R/geom_ma.R
In phamdinhkhanh/VNDS: VNDS Quantitative VietNam Financial Analysis
#' Plot moving averages
#'
#' 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:
#' \itemize{
#' \item \strong{\href{http://www.investopedia.com/terms/s/sma.asp}{Simple moving averages (SMA)}}:
#' Rolling mean over a period defined by `n`.
#' \item \strong{\href{http://www.investopedia.com/terms/e/ema.asp}{Exponential moving averages (EMA)}}: Includes
#' exponentially-weighted mean that gives more weight to recent observations.
#' Uses `wilder` and `ratio` args.
#' \item \strong{\href{http://www.investopedia.com/ask/answers/071414/whats-difference-between-moving-average-and-weighted-moving-average.asp}{Weighted moving averages (WMA)}}:
#' Uses a set of weights, `wts`, to weight observations in the moving average.
#' \item \strong{\href{http://www.investopedia.com/articles/trading/10/double-exponential-moving-average.asp}{Double exponential moving averages (DEMA)}}:
#' Uses `v` volume factor, `wilder` and `ratio` args.
#' \item \strong{\href{https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average}{Zero-lag exponential moving averages (ZLEMA)}}:
#' Uses `wilder` and `ratio` args.
#' \item \strong{\href{http://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp}{Volume-weighted moving averages (VWMA)}}:
#' Requires `volume` aesthetic.
#' \item \strong{\href{http://www.motivewave.com/studies/elastic_volume_weighted_moving_average.htm}{Elastic, volume-weighted moving averages (EVWMA)}}:
#' Requires `volume` aesthetic.
#' }
#'
#'
#' @param 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.
#' @param 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.
#'
#' @param na.rm If `TRUE`, silently removes `NA` values, which
#' typically desired for moving averages.
#'
#' @param ... 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.
#'
#' @param 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()].
#'
#' @param 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.
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_line
#' @inheritParams TTR::SMA
#'
#'
#' @section Aesthetics:
#' The following aesthetics are understood (required are in bold):
#' \itemize{
#' \item \strong{`x`}
#' \item \strong{`y`}
#' \item `volume`, Required for VWMA and EVWMA
#' \item `alpha`
#' \item `colour`
#' \item `group`
#' \item `linetype`
#' \item `size`
#' }
#'
#'
#' @seealso See individual modeling functions for underlying parameters:
#' \itemize{
#' \item [TTR::SMA()] for simple moving averages
#' \item [TTR::EMA()] for exponential moving averages
#' \item [TTR::WMA()] for weighted moving averages
#' \item [TTR::DEMA()] for double exponential moving averages
#' \item [TTR::ZLEMA()] for zero-lag exponential moving averages
#' \item [TTR::VWMA()] for volume-weighted moving averages
#' \item [TTR::EVWMA()] for elastic, volume-weighted moving averages
#' \item [coord_x_date()] for zooming into specific regions of a plot
#' }
#'
#'
#' @name geom_ma
#'
#' @export
#'
#' @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
#' @rdname geom_ma
#' @export
geom_ma <- function(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, ...) {
ma_fun <- deparse(substitute(ma_fun))
geom_ma_(mapping = mapping, data = data,
position = position, na.rm = na.rm, show.legend = show.legend,
inherit.aes = inherit.aes,
ma_fun = ma_fun, n = n,
wilder = wilder, ratio = ratio, v = v, wts = wts, ...)
}
#' @rdname geom_ma
#' @export
geom_ma_ <- function(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, ...) {
# Toggle if volume based
if (ma_fun == "VWMA" || ma_fun == "EVWMA") {
stat_ma <- StatMA_vol
} else {
stat_ma <- StatMA
}
ggplot2::layer(
stat = stat_ma, geom = GeomMA, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(ma_fun = ma_fun, n = n, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = 1, wts = 1:n, ...)
)
}
StatMA <- ggplot2::ggproto("StatSMA", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 10, wilder = FALSE,
ratio = ratio, v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
grid$y <- get_ma(x = data$y,
ma_fun = ma_fun,
n = n,
wilder = wilder,
ratio = ratio,
v = v,
wts = wts)
grid
}
)
StatMA_vol <- ggplot2::ggproto("StatSMA_vol", Stat,
required_aes = c("x", "y", "volume"),
compute_group = function(data, scales, params,
ma_fun, n = 10,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
ma_fun <- eval(parse(text = paste0("TTR::", ma_fun)))
grid$y <- ma_fun(price = data$y,
volume = data$volume,
n = n)
grid
}
)
# Utility Functions -----
GeomMA <- ggproto("GeomMA", GeomLine,
default_aes = aes(colour = "darkblue",
linetype = 2,
size = 0.5,
alpha = NA)
)
get_ma <- function(ma_fun, x, n, wilder, ratio, v, wts) {
if (ma_fun == "SMA") {
ret <- TTR::SMA(x = x,
n = n)
} else if (ma_fun == "EMA") {
ret <- TTR::EMA(x = x,
n = n,
wilder = wilder,
ratio = ratio)
} else if (ma_fun == "DEMA") {
ret <- TTR::DEMA(x = x,
n = n,
wilder = wilder,
ratio = ratio,
v = v)
} else if (ma_fun == "WMA") {
ret <- TTR::WMA(x = x,
n = n,
wts = wts)
} else if (ma_fun == "ZLEMA") {
ret <- TTR::ZLEMA(x = x,
n = n,
ratio = ratio)
} else {
stop(paste0("Unsupported ma_fun: ", ma_fun))
}
return(ret)
}
phamdinhkhanh/VNDS documentation built on May 29, 2019, 2:51 p.m.
