我想制作 charts.PerformanceSummary 包中可用的 PerformanceAnalytics 基本功能的“ggplot 版本”，因为我认为 ggplot 在编辑图像方面通常更漂亮，理论上更强大。我已经相当接近了，但有一些问题我需要一些帮助。即:
PerformanceAnalytics charts.PerformanceSummary 如果有更好的方法可以使用
gridExtra 而不是 facets 来做到这一点......我并不反对人们向我展示如何看起来更好......这里的问题是美学，我想可能易于操作，因为 PerformanceAnalytics 已经有一个很好的工作示例，我只是想让它更漂亮/更专业......
除了这个奖励积分之外，我希望能够在每个 Assets 的图表上或下方或侧面的某个地方显示一些与它相关的性能统计数据......不太确定最好显示或显示的位置此信息。
此外，如果人们对此有建议，我不会反对建议清理我的代码的部分。
这是我可重复的示例...
首先生成返回数据:
require(xts)
X.stock.rtns <- xts(rnorm(1000,0.00001,0.0003), Sys.Date()-(1000:1))
Y.stock.rtns <- xts(rnorm(1000,0.00003,0.0004), Sys.Date()-(1000:1))
Z.stock.rtns <- xts(rnorm(1000,0.00005,0.0005), Sys.Date()-(1000:1))
rtn.obj <- merge(X.stock.rtns , Y.stock.rtns, Z.stock.rtns)
colnames(rtn.obj) <- c("x.stock.rtns","y.stock.rtns","z.stock.rtns")
我想从以下结果复制图像:
require(PerformanceAnalytics)
charts.PerformanceSummary(rtn.obj, geometric=TRUE)
这是我迄今为止的尝试......
gg.charts.PerformanceSummary <- function(rtn.obj, geometric=TRUE, main="",plot=TRUE){
# load libraries
suppressPackageStartupMessages(require(ggplot2))
suppressPackageStartupMessages(require(scales))
suppressPackageStartupMessages(require(reshape))
suppressPackageStartupMessages(require(PerformanceAnalytics))
# create function to clean returns if having NAs in data
clean.rtn.xts <- function(univ.rtn.xts.obj,na.replace=0){
univ.rtn.xts.obj[is.na(univ.rtn.xts.obj)]<- na.replace
univ.rtn.xts.obj
}
# Create cumulative return function
cum.rtn <- function(clean.xts.obj, g=TRUE){
x <- clean.xts.obj
if(g==TRUE){y <- cumprod(x+1)-1} else {y <- cumsum(x)}
y
}
# Create function to calculate drawdowns
dd.xts <- function(clean.xts.obj, g=TRUE){
x <- clean.xts.obj
if(g==TRUE){y <- Drawdowns(x)} else {y <- Drawdowns(x,geometric=FALSE)}
y
}
# create a function to create a dataframe to be usable in ggplot to replicate charts.PerformanceSummary
cps.df <- function(xts.obj,geometric){
x <- clean.rtn.xts(xts.obj)
series.name <- colnames(xts.obj)[1]
tmp <- cum.rtn(x,geometric)
tmp$rtn <- x
tmp$dd <- dd.xts(x,geometric)
colnames(tmp) <- c("Cumulative_Return","Daily_Return","Drawdown")
tmp.df <- as.data.frame(coredata(tmp))
tmp.df$Date <- as.POSIXct(index(tmp))
tmp.df.long <- melt(tmp.df,id.var="Date")
tmp.df.long$asset <- rep(series.name,nrow(tmp.df.long))
tmp.df.long
}
# A conditional statement altering the plot according to the number of assets
if(ncol(rtn.obj)==1){
# using the cps.df function
df <- cps.df(rtn.obj,geometric)
# adding in a title string if need be
if(main==""){
title.string <- paste0(df$asset[1]," Performance")
