Visualizaing Financial Data

In-class Exercise

Lesson 10: In-class Exercise

Leslie Long Nu https://www.linkedin.com/in/leslielongnu/ (SMU, MITB)https://scis.smu.edu.sg/master-it-business
2022-06-25

Import Libraries

For the purpose of this hands-on exercise, the following R packages will be used.

show 
packages = c('tidyverse', 'lubridate', 'tidyquant', 'ggHoriPlot',
             'timetk', 'ggthemes', 'plotly')
for (p in packages){
  if(!require(p, character.only = T)){
    install.packages(p)
  }
  library(p,character.only = T)
}

Data Extraction with tidyquant

tidyquant integrates resources for collecting and analyzing financial data with the tidy data infrastructure of the tidyverse, allowing for seamless interaction between each.

Step 1

We will import a pre-prepared company list called companySG.csv onto R. The list consists of top 45 companies by market capitalization in Singapore. However, we just want the top 40.

show 
company <- read_csv("data/companySG.csv")
Top40 <- company %>% 
  slice_max(`marketcap`, n=40) %>% 
  select(symbol)

Step 2

tq_get() method will be used to extract daily values of these stocks from Yahoo Finance via APIs. The time period for the data was set from 1st January 2020 to 31st March 2021. The data are specified to be returned in daily intervals.

show 
Stock40_daily <- Top40 %>%
  tq_get(get = "stock.prices", 
         from = "2020-01-01", 
         to = "2022-03-31") %>%
  group_by(symbol) %>%
  tq_transmute(select = NULL, 
               mutate_fun = to.period, 
               period  = "days")

Visualizations

Horizon Graph

The following code plots a horizon graph using geom_horizon.

show 
Stock40_daily %>% 
  ggplot() +
  geom_horizon(aes(x = date, y=adjusted), origin = "midpoint", horizonscale = 6)+
  facet_grid(symbol~.)+
  theme_few() +
  scale_fill_hcl(palette = 'RdBu') +
  theme(panel.spacing.y=unit(0, "lines"), 
    strip.text.y = element_text(size = 10, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=10),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank(),
    plot.title = element_text(size= 22)
    ) +
  scale_x_date(expand=c(0,0), date_breaks = "1 month", date_labels = "%b%y") +
  ggtitle('Daily Adjusted Prices (Jan 2020 to Mar 2022)')

Horizon Graph Makeover

left_join() of dplyr package is used to append fields from company data.frame onto Stock_daily data.frame. Next select() is used to select columns 1 to 8 and 11 to 12.

show 
Stock40_daily <- Stock40_daily %>%
  left_join(company) %>%
  select(1:8, 11:12)

geom_vline() is used to add the vertical reference lines.

show 
Stock40_daily %>% 
  ggplot() +
  geom_horizon(aes(x = date, y=adjusted), origin = "midpoint", horizonscale = 6)+
  facet_grid(Name~.)+
  geom_vline(xintercept = as.Date("2020-03-11"), colour = "grey15", linetype = "dashed", size = 0.5)+
  geom_vline(xintercept = as.Date("2020-12-14"), colour = "grey15", linetype = "dashed", size = 0.5)+
  theme_few() +
  scale_fill_hcl(palette = 'RdBu') +
  theme(panel.spacing.y=unit(0, "lines"),
        strip.text.y = element_text(size = 10, angle = 0, hjust = 0),
    legend.position = 'none',
    axis.text.y = element_blank(),
    axis.text.x = element_text(size=8),
    axis.title.y = element_blank(),
    axis.title.x = element_blank(),
    axis.ticks.y = element_blank(),
    panel.border = element_blank(),
    plot.title = element_text(size= 22)
    ) +
  scale_x_date(expand=c(0,0), date_breaks = "1 month", date_labels = "%b%y") +
  ggtitle('Daily Adjusted Prices (Jan 2020 to Mar 2022)')

Interactive Stock Price Line Graphs

Step 1

Select the four stocks of interest.

show 
selected_stocks <-  Stock40_daily %>%
  filter (`symbol` == c("C09.SI", "SINGF", "SNGNF", "C52.SI"))

