# Load packages
 
# Core
library(tidyverse)
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library(tidyquant)
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Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("SOFR", "MSFT", "SONY", "AMZN", "SNP")
 
prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")
## Warning: There were 2 warnings in `dplyr::mutate()`.
## The first warning was:
## ℹ In argument: `data.. = purrr::map(...)`.
## Caused by warning:
## ! x = 'SOFR', get = 'stock.prices': Error in getSymbols.yahoo(Symbols = "SOFR", env = <environment>, verbose = FALSE, : Unable to import "SOFR".
## cannot open the connection
##  Removing SOFR.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.

2 Convert prices to returns

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"))

3 Assign a weight to each asset

symbols <- asset_returns_tbl %>%
  dplyr::distinct(asset) %>%
  dplyr::arrange(asset) %>%
  dplyr::pull()
 
print(symbols)
## [1] "AMZN" "MSFT" "SONY"
cat("n assets =", length(symbols), "\n")
## n assets = 3
w <- rep(1/length(symbols), length(symbols))
 
stopifnot(length(symbols) == length(w),
          all(w >= 0),
          abs(sum(w) - 1) < 1e-8)
 
w_tbl <- tibble::tibble(asset = symbols, weight = w)

4 Build a portfolio

portfolio_returns_rebalanced_monthly_tbl <- asset_returns_tbl %>%
   
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "months")
 
portfolio_returns_rebalanced_monthly_tbl
## # A tibble: 20 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-03-28  0.196  
##  2 2013-06-28  0.145  
##  3 2013-09-30  0.0366 
##  4 2013-12-31  0.0497 
##  5 2014-03-31  0.0120 
##  6 2014-06-30 -0.0473 
##  7 2014-09-30  0.0593 
##  8 2014-12-31  0.0321 
##  9 2015-03-31  0.108  
## 10 2015-06-30  0.100  
## 11 2015-09-30  0.00993
## 12 2015-12-31  0.172  
## 13 2016-03-31 -0.0265 
## 14 2016-06-30  0.0832 
## 15 2016-09-30  0.136  
## 16 2016-12-30 -0.0658 
## 17 2017-03-31  0.140  
## 18 2017-06-30  0.0878 
## 19 2017-09-29  0.0188 
## 20 2017-12-29  0.175

5 Visualize

portfolio_returns_rebalanced_monthly_tbl %>%
   
    ggplot(aes(x = date, y = returns)) +
    geom_point(color = "cornflower blue") +
   
    scale_x_date(breaks = scales::breaks_pretty(n = 6)) +
   
    labs(title = "Portfolio Returns Scatter",
         y = "monthly return")

portfolio_returns_rebalanced_monthly_tbl %>%
   
    ggplot(aes(returns)) +
    geom_histogram(fill = "cornflower blue",
                   binwidth = 0.005) +
   
    labs(title = "Portfolio Returns Distribution",
         y = "count",
         x = "returns")

portfolio_returns_rebalanced_monthly_tbl %>%
   
    ggplot(aes(returns)) +
    geom_histogram(fill = "cornflower blue",
                   binwidth = 0.01) +
    geom_density(aes(returns)) +
   
    labs(title = "Portfolio Histogram and Density",
         y = "distribution",
         x = "monthly returns")

What return should you expect from the portfolio in a typical quarter?