# Load packages
# Core
library(tidyverse)
library(tidyquant)
Collect individual returns into a portfolio by assigning a weight to each stock
five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”
from 2012-12-31 to 2017-12-31
symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x = symbols,
get = "stock.prices",
fro = "2012-12-31",
to = "2017-12-31")
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "monthly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))
# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# Weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
# ?tq_portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
rebalance_on = "months")
portfolio_returns_tbl %>%
ggplot(mapping = aes(x = date, y = portfolio.returns)) +
geom_point(color = "cornflowerblue") +
# Formatting
scale_x_date(date_breaks = "1 year",
date_labels = "%Y") +
# Labeling
labs(y = "monthly returns",
x = NULL,
title = "Portfolio Returns Scatter")
portfolio_returns_tbl %>%
ggplot(mapping = aes(x = portfolio.returns)) +
geom_histogram(fill = "cornflowerblue", binwidth = 0.005) +
labs(x = "returns",
title = "Portfolio Returns Distribution")
portfolio_returns_tbl %>%
ggplot(mapping = aes(x = portfolio.returns)) +
geom_histogram(fill = "cornflowerblue", binwidth = 0.01) +
geom_density() +
# Formatting
scale_x_continuous(labels = scales::percent_format()) +
labs(x = "returns",
title = "Portfolio Histogram & Density")