# Load packages
# Core
library(tidyverse)
library(tidyquant)
Collect individual returns into a portfolio by assigning a weight to each stock
Choose your stocks.
from 2012-12-31 to 2017-12-31
symbols <- c("TGT", "AMZN", "WMT")
prices <- tq_get(x = symbols,
get = "stock.prices",
from = "2012-12-31",
to = "2017-12-31")
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] "AMZN" "TGT" "WMT"
# weights
weights <- c(0.25, 0.25, 0.2)
weights
## [1] 0.25 0.25 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
## symbols weights
## <chr> <dbl>
## 1 AMZN 0.25
## 2 TGT 0.25
## 3 WMT 0.2
# 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: 20 × 2
## date portfolio.returns
## <date> <dbl>
## 1 2013-03-28 0.0727
## 2 2013-06-28 0.0134
## 3 2013-09-30 0.0126
## 4 2013-12-31 0.0732
## 5 2014-03-31 -0.0564
## 6 2014-06-30 -0.0201
## 7 2014-09-30 0.0251
## 8 2014-12-31 0.0646
## 9 2015-03-31 0.0591
## 10 2015-06-30 0.0105
## 11 2015-09-30 0.0171
## 12 2015-12-31 0.0419
## 13 2016-03-31 0.0245
## 14 2016-06-30 0.0218
## 15 2016-09-30 0.0360
## 16 2016-12-30 -0.0199
## 17 2017-03-31 -0.0133
## 18 2017-06-30 0.0223
## 19 2017-09-29 0.0390
## 20 2017-12-29 0.125
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 = "disribution",
title = "Portfolio Histogram & Density")
What return should you expect from the portfolio in a typical quarter?
You should expect a positive return for most quarters ranging between 0.01-0.07 every three months.