R/ggplot-geom_bbands.R
In business-science/tidyquant: Tidy Quantitative Financial Analysis
Defines functions
get_bbands
geom_bbands_
geom_bbands
Documented in
geom_bbands
geom_bbands_
#' Plot Bollinger Bands using Moving Averages
#'
#' 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:
#' \itemize{
#' \item \strong{\href{https://www.investopedia.com/terms/s/sma.asp}{Simple moving averages (SMA)}}:
#' Rolling mean over a period defined by `n`.
#' \item \strong{\href{https://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{https://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{https://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{https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp}{Volume-weighted moving averages (VWMA)}}:
#' Requires `volume` aesthetic.
#' \item \strong{\href{https://www.motivewave.com/studies/elastic_volume_weighted_moving_average.htm}{Elastic, volume-weighted moving averages (EVWMA)}}:
#' Requires `volume` aesthetic.
#' }
#'
#' @inheritParams geom_ma
#' @inheritParams ggplot2::geom_ribbon
#' @inheritParams TTR::SMA
#' @inheritParams TTR::BBands
#' @param color_ma,color_bands Select the line color to be applied for the moving
#' average line and the Bollinger band line.
#' @param alpha Used to adjust the alpha transparency for the BBand ribbon.
#' @param fill Used to adjust the fill color for the BBand ribbon.
#'
#' @section Aesthetics:
#' The following aesthetics are understood (required are in bold):
#' \itemize{
#' \item \strong{`x`}, Typically a date
#' \item \strong{`high`}, Required to be the high price
#' \item \strong{`low`}, Required to be the low price
#' \item \strong{`close`}, Required to be the close price
#' \item `volume`, Required for VWMA and EVWMA
#' \item `colour`, Affects line colors
#' \item `fill`, Affects ribbon fill color
#' \item `alpha`, Affects ribbon alpha value
#' \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_bbands
#'
#' @export
#'
#' @examples
#' 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))
#' @rdname geom_bbands
#' @export
geom_bbands <- function(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", ...) {
ma_fun <- deparse(substitute(ma_fun))
geom_bbands_(mapping = mapping, data = data,
position = position, na.rm = na.rm, show.legend = show.legend,
inherit.aes = inherit.aes,
ma_fun = ma_fun, n = n, sd = sd,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color_ma = color_ma, color_bands = color_bands,
alpha = alpha, fill = fill, ...)
}
#' @rdname geom_bbands
#' @export
geom_bbands_ <- function(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", ...) {
# Check ma_fun is valid
# Toggle if volume based
if (ma_fun == "VWMA" || ma_fun == "EVWMA") {
stat_ribbon <- StatBBandsRibbon_vol
stat_ma <- StatBBandsMA_vol
} else {
stat_ribbon <- StatBBandsRibbon
stat_ma <- StatBBandsMA
}
ribbon <- ggplot2::layer(
stat = stat_ribbon, geom = GeomBBandsRibbon, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(n = n, ma_fun = ma_fun, sd = sd, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color = color_bands, alpha = alpha, fill = fill, ...)
)
ma <- ggplot2::layer(
stat = stat_ma, geom = GeomBBandsMA, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(n = n, ma_fun = ma_fun, sd = sd, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color = color_ma, alpha = NA, fill = fill, ...)
