# Load Packages
# Core
library(tidyverse)
library(tidyquant)
symbols <- c("LULU", "NKE", "UA")
prices <- tq_get(x = symbols,
from = "2021-12-31",
to = "2024-12-31")
asset_returns_tbl <- prices %>%
group_by(symbol) %>%
tq_transmute(select = adjusted,
mutate_fun = periodReturn,
period = "quarterly",
type = "log") %>%
slice(-1) %>%
ungroup() %>%
set_names(c("asset", "date", "returns"))
# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "LULU" "NKE" "UA"
# weights
weights <- c(0.25, 0.2, 0.1)
weights
## [1] 0.25 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
## symbols weights
## <chr> <dbl>
## 1 LULU 0.25
## 2 NKE 0.2
## 3 UA 0.1
# ?tq_portfolio
portfolio_returns_tbl <- asset_returns_tbl %>%
tq_portfolio(assets_col = asset,
returns_col = returns,
weights = w_tbl,
rebalance_on = "quarters")
portfolio_returns_tbl
## # A tibble: 12 × 2
## date portfolio.returns
## <date> <dbl>
## 1 2022-03-31 -0.0745
## 2 2022-06-30 -0.200
## 3 2022-09-30 -0.0585
## 4 2022-12-30 0.143
## 5 2023-03-31 0.0375
## 6 2023-06-30 -0.0348
## 7 2023-09-29 -0.0284
## 8 2023-12-29 0.124
## 9 2024-03-28 -0.111
## 10 2024-06-28 -0.119
## 11 2024-09-30 0.0335
## 12 2024-12-30 0.0414
Histogram & Density Plot
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",
y = "distribution",
title = "Portfolio Histogram & Density")
What return should you expect from the portfolio in a typical quarter? The graph shows a peak at about -4% returns from all three companies