我想charts.PerformanceSummary在PerformanceAnalytics包中提供基本功能的“ggplot 版本”，因为我认为 ggplot 通常更漂亮，理论上在编辑图像方面更强大。我已经相当接近了，但有一些问题需要一些帮助。即：
- 减少图例占用的空间量，当上面有超过 10 行时，它会变得可怕/丑陋......（只需线条颜色和名称就足够了）
- 增加 Daily_Returns 方面的大小以匹配图表的大小。PerformanceSummary 在
PerformanceAnalytics - 有一个选项可以指定在 Daily_Returns 方面的每日回报系列中显示哪些资产，而不是总是使用第一列，这比发生在
charts.PerformanceSummary
如果有更好的方法可以使用gridExtra而不是方面来做到这一点......我并不反对人们向我展示这看起来会更好......
这里的问题是美学，我想可能很容易操作，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......
除了奖励积分之外，我还希望能够在每个资产的图表上方或下方或侧面的某处显示一些与之相关的性能统计数据......不太确定在哪里最好显示或显示此信息。
此外，如果有人对此提出建议，我并不反对那些建议清理我的代码的部分。
这是我的可重现示例...
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")
我想从以下结果中复制图像：
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)
