View source: R/ggplot-geom_bbands.R
geom_bbands | R Documentation |
Bollinger Bands plot a range around a moving average typically two standard deviations up and down.
The geom_bbands()
function enables plotting Bollinger Bands quickly using various moving average functions.
The moving average functions used are specified in TTR::SMA()
from the TTR package. Use coord_x_date()
to zoom into specific plot regions.
The following moving averages are available:
Simple moving averages (SMA):
Rolling mean over a period defined by n
.
Exponential moving averages (EMA): Includes
exponentially-weighted mean that gives more weight to recent observations.
Uses wilder
and ratio
args.
Weighted moving averages (WMA):
Uses a set of weights, wts
, to weight observations in the moving average.
Double exponential moving averages (DEMA):
Uses v
volume factor, wilder
and ratio
args.
Zero-lag exponential moving averages (ZLEMA):
Uses wilder
and ratio
args.
Volume-weighted moving averages (VWMA):
Requires volume
aesthetic.
Elastic, volume-weighted moving averages (EVWMA):
Requires volume
aesthetic.
geom_bbands(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
ma_fun = SMA,
n = 20,
sd = 2,
wilder = FALSE,
ratio = NULL,
v = 1,
wts = 1:n,
color_ma = "darkblue",
color_bands = "red",
alpha = 0.15,
fill = "grey20",
...
)
geom_bbands_(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
ma_fun = "SMA",
n = 10,
sd = 2,
wilder = FALSE,
ratio = NULL,
v = 1,
wts = 1:n,
color_ma = "darkblue",
color_bands = "red",
alpha = 0.15,
fill = "grey20",
...
)
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
position |
A position adjustment to use on the data for this layer. This
can be used in various ways, including to prevent overplotting and
improving the display. The
|
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
ma_fun |
The function used to calculate the moving average. Seven options are
available including: SMA, EMA, WMA, DEMA, ZLEMA, VWMA, and EVWMA. The default is
|
n |
Number of periods to average over. Must be between 1 and
|
sd |
The number of standard deviations to use. |
wilder |
logical; if |
ratio |
A smoothing/decay ratio. |
v |
The 'volume factor' (a number in [0,1]). See Notes. |
wts |
Vector of weights. Length of |
color_ma , color_bands |
Select the line color to be applied for the moving average line and the Bollinger band line. |
alpha |
Used to adjust the alpha transparency for the BBand ribbon. |
fill |
Used to adjust the fill color for the BBand ribbon. |
... |
Other arguments passed on to |
The following aesthetics are understood (required are in bold):
x
, Typically a date
high
, Required to be the high price
low
, Required to be the low price
close
, Required to be the close price
volume
, Required for VWMA and EVWMA
colour
, Affects line colors
fill
, Affects ribbon fill color
alpha
, Affects ribbon alpha value
group
linetype
size
See individual modeling functions for underlying parameters:
TTR::SMA()
for simple moving averages
TTR::EMA()
for exponential moving averages
TTR::WMA()
for weighted moving averages
TTR::DEMA()
for double exponential moving averages
TTR::ZLEMA()
for zero-lag exponential moving averages
TTR::VWMA()
for volume-weighted moving averages
TTR::EVWMA()
for elastic, volume-weighted moving averages
coord_x_date()
for zooming into specific regions of a plot
library(dplyr)
library(ggplot2)
library(lubridate)
AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")
# SMA
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_line() + # Plot stock price
geom_bbands(aes(high = high, low = low, close = close), ma_fun = SMA, n = 50) +
coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")),
ylim = c(20, 35))
# EMA
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_line() + # Plot stock price
geom_bbands(aes(high = high, low = low, close = close),
ma_fun = EMA, wilder = TRUE, ratio = NULL, n = 50) +
coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")),
ylim = c(20, 35))
# VWMA
AAPL %>%
ggplot(aes(x = date, y = close)) +
geom_line() + # Plot stock price
geom_bbands(aes(high = high, low = low, close = close, volume = volume),
ma_fun = VWMA, n = 50) +
coord_x_date(xlim = c(as_date("2016-12-31") - dyears(1), as_date("2016-12-31")),
ylim = c(20, 35))
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