Step 2 Plotting line graphs using ggplot2 and ggplotly().

show 
p <- ggplot(selected_stocks, aes(x = date, y = adjusted))+
        scale_y_continuous() +
        geom_line() +
        facet_wrap(~Name, scales = "free_y",) +
        theme_tq() +
        labs(title = "Daily stock prices of selected weak stocks", x = "", y = "Adjusted Price") +
        theme(axis.text.x = element_text(size = 6), axis.text.y = element_text(size = 6))
ggplotly(p)

Candlestick Charts

Candlestick Chart: tidyquant method

Before plotting the candlesticks, the code chunk below will be used to define the end data parameter. It will be used when setting date limits throughout the examples.

show 
end <- as_date("2022-03-31")

The following code chunk plots the candlestick plot.

show 
Stock40_daily %>%
  filter(symbol == "DBSDF") %>%
  ggplot(aes(
    x = date, y = close)) +
  geom_candlestick(aes(
    open = open, high = high, 
    low = low, close = close)) +
  geom_line(size = 0.5)+
    coord_x_date(xlim = c(end - weeks(12), 
                          end),
                 ylim = c(20, 35),
                 expand = TRUE) +
  labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price", 
       y = "Closing Price", x = "") + 
  theme_tq()

Candlestick Chart with Moving Average Lines

geom_MA is used to add the moving average line. It is a wrapper function of SMA() from the TTR package.

show 
Stock40_daily %>%
  filter(symbol == "DBSDF") %>%
  ggplot(aes(
    x = date, y = close)) +
  geom_candlestick(aes(
    open = open, high = high, 
    low = low, close = close)) +
  geom_line(size = 0.5)+
  geom_ma(color = "darkgreen") +
  geom_ma(color = "lightgreen", n = 5) + 
    coord_x_date(xlim = c(end - weeks(12), 
                          end),
                 ylim = c(20, 35),
                 expand = TRUE) +
  labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
       subtitle = "lightgreen = 5-day MA, darkgreen = 20-day MA",
       y = "Closing Price", x = "") + 
  theme_tq()

Candlestick Chart: Interactive

Step 1

A candleStick_plot() function is written as follows:

show 
candleStick_plot<-function(symbol, from, to){
  tq_get(symbol, from = from, to = to, warnings = FALSE) %>% 
    mutate(greenRed=ifelse(open-close>0, "Red", "Green")) %>% 
    ggplot()+
    geom_segment(aes(x = date, xend=date, y =open, yend =close, colour=greenRed), size=3)+
    theme_tq()+
    geom_segment(aes(x = date, xend=date, y =high, yend =low, colour=greenRed))+
    scale_color_manual(values=c("ForestGreen","Red"))+
    ggtitle(paste0(symbol," (",from," - ",to,")"))+
    theme(legend.position ="none",
          axis.title.y = element_blank(),
          axis.title.x=element_blank(),
          axis.text.x = element_text(angle = 0, vjust = 0.5, hjust=1),
          plot.title= element_text(hjust=0.5))
}

Step 2

Using the function to specify symbol name and time duration, an interactive candlestick chart is created.

show 
p <- candleStick_plot("DBSDF",
                      from = '2022-01-01',
                      to = today())
ggplotly(p)

Bollinger Bands Plot

The following code chunk is used to plot bollinger bands. geom_bbands() plots a range around a moving average, typically two standard deviations up and down. The moving average functions used are specified in SMA() from the TTR package.

show 
Stock40_daily %>%
  filter(symbol == "DBSDF") %>% 
  ggplot(aes(x=date, y=close))+
  geom_line(size=0.25)+
  geom_bbands(aes(
    high = high, low = low, close = close), 
    ma_fun = SMA, sd = 2, n = 20,
    size = 0.8, color_ma = "royalblue4", 
    color_bands = "#ff7f50")+
    coord_x_date(xlim = c("2020-02-01", 
                          "2022-03-31"), 
                 expand = TRUE)+
    labs(title = "DBS Group Holdings Ltd (DBSDF) Daily Stock Price",
         subtitle = "dotted orange lines = bollinger bands",
         x = "Date", y ="Price") +
theme(legend.position="none")