SP500 daily - надвор од програмата на #TidyTuesday

library("tidyverse")
library("treemapify")
library("quantmod")
library("BatchGetSymbols")
options(scipen = 999)

Note: All of the code in this Rmd is eval = FALSE because the data doesn’t properly load.

Functions. date_converter converts a date from the form 2019-10-11 to string “October 11, 2019” to use in naming the graphs later. is_weekend checks if the supplied date is on the weekend, and returns the date of the friday before that weekend, because stock markets close on weekends.

date_converter <- function(date) {
  months <- c("January","February","March","April","May","June","July","August","September", "October","November","December")
  converted_date <- paste(months[as.numeric(substring(date, 6, 7))],paste0(substring(date, 9, 10),","), substring(date,1,4))
  return(converted_date)
}


is_weekend <- function(date) {
if (weekdays(date)=="Saturday"){
  return(date-1)
} else if (weekdays(date)=="Sunday") {
  return(date-2)
} else {
  return(date)
}}

Get the data. Downloads a dataframe of all stocks, and a daily price chart.

first.date <- Sys.Date()
last.date <- Sys.Date()+1

#last.date <- as.Date("2019-10-16")
#first.date <- as.Date("2019-10-15")

freq.data <- 'daily'

stock_list <- GetSP500Stocks()
tickers <- stock_list$Tickers
tickers_test <- c("MMM","FB","AAPL")

l.out <- BatchGetSymbols(tickers= tickers,
                         first.date = first.date,
                         last.date = last.date)

#market cap dataset
market_cap <- getQuote(tickers, what=yahooQF("Market Capitalization"))
market_cap <- tibble::rownames_to_column(market_cap, "ticker")
market_cap <- market_cap %>% 
  select(-"Trade Time")
names(market_cap)<-str_replace_all(names(market_cap), c(" " = "." , "," = "" ))

stock_list$ticker <- stock_list$Tickers

stock_list <- stock_list %>% 
  select(-Tickers)

stock_perf <- l.out$df.tickers

stock_list_perf <- merge(stock_list, stock_perf, by="ticker")

stock_list_perf <- merge(stock_list_perf, market_cap, by="ticker")

stock_list_perf <- stock_list_perf %>% 
  filter(price.open>0) %>% 
  mutate(
    price.change.prop=(price.close-price.open)/price.open,
  )

industry_perf <- stock_list_perf %>% 
  group_by(GICS.Sector) %>% 
  summarize(
    number_stocks=n(),
    market_cap=sum(Market.Capitalization),
    price.change.prop=weighted.mean(price.change.prop, Market.Capitalization, na.rm=T),
    #price.change.prop=mean(price.change.prop),
    price.change.percent=paste0(round(price.change.prop*100,2),"%")
    )
industry_perf

ggplot(data=industry_perf, 
       mapping=aes(area=market_cap, 
                   fill=price.change.prop, 
                   subgroup=price.change.prop,
                   layout="scol")) +
  geom_treemap()+
  geom_treemap_text(aes(label=GICS.Sector), 
                    family="Ubuntu",
                    size = 14,
                    colour = "#292929",
                    place = "top", 
                    reflow=TRUE,
                    padding.x = grid::unit(2, "mm"),
                    padding.y = grid::unit(5, "mm"))+
  geom_treemap_text(aes(label=price.change.percent), 
                    family="Ubuntu",
                    size=21,
                    colour = "#292929", 
                    place="bottom", 
                    reflow=TRUE,
                    padding.x = grid::unit(2, "mm"), 
                    padding.y = grid::unit(8, "mm"))+
  geom_treemap_subgroup_border(color="#292929", 
                               size=1)+
  scale_fill_gradientn(colours = c("#590000","#b50a00","white","#00ba0c","#035900"), 
                       limits = c(-0.03, 0.03),
                       guide=FALSE)+
  labs(title=paste("S&P500 Industry Performance for", date_converter(is_weekend(first.date))),
       caption = "Ljupcho.com")+
  theme(text = element_text(family = "Ubuntu",color = "#292929"),
        plot.title = element_text(size=18))

ggsave(paste0("S&P-Industry-Perf-",first.date,".png"), width = 8, height = 7)
print(paste0("S&P-Industry-Perf-",first.date,".png"))