#' Plot One Performance Metric vs. Another for 2-Fund Portfolios
#'
#' Useful for visualizing the behavior of 2-fund portfolios, e.g. by plotting
#' a measure of growth vs. a measure of volatility.
#'
#'
#' @param metrics Data frame with Fund column and column for each metric you
#' want to plot. Typically the result of a prior call to
#' \code{\link{calc_metrics_2funds}}.
#' @param formula Formula specifying what to plot, e.g. \code{mean ~ sd},
#' \code{cagr ~ mdd}, or \code{sharpe ~ allocation}. See \code{?calc_metrics}
#' for list of metrics to choose from (\code{"allocation"} is an extra option
#' here). If you specify \code{metrics}, default behavior is to use
#' \code{mean ~ sd} unless either is not available, in which case the first two
#' performance metrics that appear as columns in \code{metrics} are used.
#' @param tickers Character vector of ticker symbols, where the first two are
#' are a two-fund pair, the next two are another, and so on.
#' @param points Numeric vector specifying allocations to include as points on
#' the curve. Set to \code{NULL} for none (0 and 100 will still be included).
#' @param ... Arguments to pass along with \code{tickers} to
#' \code{\link{load_gains}}.
#' @param gains Data frame with a date variable named Date and one column of
#' gains for each fund.
#' @param prices Data frame with a date variable named Date and one column of
#' prices for each fund.
#' @param benchmark,y.benchmark,x.benchmark Character string specifying which
#' fund to use as benchmark for metrics (if you request \code{alpha},
#' \code{alpha.annualized}, \code{beta}, or \code{r.squared}).
#' @param ref.tickers Character vector of ticker symbols to include on the
#' plot.
#' @param plotly Logical value for whether to convert the
#' \code{\link[ggplot2]{ggplot}} to a \code{\link[plotly]{plotly}} object
#' internally.
#' @param title Character string.
#' @param base_size Numeric value.
#' @param label_size Numeric value.
#' @param return Character string specifying what to return. Choices are
#' \code{"plot"}, \code{"data"}, and \code{"both"}.
#'
#'
#' @return
#' Depending on \code{return}, a \code{\link[ggplot2]{ggplot}} object, a data
#' frame, or a list containing both.
#'
#'
#' @examples
#' \dontrun{
#' # Plot mean vs. SD for UPRO/VBLTX, and compare to SPY
#' plot_metrics_2funds(
#' formula = mean ~ sd,
#' tickers = c("UPRO", "VBLTX")
#' )
#'
#' # Plot CAGR vs. max drawdown for AAPL/GOOG and FB/TWTR
#' plot_metrics_2funds(
#' formula = cagr ~ mdd,
#' tickers = c("AAPL", "GOOG", "FB", "TWTR")
#' )
#'
#' # Plot Sharpe ratio vs. allocation for SPY/TLT
#' plot_metrics_2funds(
#' formula = sharpe ~ allocation,
#' tickers = c("SPY", "TLT")
#' )
#' }
#'
#' @export
plot_metrics_2funds <- function(metrics = NULL,
formula = mean ~ sd,
tickers = NULL, ...,
points = seq(0, 100, 10),
gains = NULL,
prices = NULL,
benchmark = "SPY",
y.benchmark = benchmark,
x.benchmark = benchmark,
ref.tickers = NULL,
plotly = FALSE,
title = NULL,
base_size = 16,
label_size = 5,
return = "plot") {
# Extract info from formula
all.metrics <- all.vars(formula, functions = FALSE)
# If metrics is specified but doesn't include the expected variables, set defaults
if (! is.null(metrics) & ! all(unlist(stocks:::metric_label(all.metrics)) %in% names(metrics))) {
all.metrics <- unlist(stocks:::label_metric(names(metrics)))
if (length(all.metrics) == 1) {
all.metrics <- c(all.metrics, ".")
