tidyquant (version 0.3.0)

geom_ma: Plot moving averages

Description

The underlying moving average functions used are specified in 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 aes or 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 ggplot.

A data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See 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.

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. 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 SMA for underlying functions.

n

Number of periods to average over.

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 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

See Also

See individual modeling functions for underlying parameters:

• SMA for simple moving averages
• EMA for exponential moving averages
• WMA for weighted moving averages
• DEMA for double exponential moving averages
• ZLEMA for zero-lag exponential moving averages
• VWMA for volume-weighted moving averages
• EVWMA for elastic, volume-weighted moving averages
• coord_x_date for zooming into specific regions of a plot

Examples

# Load libraries
library(tidyquant)

AAPL <- tq_get("AAPL")

# 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(today() - weeks(12), today()),
               ylim = c(100, 130))                     # 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(today() - weeks(12), today()),
               ylim = c(100, 130))                                  # Zoom in