)
list(ribbon, ma)
}
StatBBandsRibbon <- ggplot2::ggproto("StatBBandsRibbon", ggplot2::Stat,
required_aes = c("x", "high", "low", "close"),
dropped_aes = c("high", "low", "close", "y"),
compute_group = function(data, scales, params,
n = 10, ma_fun = SMA, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- get_bbands(HLC, n, ma_fun,
sd, wilder, ratio,
v, wts)
grid$ymin <- bbands[,"dn"]
grid$ymax <- bbands[,"up"]
grid
}
)
StatBBandsMA <- ggplot2::ggproto("StatBBandsMA", ggplot2::Stat,
required_aes = c("x", "high", "low", "close"),
dropped_aes = c("high", "low", "close", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 20, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n,
fill = "grey20") {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- get_bbands(HLC, n, ma_fun,
sd, wilder, ratio,
v, wts)
grid$y <- bbands[,"mavg"]
grid
}
)
StatBBandsRibbon_vol <- ggplot2::ggproto("StatBBandsRibbon", ggplot2::Stat,
required_aes = c("x", "high", "low", "close", "volume"),
dropped_aes = c("high", "low", "close", "volume", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 10, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
volume = data$volume,
sd = sd)
grid$ymin <- bbands[,"dn"]
grid$ymax <- bbands[,"up"]
grid
}
)
StatBBandsMA_vol <- ggplot2::ggproto("StatBBandsMA", ggplot2::Stat,
required_aes = c("x", "high", "low", "close", "volume"),
dropped_aes = c("high", "low", "close", "volume", "y"),
compute_group = function(data, scales, params,
n = 20, ma_fun = SMA, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n,
fill = "grey20") {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
volume = data$volume,
sd = sd)
grid$y <- bbands[,"mavg"]
grid
}
)
# Geoms ----
GeomBBandsRibbon <- ggplot2::ggproto("GeomBBandsRibbon", ggplot2::GeomRibbon,
default_aes = ggplot2::aes(colour = "red",
fill = "grey20",
linewidth = 0.5,
linetype = 2,
alpha = 0.15)
)
GeomBBandsMA <- ggplot2::ggproto("GeomBBandsMA", ggplot2::GeomLine,
default_aes = ggplot2::aes(colour = "darkblue",
linetype = 2,
linewidth = 0.5,
alpha = NA)
)
# Get BBands -----
get_bbands <- function(HLC, n, ma_fun, sd, wilder, ratio, v, wts) {
if (ma_fun == "SMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd)
} else if (ma_fun == "EMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wilder = wilder,
ratio = ratio)
} else if (ma_fun == "DEMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wilder = wilder,
ratio = ratio,
v = v)
} else if (ma_fun == "WMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wts = wts)
} else if (ma_fun == "ZLEMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
ratio = ratio)
} else {
stop(paste0("Unsupported ma_fun: ", ma_fun))
}
return(bbands)
}
business-science/tidyquant documentation built on Feb. 2, 2024, 2:50 a.m.
R Package Documentation
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Defines functions get_bbands geom_bbands_ geom_bbands
Documented in geom_bbands geom_bbands_
#' Plot Bollinger Bands using Moving Averages
#'
#' 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:
#' \itemize{
#' \item \strong{\href{https://www.investopedia.com/terms/s/sma.asp}{Simple moving averages (SMA)}}:
#' Rolling mean over a period defined by `n`.
#' \item \strong{\href{https://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{https://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{https://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{https://www.investopedia.com/articles/trading/11/trading-with-vwap-mvwap.asp}{Volume-weighted moving averages (VWMA)}}:
#' Requires `volume` aesthetic.
#' \item \strong{\href{https://www.motivewave.com/studies/elastic_volume_weighted_moving_average.htm}{Elastic, volume-weighted moving averages (EVWMA)}}:
#' Requires `volume` aesthetic.
#' }
#'
#' @inheritParams geom_ma
#' @inheritParams ggplot2::geom_ribbon
#' @inheritParams TTR::SMA
#' @inheritParams TTR::BBands
#' @param color_ma,color_bands Select the line color to be applied for the moving
#' average line and the Bollinger band line.
#' @param alpha Used to adjust the alpha transparency for the BBand ribbon.
#' @param fill Used to adjust the fill color for the BBand ribbon.
#'
#' @section Aesthetics:
#' The following aesthetics are understood (required are in bold):
#' \itemize{
#' \item \strong{`x`}, Typically a date
#' \item \strong{`high`}, Required to be the high price
#' \item \strong{`low`}, Required to be the low price
#' \item \strong{`close`}, Required to be the close price
#' \item `volume`, Required for VWMA and EVWMA
#' \item `colour`, Affects line colors
#' \item `fill`, Affects ribbon fill color
#' \item `alpha`, Affects ribbon alpha value
#' \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_bbands
#'
#' @export
#'
#' @examples
#' 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))
#' @rdname geom_bbands
#' @export
geom_bbands <- function(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", ...) {
ma_fun <- deparse(substitute(ma_fun))
geom_bbands_(mapping = mapping, data = data,
position = position, na.rm = na.rm, show.legend = show.legend,
inherit.aes = inherit.aes,
ma_fun = ma_fun, n = n, sd = sd,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color_ma = color_ma, color_bands = color_bands,
alpha = alpha, fill = fill, ...)
