# Load packages
# Core
library(tidyverse)
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library(tidyquant)
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## method from
## as.zoo.data.frame zoo
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Take raw prices of five individual stocks and transform them into monthly returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
# Choose stocks
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-01-01",
to = "2017-01-01")
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(
select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log"
) %>%
rename(
asset = symbol,
returns = monthly.returns
) %>%
ungroup()
asset_returns_tbl %>%
ggplot(aes(x = returns)) +
geom_density(aes(color = asset), alpha = 1) +
geom_histogram(aes(fill = asset), show.legend = FALSE, alpha = 0.3, binwidth = 0.001) + facet_wrap(~asset, ncol = 1)+
# labeling
labs(
title = "Distribution of Monthly Returns, 2012-2016",
y = "frequency",
x = "rate of returns",
caption = "A typical monthly return is higher for SPY and IJS than for AGG, EEM, and EFA.")