Retrieving Stock Price using R
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Let’s download stock prices using getSymbols() function in quantmod R library. I hope he will come back soon. Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
It is easy to download. The first thing is to find the ticker for the security. The second thing is to run the following R code with ticker. Tickers are easily found in search engines such as Google.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | #=========================================================================# # Financial Econometrics & Derivatives, ML/DL using R, Python, Tensorflow # by Sang-Heon Lee #————————————————————————-# # Retreiving Stock Price using quantmod #=========================================================================# library(quantmod) library(xts) library(ggplot2) library(gridExtra) # grid.arrange graphics.off() rm(list=ls()) # Data Range sdate <– as.Date(“2018-07-01”) edate <– as.Date(“2019-12-31”) # Samsung Electronics (005930), Naver (035420) ss_stock=getSymbols(‘005930.KS’,from=sdate,to=edate,auto.assign = F) nv_stock=getSymbols(‘035420.KS’,from=sdate,to=edate,auto.assign = F) # Typically use previous value for NA no.na <– which(is.na(ss_stock[,6])) # no for NA ss_stock[no.na,6] <– ss_stock[no.na–1,6] no.na <– which(is.na(nv_stock[,6])) nv_stock[no.na,6] <– nv_stock[no.na–1,6] # Only stock price ss_price <– ss_stock[,6] nv_price <– nv_stock[,6] # log return using adjusted stock price ss_rtn <– diff(log(ss_price),1) nv_rtn <– diff(log(nv_price),1) # draw graph x11(width=5.5, height=6) plot1<–ggplot(ss_price, aes(x = index(ss_price), y = ss_price)) + geom_line(color = “blue”, size=1.2) + ggtitle(“SEC stock price”) + xlab(“Date”) + ylab(“Price(￦)”) + theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = “%y-%m”, date_breaks = “3 months”) plot2 <– ggplot(ss_rtn, aes(x = index(ss_rtn), y = ss_rtn)) + geom_line(color = “red”, size=1.2) + ggtitle(“SEC stock return”) + xlab(“Date”) + ylab(“Return(%)”) + theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = “%y-%m”, date_breaks = “3 months”) grid.arrange(plot1, plot2, ncol=1, nrow = 2) x11(width=5.5, height=6) plot1<–ggplot(nv_price, aes(x = index(nv_price), y = nv_price)) + geom_line(color = “blue”, size=1.2) + ggtitle(“Naver stock price”) + xlab(“Date”) + ylab(“Price(￦)”) + theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = “%y-%m”, date_breaks = “3 months”) plot2 <– ggplot(nv_rtn, aes(x = index(nv_rtn), y = nv_rtn)) + geom_line(color = “red”, size=1.2) + ggtitle(“Naver stock return”) + xlab(“Date”) + ylab(“Return(%)”) + theme(plot.title = element_text(hjust = 0.5)) + scale_x_date(date_labels = “%y-%m”, date_breaks = “3 months”) grid.arrange(plot1, plot2, ncol=1, nrow = 2) | cs |
Running the above R code downloads the daily stock prices of Samsung Electronic Company(SEC) and Naver from 2018.07 to 2019.12 and calculates daily returns and draws a chart.
From this work, we can prepare historical stock prices for the further analysis. You can change the period to include recent data. \(\blacksquare\)
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