Chart Types
Financial charts provide visual cues to open, high, low, and close prices. The following chart geoms are available:
- Bar
Chart: Use
geom_barchart - Candlestick
Chart: Use
geom_candlestick
Line Chart
Before we visualize bar charts and candlestick charts using the
tidyquant geoms, let’s visualize stock prices with a simple
line chart to get a sense of the “grammar of graphics” workflow. This is
done using the geom_line from the ggplot2
package. The workflow begins with the stock data, and uses the pipe
operator (%>%) to send to the ggplot()
function.
The primary features controlling the chart are the aesthetic
arguments: these are used to add data to the chart by way of the
aes() function. When added inside the ggplot()
function, the aesthetic arguments are available to all underlying
layers. Alternatively, the aesthetic arguments can be applied to each
geom individually, but typically this is minimized in practice because
it duplicates code. We set aesthetic arguments, x = date
and y = close, to chart the closing price versus date. The
geom_line() function inherits the aesthetic arguments from
the ggplot() function and produces a line on the chart.
Labels are added separately using the labs() function.
Thus, the chart is built from the ground up by starting with data and
progressively adding geoms, labels, coordinates / scales and other
attributes to create a the final chart. This is enables maximum
flexibility wherein the analyst can create very complex charts using the
“grammar of graphics”.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_line() +
labs(title = "AAPL Line Chart", y = "Closing Price", x = "") +
theme_tq()
Bar Chart
Visualizing the bar chart is as simple as replacing
geom_line with geom_barchart in the ggplot
workflow. Because the bar chart uses open, high, low, and close prices
in the visualization, we need to specify these as part of the aesthetic
arguments, aes(). We can do so internal to the geom or in
the ggplot() function.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_barchart(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Bar Chart", y = "Closing Price", x = "") +
theme_tq()
We zoom into specific sections using coord_x_date, which
has xlim and ylim arguments specified as
c(start, end) to focus on a specific region of the chart.
For xlim, we’ll use lubridate to convert a
character date to date class, and then subtract six weeks using the
weeks() function. For ylim we zoom in on
prices in the range from 100 to 120.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_barchart(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Bar Chart",
subtitle = "Zoomed in using coord_x_date",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(end - weeks(6), end),
ylim = c(aapl_range_60_tbl$min_low, aapl_range_60_tbl$max_high)) +
theme_tq()
The colors can be modified using colour_up and
colour_down arguments, and parameters such as
size can be used to control the appearance.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_barchart(aes(open = open, high = high, low = low, close = close),
colour_up = "darkgreen", colour_down = "darkred", size = 1) +
labs(title = "AAPL Bar Chart",
subtitle = "Zoomed in, Experimenting with Formatting",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(end - weeks(6), end),
c(aapl_range_60_tbl$min_low, aapl_range_60_tbl$max_high)) +
theme_tq()
Candlestick Chart
Creating a candlestick chart is very similar to the process with the
bar chart. Using geom_candlestick, we can insert into the
ggplot workflow.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Candlestick Chart", y = "Closing Price", x = "") +
theme_tq()
We zoom into specific sections using coord_x_date.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
labs(title = "AAPL Candlestick Chart",
subtitle = "Zoomed in using coord_x_date",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(end - weeks(6), end),
c(aapl_range_60_tbl$min_low, aapl_range_60_tbl$max_high)) +
theme_tq()
The colors can be modified using colour_up and
colour_down, which control the line color, and
fill_up and fill_down, which control the
rectangle fills.
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close),
colour_up = "darkgreen", colour_down = "darkred",
fill_up = "darkgreen", fill_down = "darkred") +
labs(title = "AAPL Candlestick Chart",
subtitle = "Zoomed in, Experimenting with Formatting",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(end - weeks(6), end),
c(aapl_range_60_tbl$min_low, aapl_range_60_tbl$max_high)) +
theme_tq()
Charting Multiple Securities
We can use facet_wrap to visualize multiple stocks at
the same time. By adding a group aesthetic in the main
ggplot() function and combining with a
facet_wrap() function at the end of the ggplot
workflow, all four “FANG” stocks can be viewed simultaneously. You may
notice an odd filter() call before the call to
ggplot(). I’ll discuss this next.
start <- end - weeks(6)
FANG %>%
filter(date >= start - days(2 * 15)) %>%
ggplot(aes(x = date, y = close, group = symbol)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
labs(title = "FANG Candlestick Chart",
subtitle = "Experimenting with Mulitple Stocks",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(start, end)) +
facet_wrap(~ symbol, ncol = 2, scale = "free_y") +
theme_tq()
A note about out-of-bounds data (or “clipping”), which is particularly important with faceting and charting moving averages:
The coord_x_date coordinate function is designed to zoom
into specific sections of a chart without “clipping” data that is
outside of the view. This is in contrast to scale_x_date,
which removes out-of-bounds data from the charting. Under normal
circumstances clipping is not a big deal (and is actually helpful for
scaling the y-axis), but with financial applications users want to chart
rolling/moving averages, lags, etc that depend on data outside of the
view port. Because of this need for out-of-bounds data, there is a
trade-off when charting: Too much out-of-bounds data distorts
the scale of the y-axis, and too little and we cannot get a moving
average. The optimal method is to include “just enough”
out-of-bounds data to get the chart we want. This is why below the FANG
data is filtered by date from double the number of moving-average days
(2 * n) previous to the start date. This yields a nice
y-axis scale and still allows us to create a moving average line using
geom_ma.
start <- end - weeks(6)
FANG %>%
filter(date >= start - days(2 * 15)) %>%
ggplot(aes(x = date, y = close, group = symbol)) +
geom_candlestick(aes(open = open, high = high, low = low, close = close)) +
geom_ma(ma_fun = SMA, n = 15, color = "darkblue", size = 1) +
labs(title = "FANG Candlestick Chart",
subtitle = "Experimenting with Mulitple Stocks",
y = "Closing Price", x = "") +
coord_x_date(xlim = c(start, end)) +
facet_wrap(~ symbol, ncol = 2, scale = "free_y") +
theme_tq()