} else if (length(all.metrics) >= 2) {
all.metrics <- all.metrics[1: 2]
} else {
stop("The input 'metrics' must have at least one column with a performance metric")
}
}
y.metric <- x.metric <- NULL
if (all.metrics[1] != ".") y.metric <- all.metrics[1]
if (all.metrics[2] != ".") x.metric <- all.metrics[2]
all.metrics <- c(y.metric, x.metric)
# Prep for calculating metrics and plotting
ylabel <- stocks:::metric_label(y.metric)
xlabel <- stocks:::metric_label(x.metric)
# Set benchmarks to NULL if not needed
if (! any(c("alpha", "alpha.annualized", "beta", "r.squared", "r", "rho") %in% all.metrics)) {
benchmark <- y.benchmark <- x.benchmark <- NULL
}
# Check that requested metrics are valid
invalid.requests <- all.metrics[! (all.metrics %in% c(metric.choices, "allocation") | grepl("growth.", all.metrics, fixed = TRUE))]
if (length(invalid.requests) > 0) {
stop(paste("The following metrics are not allowed (see ?calc_metrics for choices):",
paste(invalid.requests, collapse = ", ")))
}
# Drop reference tickers that also appear in tickers
ref.tickers <- setdiff(ref.tickers, tickers)
if (length(ref.tickers) == 0) ref.tickers <- NULL
# Calculate performance metrics if not pre-specified
if (is.null(metrics)) {
# Determine gains if not pre-specified
if (is.null(gains)) {
if (! is.null(prices)) {
date.var <- names(prices) == "Date"
gains <- cbind(prices[-1, date.var, drop = FALSE],
sapply(prices[! date.var], pchanges))
} else if (! is.null(tickers)) {
gains <- load_gains(tickers = unique(c(y.benchmark, x.benchmark, ref.tickers, tickers)),
mutual.start = TRUE, mutual.end = TRUE, ...)
#tickers <- setdiff(names(gains), c("Date", y.benchmark, x.benchmark))
} else {
stop("You must specify 'metrics', 'gains', 'prices', or 'tickers'")
}
}
# If tickers is NULL, set to all funds other than benchmark/reference tickers
if (is.null(tickers)) tickers <- setdiff(names(gains), c("Date", y.benchmark, x.benchmark, ref.tickers))
# Drop NA's
gains <- gains[complete.cases(gains), , drop = FALSE]
# Figure out conversion factor in case CAGR or annualized alpha is requested
min.diffdates <- min(diff(unlist(head(gains$Date, 10))))
units.year <- ifelse(min.diffdates == 1, 252, ifelse(min.diffdates <= 30, 12, 1))
# Extract benchmark gains
if (! is.null(y.benchmark)) {
y.benchmark.gains <- gains[[y.benchmark]]
} else {
y.benchmark.gains <- NULL
}
if (! is.null(x.benchmark)) {
x.benchmark.gains <- gains[[x.benchmark]]
} else {
x.benchmark.gains <- NULL
}
# Calculate metrics for each pair
weights <- rbind(seq(0, 1, 0.01), seq(1, 0, -0.01))
w1 <- seq(0, 100, 1)
w2 <- seq(100, 0, -1)
df <- lapply(seq(1, length(tickers), 2), function(x) {
gains.pair <- as.matrix(gains[tickers[x: (x + 1)]])
wgains.pair <- gains.pair %*% weights
df.pair <- tibble(
Pair = paste(colnames(gains.pair), collapse = "-"),
`Fund 1` = colnames(gains.pair)[1],
`Fund 2` = colnames(gains.pair)[2],
`Allocation 1 (%)` = w1,
`Allocation 2 (%)` = w2,
`Allocation (%)` = `Allocation 1 (%)`
)
if (y.metric != "allocation") {
df.pair[[ylabel]] <- apply(wgains.pair, 2, function(x) {
