tidyquant: geom_ma – R documentation

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geom_ma

Plot moving averages

Description

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.

Usage

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,
  ...
)

Arguments

`mapping`	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.
`data`	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`	Position adjustment, either as a string, or the result of a call to a position adjustment function.
`na.rm`	If `TRUE`, silently removes `NA` values, which typically desired for moving averages.
`show.legend`	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.
`inherit.aes`	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()`.
`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 `SMA`. See `TTR::SMA()` for underlying functions.
`n`	Number of periods to average over. Must be between 1 and `nrow(x)`, inclusive.
`wilder`	logical; if `TRUE`, a Welles Wilder type EMA will be calculated; see notes.
`ratio`	A smoothing/decay ratio. `ratio` overrides `wilder` in `EMA`, and provides additional smoothing in `VMA`.
`v`	The 'volume factor' (a number in [0,1]). See Notes.
`wts`	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.

Aesthetics

The following aesthetics are understood (required are in bold):

x
y
volume, Required for VWMA and EVWMA
alpha
colour
group
linetype
size

Examples

# 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

tidyquant

Tidy Quantitative Financial Analysis

v1.0.3

MIT + file LICENSE

Authors

Matt Dancho [aut, cre], Davis Vaughan [aut]

Initial release