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#' Plot moving averages
#'
#' 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:
#' \itemize{
#' \item \strong{\href{http://www.investopedia.com/terms/s/sma.asp}{Simple moving averages (SMA)}}:
#' Rolling mean over a period defined by `n`.
#' \item \strong{\href{http://www.investopedia.com/terms/e/ema.asp}{Exponential moving averages (EMA)}}: Includes
#' exponentially-weighted mean that gives more weight to recent observations.
#' Uses `wilder` and `ratio` args.
#' \item \strong{\href{http://www.investopedia.com/ask/answers/071414/whats-difference-between-moving-average-and-weighted-moving-average.asp}{Weighted moving averages (WMA)}}:
#' Uses a set of weights, `wts`, to weight observations in the moving average.
#' \item \strong{\href{http://www.investopedia.com/articles/trading/10/double-exponential-moving-average.asp}{Double exponential moving averages (DEMA)}}:
#' Uses `v` volume factor, `wilder` and `ratio` args.
#' \item \strong{\href{https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average}{Zero-lag exponential moving averages (ZLEMA)}}:
#' Uses `wilder` and `ratio` args.
#' \item \strong{\href{http://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp}{Volume-weighted moving averages (VWMA)}}:
#' Requires `volume` aesthetic.
#' \item \strong{\href{http://www.motivewave.com/studies/elastic_volume_weighted_moving_average.htm}{Elastic, volume-weighted moving averages (EVWMA)}}:
#' Requires `volume` aesthetic.
#' }
#'
#'
#' @param 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.
#' @param 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.
#'
#' @param na.rm If `TRUE`, silently removes `NA` values, which
#' typically desired for moving averages.
#'
#' @param ... 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.
#'
#' @param 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()].
#'
#' @param 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.
#'
#' @inheritParams ggplot2::layer
#' @inheritParams ggplot2::geom_line
#' @inheritParams TTR::SMA
#'
#'
#' @section Aesthetics:
#' The following aesthetics are understood (required are in bold):
#' \itemize{
#' \item \strong{`x`}
#' \item \strong{`y`}
#' \item `volume`, Required for VWMA and EVWMA
#' \item `alpha`
#' \item `colour`
#' \item `group`
#' \item `linetype`
#' \item `size`
#' }
#'
#'
#' @seealso See individual modeling functions for underlying parameters:
#' \itemize{
#' \item [TTR::SMA()] for simple moving averages
#' \item [TTR::EMA()] for exponential moving averages
#' \item [TTR::WMA()] for weighted moving averages
#' \item [TTR::DEMA()] for double exponential moving averages
#' \item [TTR::ZLEMA()] for zero-lag exponential moving averages
#' \item [TTR::VWMA()] for volume-weighted moving averages
#' \item [TTR::EVWMA()] for elastic, volume-weighted moving averages
#' \item [coord_x_date()] for zooming into specific regions of a plot
#' }
#'
#'
#' @name geom_ma
#'
#' @export
#'
#' @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
#' @rdname geom_ma
#' @export
geom_ma <- function(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, ...) {
ma_fun <- deparse(substitute(ma_fun))
geom_ma_(mapping = mapping, data = data,
position = position, na.rm = na.rm, show.legend = show.legend,
inherit.aes = inherit.aes,
ma_fun = ma_fun, n = n,
wilder = wilder, ratio = ratio, v = v, wts = wts, ...)
}
#' @rdname geom_ma
#' @export
geom_ma_ <- function(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, ...) {
# Toggle if volume based
if (ma_fun == "VWMA" || ma_fun == "EVWMA") {
stat_ma <- StatMA_vol
} else {
stat_ma <- StatMA
}
ggplot2::layer(
stat = stat_ma, geom = GeomMA, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(ma_fun = ma_fun, n = n, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = 1, wts = 1:n, ...)
)
}
StatMA <- ggplot2::ggproto("StatSMA", Stat,
required_aes = c("x", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 10, wilder = FALSE,
ratio = ratio, v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
grid$y <- get_ma(x = data$y,
ma_fun = ma_fun,
n = n,
wilder = wilder,
ratio = ratio,
v = v,
wts = wts)
grid
}
)
StatMA_vol <- ggplot2::ggproto("StatSMA_vol", Stat,
required_aes = c("x", "y", "volume"),
compute_group = function(data, scales, params,
ma_fun, n = 10,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
ma_fun <- eval(parse(text = paste0("TTR::", ma_fun)))
grid$y <- ma_fun(price = data$y,
volume = data$volume,
n = n)
grid
}
)
# Utility Functions -----
GeomMA <- ggproto("GeomMA", GeomLine,
default_aes = aes(colour = "darkblue",
linetype = 2,
size = 0.5,
alpha = NA)
)
get_ma <- function(ma_fun, x, n, wilder, ratio, v, wts) {
if (ma_fun == "SMA") {
ret <- TTR::SMA(x = x,
n = n)
} else if (ma_fun == "EMA") {
ret <- TTR::EMA(x = x,
n = n,
wilder = wilder,
ratio = ratio)
} else if (ma_fun == "DEMA") {
ret <- TTR::DEMA(x = x,
n = n,
wilder = wilder,
ratio = ratio,
v = v)
} else if (ma_fun == "WMA") {
ret <- TTR::WMA(x = x,
n = n,
wts = wts)
} else if (ma_fun == "ZLEMA") {
ret <- TTR::ZLEMA(x = x,
n = n,
ratio = ratio)
} else {
stop(paste0("Unsupported ma_fun: ", ma_fun))
}
return(ret)
}
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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