} else {
title.string <- main
}
# generating the ggplot output with all the added extras....
gg.xts <- ggplot(df, aes_string(x="Date",y="value",group="variable"))+
facet_grid(variable ~ ., scales="free", space="free")+
geom_line(data=subset(df,variable=="Cumulative_Return"))+
geom_bar(data=subset(df,variable=="Daily_Return"),stat="identity")+
geom_line(data=subset(df,variable=="Drawdown"))+
ylab("")+
geom_abline(intercept=0,slope=0,alpha=0.3)+
ggtitle(title.string)+
theme(axis.text.x = element_text(angle = 45, hjust = 1))+
scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%d/%m/%Y"))
} else {
# a few extra bits to deal with the added rtn columns
no.of.assets <- ncol(rtn.obj)
asset.names <- colnames(rtn.obj)
df <- do.call(rbind,lapply(1:no.of.assets, function(x){cps.df(rtn.obj[,x],geometric)}))
df$asset <- ordered(df$asset, levels=asset.names)
if(main==""){
title.string <- paste0(df$asset[1]," Performance")
} else {
title.string <- main
}
if(no.of.assets>5){legend.rows <- 5} else {legend.rows <- no.of.assets}
gg.xts <- ggplot(df, aes_string(x="Date", y="value",group="asset"))+
facet_grid(variable~.,scales="free",space="free")+
geom_line(data=subset(df,variable=="Cumulative_Return"),aes(colour=factor(asset)))+
geom_bar(data=subset(df,variable=="Daily_Return"),stat="identity",aes(fill=factor(asset),colour=factor(asset)),position="dodge")+
geom_line(data=subset(df,variable=="Drawdown"),aes(colour=factor(asset)))+
ylab("")+
geom_abline(intercept=0,slope=0,alpha=0.3)+
ggtitle(title.string)+
theme(legend.title=element_blank(), legend.position=c(0,1), legend.justification=c(0,1),
axis.text.x = element_text(angle = 45, hjust = 1))+
guides(col=guide_legend(nrow=legend.rows))+
scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%d/%m/%Y"))
}
assign("gg.xts", gg.xts,envir=.GlobalEnv)
if(plot==TRUE){
plot(gg.xts)
} else {}
}
# seeing the ggplot equivalent....
gg.charts.PerformanceSummary(rtn.obj, geometric=TRUE)
最佳答案
我只是在寻找那个。你已经很接近了。站在你的肩膀上，我能够解决一些问题。
编辑(2015 年 5 月 9 日): 现在可以通过三冒号运算符 Drawdown() 调用函数 PerformanceAnalytics:::Drawdown() 。下面的代码经过编辑以反射(reflect)此更改。 编辑(2018 年 4 月 22 日): show_guide 已被弃用并替换为 show.legend 。
require(xts)
X.stock.rtns <- xts(rnorm(1000,0.00001,0.0003), Sys.Date()-(1000:1))
Y.stock.rtns <- xts(rnorm(1000,0.00003,0.0004), Sys.Date()-(1000:1))
Z.stock.rtns <- xts(rnorm(1000,0.00005,0.0005), Sys.Date()-(1000:1))
rtn.obj <- merge(X.stock.rtns , Y.stock.rtns, Z.stock.rtns)
colnames(rtn.obj) <- c("x","y","z")
# advanced charts.PerforanceSummary based on ggplot
gg.charts.PerformanceSummary <- function(rtn.obj, geometric = TRUE, main = "", plot = TRUE)
{
# load libraries
suppressPackageStartupMessages(require(ggplot2))
suppressPackageStartupMessages(require(scales))
suppressPackageStartupMessages(require(reshape))
suppressPackageStartupMessages(require(PerformanceAnalytics))
# create function to clean returns if having NAs in data
clean.rtn.xts <- function(univ.rtn.xts.obj,na.replace=0){
univ.rtn.xts.obj[is.na(univ.rtn.xts.obj)]<- na.replace
univ.rtn.xts.obj
}
# Create cumulative return function
cum.rtn <- function(clean.xts.obj, g = TRUE)
{
x <- clean.xts.obj
if(g == TRUE){y <- cumprod(x+1)-1} else {y <- cumsum(x)}
y
}
# Create function to calculate drawdowns
dd.xts <- function(clean.xts.obj, g = TRUE)
{
x <- clean.xts.obj
if(g == TRUE){y <- PerformanceAnalytics:::Drawdowns(x)} else {y <- PerformanceAnalytics:::Drawdowns(x,geometric = FALSE)}
y
}
# create a function to create a dataframe to be usable in ggplot to replicate charts.PerformanceSummary
cps.df <- function(xts.obj,geometric)
{
x <- clean.rtn.xts(xts.obj)
series.name <- colnames(xts.obj)[1]
tmp <- cum.rtn(x,geometric)
tmp$rtn <- x
tmp$dd <- dd.xts(x,geometric)
colnames(tmp) <- c("Index","Return","Drawdown") # names with space
tmp.df <- as.data.frame(coredata(tmp))
tmp.df$Date <- as.POSIXct(index(tmp))
tmp.df.long <- melt(tmp.df,id.var="Date")
tmp.df.long$asset <- rep(series.name,nrow(tmp.df.long))
tmp.df.long
}
# A conditional statement altering the plot according to the number of assets
if(ncol(rtn.obj)==1)
