cap for Each Security for the Last 3 Years", x="Ticker",
y="Total Market Cap")
print(mktcap)

8. price <- ggplot(data=maindata, aes(x=ticker, y=PX_LAST, colour=ticker))+ geom_point() +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.background = element_blank(),
legend.background=element_rect(fill="white", colour="white") ) +
labs(title="Price for Each Security for the Last 3 Years", x="Ticker", y="Last Price")
print(price)

9. shares <- ggplot(data=maindata, aes(x=ticker, y=EQY_SH_OUT, colour=ticker))+
geom_point() +
theme_bw() +
theme(panel.grid.major = element_blank(), panel.background = element_blank(),
legend.background=element_rect(fill="white", colour="white") ) +
labs(title="Amount of Shares for Each Security for the Last 3 Years", x="Ticker",
y="Amount of Shares Outstanding")
print(shares)

10. volume <- ggplot(data=maindata, aes(x=ticker, y=VOLUME, colour=ticker)) + geom_point()
+
theme_bw() +
theme(panel.grid.major = element_blank(), panel.background = element_blank(),
legend.background=element_rect(fill="white", colour="white") ) +
labs(title="Volume for Each Security for the Last 3 Years", x="Ticker",
y="Volume per Day")
print(volume)

11. DAL <- subset(maindata,
ticker=="DAL US Equity")
DALvol <- ggplot(data=DAL,
aes(x=date, y=VOLUME,
colour=ticker))+
geom_point() +
theme_bw() +
theme(panel.grid.major = element_blank(),
panel.background =
element_blank(),
legend.background=
element_rect(fill="white",
colour="white")) +
labs(title="Delta's (DAL US Equity) Daily
Volume for the Last 3 Years",
x="Date", y="Volume per Day")
print(DALvol)