}
#' @rdname geom_bbands
#' @export
geom_bbands_ <- function(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", ...) {
# Check ma_fun is valid
# Toggle if volume based
if (ma_fun == "VWMA" || ma_fun == "EVWMA") {
stat_ribbon <- StatBBandsRibbon_vol
stat_ma <- StatBBandsMA_vol
} else {
stat_ribbon <- StatBBandsRibbon
stat_ma <- StatBBandsMA
}
ribbon <- ggplot2::layer(
stat = stat_ribbon, geom = GeomBBandsRibbon, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(n = n, ma_fun = ma_fun, sd = sd, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color = color_bands, alpha = alpha, fill = fill, ...)
)
ma <- ggplot2::layer(
stat = stat_ma, geom = GeomBBandsMA, data = data, mapping = mapping,
position = position, show.legend = show.legend, inherit.aes = inherit.aes,
params = list(n = n, ma_fun = ma_fun, sd = sd, na.rm = na.rm,
wilder = wilder, ratio = ratio, v = v, wts = wts,
color = color_ma, alpha = NA, fill = fill, ...)
)
list(ribbon, ma)
}
StatBBandsRibbon <- ggplot2::ggproto("StatBBandsRibbon", ggplot2::Stat,
required_aes = c("x", "high", "low", "close"),
dropped_aes = c("high", "low", "close", "y"),
compute_group = function(data, scales, params,
n = 10, ma_fun = SMA, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- get_bbands(HLC, n, ma_fun,
sd, wilder, ratio,
v, wts)
grid$ymin <- bbands[,"dn"]
grid$ymax <- bbands[,"up"]
grid
}
)
StatBBandsMA <- ggplot2::ggproto("StatBBandsMA", ggplot2::Stat,
required_aes = c("x", "high", "low", "close"),
dropped_aes = c("high", "low", "close", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 20, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n,
fill = "grey20") {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- get_bbands(HLC, n, ma_fun,
sd, wilder, ratio,
v, wts)
grid$y <- bbands[,"mavg"]
grid
}
)
StatBBandsRibbon_vol <- ggplot2::ggproto("StatBBandsRibbon", ggplot2::Stat,
required_aes = c("x", "high", "low", "close", "volume"),
dropped_aes = c("high", "low", "close", "volume", "y"),
compute_group = function(data, scales, params,
ma_fun = "SMA", n = 10, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n) {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
volume = data$volume,
sd = sd)
grid$ymin <- bbands[,"dn"]
grid$ymax <- bbands[,"up"]
grid
}
)
StatBBandsMA_vol <- ggplot2::ggproto("StatBBandsMA", ggplot2::Stat,
required_aes = c("x", "high", "low", "close", "volume"),
dropped_aes = c("high", "low", "close", "volume", "y"),
compute_group = function(data, scales, params,
n = 20, ma_fun = SMA, sd = 2,
wilder = FALSE, ratio = NULL,
v = 1, wts = 1:n,
fill = "grey20") {
grid <- tibble::tibble(x = data$x)
HLC <- tibble::tibble(high = data$high,
low = data$low,
close = data$close)
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
volume = data$volume,
sd = sd)
grid$y <- bbands[,"mavg"]
grid
}
)
# Geoms ----
GeomBBandsRibbon <- ggplot2::ggproto("GeomBBandsRibbon", ggplot2::GeomRibbon,
default_aes = ggplot2::aes(colour = "red",
fill = "grey20",
linewidth = 0.5,
linetype = 2,
alpha = 0.15)
)
GeomBBandsMA <- ggplot2::ggproto("GeomBBandsMA", ggplot2::GeomLine,
default_aes = ggplot2::aes(colour = "darkblue",
linetype = 2,
linewidth = 0.5,
alpha = NA)
)
# Get BBands -----
get_bbands <- function(HLC, n, ma_fun, sd, wilder, ratio, v, wts) {
if (ma_fun == "SMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd)
} else if (ma_fun == "EMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wilder = wilder,
ratio = ratio)
} else if (ma_fun == "DEMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wilder = wilder,
ratio = ratio,
v = v)
} else if (ma_fun == "WMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
wts = wts)
} else if (ma_fun == "ZLEMA") {
bbands <- TTR::BBands(HLC = HLC,
n = n,
ma_fun = eval(parse(text = ma_fun)),
sd = sd,
ratio = ratio)
} else {
stop(paste0("Unsupported ma_fun: ", ma_fun))
}
return(bbands)
}
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