{
# using the cps.df function
df <- cps.df(rtn.obj,geometric)
# adding in a title string if need be
if(main == ""){
title.string <- paste("Asset Performance")
} else {
title.string <- main
}
gg.xts <- ggplot(df, aes_string( x = "Date", y = "value", group = "variable" )) +
facet_grid(variable ~ ., scales = "free_y", space = "fixed") +
geom_line(data = subset(df, variable == "Index")) +
geom_bar(data = subset(df, variable == "Return"), stat = "identity") +
geom_line(data = subset(df, variable == "Drawdown")) +
geom_hline(yintercept = 0, size = 0.5, colour = "black") +
ggtitle(title.string) +
theme(axis.text.x = element_text(angle = 0, hjust = 1)) +
scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%m/%Y")) +
ylab("") +
xlab("")
}
else
{
# a few extra bits to deal with the added rtn columns
no.of.assets <- ncol(rtn.obj)
asset.names <- colnames(rtn.obj)
df <- do.call(rbind,lapply(1:no.of.assets, function(x){cps.df(rtn.obj[,x],geometric)}))
df$asset <- ordered(df$asset, levels=asset.names)
if(main == ""){
title.string <- paste("Asset",asset.names[1],asset.names[2],asset.names[3],"Performance")
} else {
title.string <- main
}
if(no.of.assets>5){legend.rows <- 5} else {legend.rows <- no.of.assets}
gg.xts <- ggplot(df, aes_string(x = "Date", y = "value" )) +
# panel layout
facet_grid(variable~., scales = "free_y", space = "fixed", shrink = TRUE, drop = TRUE, margin =
, labeller = label_value) + # label_value is default
# display points for Index and Drawdown, but not for Return
geom_point(data = subset(df, variable == c("Index","Drawdown"))
, aes(colour = factor(asset), shape = factor(asset)), size = 1.2, show.legend = TRUE) +
# manually select shape of geom_point
scale_shape_manual(values = c(1,2,3)) +
# line colours for the Index
geom_line(data = subset(df, variable == "Index"), aes(colour = factor(asset)), show.legend = FALSE) +
# bar colours for the Return
geom_bar(data = subset(df,variable == "Return"), stat = "identity"
, aes(fill = factor(asset), colour = factor(asset)), position = "dodge", show.legend = FALSE) +
# line colours for the Drawdown
geom_line(data = subset(df, variable == "Drawdown"), aes(colour = factor(asset)), show.legend = FALSE) +
# horizontal line to indicate zero values
geom_hline(yintercept = 0, size = 0.5, colour = "black") +
# horizontal ticks
scale_x_datetime(breaks = date_breaks("6 months"), labels = date_format("%m/%Y")) +
# main y-axis title
ylab("") +
# main x-axis title
xlab("") +
# main chart title
ggtitle(title.string)
# legend
gglegend <- guide_legend(override.aes = list(size = 3))
gg.xts <- gg.xts + guides(colour = gglegend, size = "none") +
# gglegend <- guide_legend(override.aes = list(size = 3), direction = "horizontal") # direction overwritten by legend.box?
# gg.xts <- gg.xts + guides(colour = gglegend, size = "none", shape = gglegend) + # Warning: "Duplicated override.aes is ignored"
theme( legend.title = element_blank()
, legend.position = c(0,1)
, legend.justification = c(0,1)
, legend.background = element_rect(colour = 'grey')
, legend.key = element_rect(fill = "white", colour = "white")
, axis.text.x = element_text(angle = 0, hjust = 1)
, strip.background = element_rect(fill = "white")
, panel.background = element_rect(fill = "white", colour = "white")
, panel.grid.major = element_line(colour = "grey", size = 0.5)
, panel.grid.minor = element_line(colour = NA, size = 0.0)
)
}
assign("gg.xts", gg.xts,envir=.GlobalEnv)
if(plot == TRUE){
plot(gg.xts)
} else {}
}
# display chart
gg.charts.PerformanceSummary(rtn.obj, geometric = TRUE)
更新:标签
自定义标签可以通过以下功能获得:
# create a function to store fancy axis labels
my_labeller <- function(var, value){ # from the R Cookbook
value <- as.character(value)
if (var=="variable")
{
value[value=="Index"] <- "Cumulative Returns"
value[value=="Return"] <- "Daily Returns"
value[value=="Drawdown"] <- "Drawdown"
}
return(value)
}
更新:背景
背景、网格线、颜色等的外观可以在 theme() 函数内进行控制:上面的代码已更新以反射(reflect)这些更改。
关于r - ggplot 版本的 charts.PerformanceSummary，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/14817006/