The underlying 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_ma(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
ma_fun = SMA,
n = 20,
wilder = FALSE,
ratio = NULL,
v = 1,
wts = 1:n,
...
)
geom_ma_(
mapping = NULL,
data = NULL,
position = "identity",
na.rm = TRUE,
show.legend = NA,
inherit.aes = TRUE,
ma_fun = "SMA",
n = 20,
wilder = FALSE,
ratio = NULL,
v = 1,
wts = 1:n,
...
)
Set of aesthetic mappings created by ggplot2::aes()
or
ggplot2::aes_()
. If specified and inherit.aes = TRUE
(the
default), it is combined with the default mapping at the top level of the
plot. You must supply mapping
if there is no plot mapping.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot2::ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
ggplot2::fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame.
, and
will be used as the layer data.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
If TRUE
, silently removes NA
values, which
typically desired for moving averages.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If FALSE
, overrides the default aesthetics,
rather than combining with them. This is most useful for helper functions
that define both data and aesthetics and shouldn't inherit behaviour from
the default plot specification, e.g. ggplot2::borders()
.
The function used to calculate the moving average. Seven options are
available including: SMA, EMA, WMA, DEMA, ZLEMA, VWMA, and EVWMA. The default is
SMA
. See TTR::SMA()
for underlying functions.
Number of periods to average over. Must be between 1 and
nrow(x)
, inclusive.
logical; if TRUE
, a Welles Wilder type EMA will be
calculated; see notes.
A smoothing/decay ratio. ratio
overrides wilder
in EMA
, and provides additional smoothing in VMA
.
The 'volume factor' (a number in [0,1]). See Notes.
Vector of weights. Length of wts
vector must equal the
length of x
, or n
(the default).
Other arguments passed on to ggplot2::layer()
. These are
often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"
or size = 3
. They may also be parameters
to the paired geom/stat.
The following aesthetics are understood (required are in bold):
x
y
volume
, Required for VWMA and EVWMA
alpha
colour
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
# Load libraries
library(tidyquant)
library(dplyr)
library(ggplot2)
AAPL <- tq_get("AAPL", from = "2013-01-01", to = "2016-12-31")
# SMA
AAPL %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line() + # Plot stock price
geom_ma(ma_fun = SMA, n = 50) + # Plot 50-day SMA
geom_ma(ma_fun = SMA, n = 200, color = "red") + # Plot 200-day SMA
coord_x_date(xlim = c("2016-01-01", "2016-12-31"),
ylim = c(75, 125)) # Zoom in
# EVWMA
AAPL %>%
ggplot(aes(x = date, y = adjusted)) +
geom_line() + # Plot stock price
geom_ma(aes(volume = volume), ma_fun = EVWMA, n = 50) + # Plot 50-day EVWMA
coord_x_date(xlim = c("2016-01-01", "2016-12-31"),
ylim = c(75, 125)) # Zoom in