ticker_list <- c("LULU")
yahoo_url <- "https://finance.yahoo.com/quote/LULU/analysis?p=LULU&.tsrc=fin-srch"
url_file <- GET(yahoo_url)
web_page_parsed <- htmlParse(url_file, encoding = "UTF-8")
tables <- readHTMLTable(web_page_parsed)
print(head(tables))## $`NULL`
## Earnings Estimate Current Qtr. (Oct 2018) Next Qtr. (Jan 2019)
## 1 No. of Analysts 34 33
## 2 Avg. Estimate 0.69 1.64
## 3 Low Estimate 0.67 1.56
## 4 High Estimate 0.75 1.76
## 5 Year Ago EPS 0.56 1.33
## Current Year (2019) Next Year (2020)
## 1 37 37
## 2 3.59 4.24
## 3 3.5 3.86
## 4 3.8 4.7
## 5 2.59 3.59
##
## $`NULL`
## Revenue Estimate Current Qtr. (Oct 2018) Next Qtr. (Jan 2019)
## 1 No. of Analysts 31 30
## 2 Avg. Estimate 735.87M 1.12B
## 3 Low Estimate 724.1M 1.09B
## 4 High Estimate 766.1M 1.2B
## 5 Year Ago Sales 619.02M 928.8M
## 6 Sales Growth (year/est) 18.90% 20.80%
## Current Year (2019) Next Year (2020)
## 1 35 35
## 2 3.23B 3.66B
## 3 3.19B 3.49B
## 4 3.32B 3.8B
## 5 2.65B 3.23B
## 6 21.90% 13.40%
##
## $`NULL`
## Earnings History 10/30/2017 1/30/2018 4/29/2018 7/30/2018
## 1 EPS Est. 0.52 1.27 0.46 0.49
## 2 EPS Actual 0.56 1.33 0.55 0.71
## 3 Difference 0.04 0.06 0.09 0.22
## 4 Surprise % 7.70% 4.70% 19.60% 44.90%
##
## $`NULL`
## EPS Trend Current Qtr. (Oct 2018) Next Qtr. (Jan 2019)
## 1 Current Estimate 0.69 1.64
## 2 7 Days Ago 0.69 1.64
## 3 30 Days Ago 0.69 1.64
## 4 60 Days Ago 0.68 1.64
## 5 90 Days Ago 0.63 1.56
## Current Year (2019) Next Year (2020)
## 1 3.59 4.24
## 2 3.59 4.24
## 3 3.59 4.22
## 4 3.58 4.19
## 5 3.23 3.74
##
## $`NULL`
## EPS Revisions Current Qtr. (Oct 2018) Next Qtr. (Jan 2019)
## 1 Up Last 7 Days 1 1
## 2 Up Last 30 Days 3 3
## 3 Down Last 7 Days N/A N/A
## 4 Down Last 30 Days N/A N/A
## Current Year (2019) Next Year (2020)
## 1 1 1
## 2 4 4
## 3 N/A N/A
## 4 N/A N/A
##
## $`NULL`
## Growth Estimates LULU Industry Sector S&P 500
## 1 Current Qtr. 23.20% N/A N/A 0.48
## 2 Next Qtr. 23.30% N/A N/A 0.40
## 3 Current Year 38.60% N/A N/A 0.21
## 4 Next Year 18.10% N/A N/A 0.10
## 5 Next 5 Years (per annum) 24.06% N/A N/A 0.11
## 6 Past 5 Years (per annum) 9.54% N/A N/A N/A
Data frame with Ticker, ind, quarter, value
http://markets.money.cnn.com/Marketsdata/Sectors
industrial_list <- c("AROC","BHGE","BAS","BRS","CJ","WHD","CRR","CLB","CCLP","DWSN","DRQ")
energy_list <- c("ANDX","CVI","CVRR","DK","ENBL","HFC","MPC","NS","PBF","PBFX","PSX")
get_table <- function(ticker_list){
industry_trend <- data.frame()
for (ticker in ticker_list){
yahoo_url <- paste("https://finance.yahoo.com/quote/",ticker,"/analysis?p=",
ticker,"&.tsrc=fin-srch",sep="")
web_page_parsed <- htmlParse(GET(yahoo_url), encoding = "UTF-8")
