Es sollte so aussehen wie im zweiten foto.
Ich habe das mit diesem Code probiert aber ohne Erfolg:
y <-tbl.SP500Monthly[1:2]
- Code: Alles auswählen
---
output:
pdf_document: default
html_document: default
---
library(tidyverse) # for overall grammar
library(lubridate) # to parse dates
library(tidyquant) # to download data from yahoo finance
library(glue) # to automatically construct figure captions
library(scales) # for nicer axis labels
library(readxl) # to read Shiller's data
library(rio)
tbl.SP500Recent <- tq_get("^SP500TR", get = "stock.prices",
from = "1988-01-04", to = "2022-05-31") %>%
transmute(Date = date, TotalReturnIndex = close) %>%
na.omit() %>%
group_by(Month = ceiling_date(Date, "month")-1) %>%
arrange(Date) %>%
filter(Date == max(Date)) %>%
ungroup() %>%
select(Month, TotalReturnIndex)
temp <- tempfile(fileext = ".xls")
download.file(url = "http://www.econ.yale.edu/~shiller/data/ie_data.xls",
destfile = temp, mode='wb')
tbl.ShillerHistorical <- read_excel(temp, sheet = "Data", skip = 7) %>%
transmute(Month = ceiling_date(ymd(str_replace(str_c(Date, ".01"), "\\.1\\.", "\\.10\\.")), "month")-1,
Price = as.numeric(P),
Dividend = as.numeric(D))
tbl.ShillerHistorical <- tbl.ShillerHistorical %>%
arrange(Month) %>%
mutate(Ret = (Price + Dividend / 12) / lag(Price) - 1)
tbl.Check <- tbl.ShillerHistorical %>%
full_join(tbl.SP500Recent, by = "Month") %>%
filter(!is.na(TotalReturnIndex)) %>%
arrange(Month) %>%
mutate(Ret = if_else(row_number() == 1, 0, Ret), # ignore first month return
TotalReturnCheck = TotalReturnIndex[1] * cumprod(1 + Ret)) %>%
na.omit()
fig.Check <- tbl.Check %>%
select(Month, Actual = TotalReturnIndex, Simulated = TotalReturnCheck) %>%
pivot_longer(cols = -Month, names_to = "Type", values_to = "Value") %>%
ggplot(aes(x = Month, y = Value, color = Type)) +
geom_line() +
theme_bw() +
scale_y_continuous(labels = comma)+
labs(x = NULL, y = NULL,
title = "Actual and Simulated S&P 500 Total Return Index",
subtitle = glue("Both Indexes start at {min(tbl.Check$Month)}"))
fig.Check
tbl.SP500Historical <- tbl.SP500Recent %>%
filter(Month == min(Month)) %>%
full_join(tbl.ShillerHistorical %>%
filter(Month <= min(tbl.SP500Recent$Month)), by = "Month") %>%
arrange(desc(Month)) %>%
mutate(Ret = if_else(row_number() == 1, 0, Ret), # ignore first month return
TotalReturnIndex = TotalReturnIndex[1] / cumprod(1 + Ret))
tbl.SP500Index <- tq_get("^GSPC", get = "stock.prices",
from = "1871-02-28", to = "2021-12-31") %>%
transmute(Date = date, Index = close) %>%
na.omit() %>%
group_by(Month = ceiling_date(Date, "month") - 1) %>%
arrange(Date) %>%
filter(Date == max(Date)) %>%
ungroup() %>%
select(Month, Index)
tbl.SP500Monthly <- tbl.SP500Recent%>%
bind_rows(tbl.SP500Historical %>%
filter(Month < min(tbl.SP500Recent$Month)) %>%
select(Month, TotalReturnIndex)) %>%
full_join(tbl.SP500Index %>%
select(Month, Index), by = "Month") %>%
filter(Month >= "1871-02-28") %>%
arrange(Month)
tbl.SP500Monthly
fig.Historical <- tbl.SP500Monthly %>%
select(Month, Index, `Total Return` = TotalReturnIndex) %>%
pivot_longer(cols = -Month, names_to = "Type", values_to = "Value") %>%
group_by(Type) %>%
arrange(Month) %>%
mutate(Value = Value / Value[1] * 100) %>%
ggplot(aes(x = Month, y = Value, color = Type)) +
geom_line() +
theme_bw() +
scale_y_log10(labels = comma) +
scale_x_date(expand = c(0, 0), date_breaks = "10 years", date_labels = "%Y") +
labs(x = NULL, y = NULL,
title = "S&P 500 Index and Total Return Index Since 1871",
subtitle = glue("Both Indexes are Normalized to 100 at {min(tbl.SP500Monthly$Month)}"))
fig.Historical
chart.Drawdown(ndx_returns)
y <-tbl.SP500Monthly[1:2]
export(tbl.SP500Monthly, "mxfile.xlsx")
