finance_google.Rmd

---
title: "Stockmarket Google Finance"
output: github_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

## Get and plot historical stokmarket data from Google Finance
As the yahoo finance no longer seems to work, an alternative using google data.

```{r}
# necessary libraries
library(readr)
library(ggplot2)
library(lubridate)
library(plotly)
```

## Function to get google finance data
Use the 'read_csv' function from 'readr'

```{r}

build_url <- function(sym, sy = 2005, sm = 1, sd = 1, ey, em, ed) {
   stock_url <- paste0("http://finance.google.com/finance/historical",
                               "?q=", sym,
                               "&startdate=", paste(sm, sd, sy, sep = "+"),
                               "&enddate=", paste(em, ed, ey, sep = "+"),
                               "&output=csv")
  
   return(stock_url)
}
google_stocks <- function(sym, current = TRUE, sy = 2005, sm = 1, sd = 1, ey, em, ed) {
  # sy, sm, sd: start year, start month and start day
  # ey, em, ed: end year, end month, and end day

  # If TRUE, use the date as the enddate
  if(current){
    system_time <- as.character(Sys.time())
    ey <- as.numeric(substr(system_time, start = 1, stop = 4))
    em <- as.numeric(substr(system_time, start = 6, stop = 7))
    ed <- as.numeric(substr(system_time, start = 9, stop = 10))
  }
  stockurl <- build_url(sym, sy, sm , sd , ey, em, ed)
  require(readr)
  query_result <- tryCatch(
    read_csv(stockurl),
    error = function(e) NULL
  )
  return(query_result)
}

```

## I want to read data from VW, Tesla, Daimler IBM and Apple

```{r,  message=FALSE, warning=FALSE }
apple_data = google_stocks('AAPL', sy = 2010)
vw_data = google_stocks('SWX%3AVW', sy = 2010)
tesla_data = google_stocks('tsla', sy = 2010)
dai_data = google_stocks('DAI', sy = 2010)
ibm_data = google_stocks('ibm', sy = 2010)
```

## Label the data and combine them to one big data.frame
```{r}
apple_label <- rep("Apple", length(apple_data$Date))
apple_data$label <- apple_label

vw_label <- rep("VW", length(vw_data$Date))
vw_data$label <- vw_label

daimler_label <- rep("Daimler", length(dai_data$Date))
dai_data$label <- daimler_label

tesla_label <- rep("Tesla", length(tesla_data$Date))
tesla_data$label <- tesla_label

ibm_label <- rep("IBM", length(ibm_data$Date))
ibm_data$label <- ibm_label

shares_dac <- rbind(vw_data, dai_data, apple_data, ibm_data, tesla_data)
shares_dac$datum <- as.factor(shares_dac$Date)

shares_dax_order <- shares_dac[with(shares_dac, order(datum)),]
shares_dax_order$YM <- format(dmy(shares_dax_order$Date), "%Y-%m")

```
## Save the data for further processing
``` {r}
save(shares_dax_order, file="shares_dax_order.rda")
load("shares_dax_order.rda")

```
## And do the Plots

```{r stockmarket, echo=FALSE}

p <- ggplot(shares_dax_order, aes(YM, Close, fill=label)) 
p <- p + geom_boxplot() 
p <- p + theme(axis.text.x = element_text(angle=45))
p <- p + theme(legend.position="bottom")
p <- p + scale_x_discrete(name ="Zeitraum")
p <- p + scale_y_discrete(name ="Schlusskurs")
p

```

##  and save the plot as well
```{r}
ggsave("shares_order.png", width = 30, height = 20, units = "cm")

```

## Interactive graph
Using plotly

```{r}
ggplotly(p, tooltip = c("Stock"))
```