tables <- readHTMLTable(web_page_parsed)
EPS_trend <- tables[[3]][1,-1]
EPS_trend$Ticker <- ticker
# names(EPS_trend) <- c("Current Qtr.","Next Qtr.","Current Year"," Next Year","Ticker")
names(EPS_trend) <- c("10/30/2017","1/30/2018","4/29/2018","7/30/2018","Ticker")
industry_trend <- rbind(industry_trend,EPS_trend)
}
return(industry_trend)
}industrial_trend <- get_table(industrial_list)
energy_trend <- get_table(energy_list)
print(industrial_trend)## 10/30/2017 1/30/2018 4/29/2018 7/30/2018 Ticker
## 1 -0.07 0 0.06 0.07 AROC
## 2 0.14 0.06 0.14 0.2 BHGE
## 3 -0.45 -0.66 -0.71 -0.74 BAS
## 4 -0.65 -0.66 -0.69 -0.64 BRS
## 5 0.24 0.37 0.52 0.16 CJ
## 6 0.28 0.32 0.49 0.5 WHD
## 7 -0.7 -0.69 -0.53 -0.38 CRR
## 8 0.58 0.57 0.59 0.63 CLB
## 9 -0.24 -0.22 -0.22 -0.14 CCLP
## 10 -0.27 -0.1 -0.34 -0.25 DWSN
## 11 0.02 -0.05 -0.08 -0.21 DRQ
print(energy_trend)## 10/30/2017 1/30/2018 4/29/2018 7/30/2018 Ticker
## 1 0.76 0.62 0.67 0.69 ANDX
## 2 0.12 -0.05 0.91 0.84 CVI
## 3 0.2 0.1 0.82 0.94 CVRR
## 4 0.4 -0.13 1.17 2 DK
## 5 0.21 0.23 0.22 0.26 ENBL
## 6 0.83 0.38 1.61 1.67 HFC
## 7 0.95 0.16 2.03 1.7 MPC
## 8 0.21 0.61 0.21 -1.99 NS
## 9 0.25 -0.2 1.36 1.03 PBF
## 10 0.59 0.49 0.45 0.53 PBFX
## 11 0.86 0.89 2.19 2.48 PSX
library(reshape)## Warning: package 'reshape' was built under R version 3.5.1
##
## Attaching package: 'reshape'
## The following object is masked from 'package:dplyr':
##
## rename
industrial_trend_melt <- melt(industrial_trend,c("Ticker"))
industrial_trend_melt$Industry <- "Industrial"
energy_trend_melt <- melt(energy_trend,c("Ticker"))
energy_trend_melt$Industry <- "Energy"
stock_trend <- rbind(energy_trend_melt,industrial_trend_melt)
names(stock_trend) <- c("Ticker","Time","EPS","Industry")
print(head(stock_trend))## Ticker Time EPS Industry
## 1 ANDX 10/30/2017 0.76 Energy
## 2 CVI 10/30/2017 0.12 Energy
## 3 CVRR 10/30/2017 0.2 Energy
## 4 DK 10/30/2017 0.4 Energy
## 5 ENBL 10/30/2017 0.21 Energy
## 6 HFC 10/30/2017 0.83 Energy
First, note that EPS is saved as a factor by reshape -- to convert the values back in numerics, unfactoring is needed (see https://stackoverflow.com/questions/3418128/how-to-convert-a-factor-to-integer-numeric-without-loss-of-information):
stock_trend$EPS <- as.numeric(levels(stock_trend$EPS))[stock_trend$EPS]## Warning: NAs introduced by coercion
Let's draw a plot!
library(ggplot2)## Warning: package 'ggplot2' was built under R version 3.5.1
ggplot2::ggplot(stock_trend, aes(x = Time, y = EPS,
group = Ticker, color = Industry)) +
geom_point(size = 2) +
geom_line(size = 1.2, alpha = 0.5) +
theme_bw() +
theme(legend.position="bottom")