calc_metric(gains = x, metric = y.metric, units.year = units.year, benchmark.gains = y.benchmark.gains)
})
}
if (x.metric != "allocation") {
df.pair[[xlabel]] <- apply(wgains.pair, 2, function(x) {
calc_metric(gains = x, metric = x.metric, units.year = units.year, benchmark.gains = x.benchmark.gains)
})
}
return(df.pair)
})
df <- bind_rows(df)
# Extract metrics for 100% each ticker
df$Label <- ifelse(df$`Allocation 1 (%)` == 0, paste("100%", df$`Fund 2`),
ifelse(df$`Allocation 2 (%)` == 0, paste("100%", df$`Fund 1`), NA))
# Calculate metrics for reference funds
if (! is.null(ref.tickers)) {
df.ref <- tibble(Pair = ref.tickers, Label = ref.tickers)
if (y.metric == "allocation") {
df.ref[[ylabel]] <- 50.1
} else {
df.ref[[ylabel]] <- sapply(gains[ref.tickers], function(x) {
calc_metric(gains = x, metric = y.metric, units.year = units.year, benchmark.gains = y.benchmark.gains)
})
}
if (x.metric == "allocation") {
df.ref[[xlabel]] <- 50.1
} else {
df.ref[[xlabel]] <- sapply(gains[ref.tickers], function(x) {
calc_metric(gains = x, metric = x.metric, units.year = units.year, benchmark.gains = x.benchmark.gains)
})
}
df <- bind_rows(df.ref, df)
}
} else {
df <- metrics
}
# Prep for ggplot
df <- as.data.frame(df)
df$tooltip <- paste(ifelse(is.na(df$`Fund 1`), df$Pair, paste(df$`Allocation 1 (%)`, "% ", df$`Fund 1`, ", ",
df$`Allocation 2 (%)`, "% ", df$`Fund 2`, sep = "")),
"<br>", stocks:::metric_title(y.metric), ": ", formatC(df[[ylabel]], stocks:::metric_decimals(y.metric), format = "f"), stocks:::metric_units(y.metric),
"<br>", stocks:::metric_title(x.metric), ": ", formatC(df[[xlabel]], stocks:::metric_decimals(x.metric), format = "f"), stocks:::metric_units(x.metric), sep = "")
df.points <- subset(df, Pair %in% ref.tickers | `Allocation (%)` %in% c(0, 100, points))
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1: n]
}
cols <- c()
pairs <- setdiff(unique(df$Pair), ref.tickers)
cols[pairs] <- gg_color_hue(length(pairs))
cols[ref.tickers] <- "black"
p <- ggplot(df, aes(y = .data[[ylabel]], x = .data[[xlabel]], group = Pair, color = Pair, text = tooltip))
if (x.metric == "allocation" & ! is.null(ref.tickers)) {
p <- p + geom_hline(data = df.ref, yintercept = df.ref[[ylabel]], lty = 2)
} else if (y.metric == "allocation" & ! is.null(ref.tickers)) {
p <- p + geom_vline(data = df.ref, yintercept = df.ref[[xlabel]], lty = 2)
}
p <- p +
geom_point(data = df.points) +
geom_path() +
ylim(range(c(0, df[[ylabel]])) * 1.01) +
xlim(range(c(0, df[[xlabel]])) * 1.01) +
scale_colour_manual(values = cols) +
theme_gray(base_size = base_size) +
theme(legend.position = "none") +
labs(title = ifelse(! is.null(title), title, paste(stocks:::metric_title(y.metric), "vs.", stocks:::metric_title(x.metric))),
y = ylabel, x = xlabel)
if (plotly) {
p <- ggplotly(p, tooltip = "tooltip") %>%
style(hoverlabel = list(font = list(size = 15)))
} else {
p <- p + geom_label_repel(mapping = aes(label = Label), data = subset(df, ! is.na(Label)), size = label_size)
}
if (return == "plot") return(p)
if (return == "data") return(df)
return(list(plot = p, data = df))
}
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