# Load packages

# Core
library(tidyverse)
library(tidyquant)

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("AAPL", "META", "AMZN", "GOOGL", "MSFT")
prices <- tq_get(x    = symbols,
                 get  = "stock.prices", 
                 from = "2012-12-31",
                 to   = "2017-12-31")

2 Convert prices to returns (quarterly)

asset_returns_table <- 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 (change the weigting scheme)

symbols <- asset_returns_table %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL"  "AMZN"  "GOOGL" "META"  "MSFT"
# weights
weights <- c(0.15, 0.25, 0.15, 0.25, 0.20)
weights
## [1] 0.15 0.25 0.15 0.25 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.15
## 2 AMZN       0.25
## 3 GOOGL      0.15
## 4 META       0.25
## 5 MSFT       0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_table %>%
    
    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           0.0111 
##  2 2013-06-28           0.0424 
##  3 2013-09-30           0.227  
##  4 2013-12-31           0.169  
##  5 2014-03-31          -0.00501
##  6 2014-06-30           0.0605 
##  7 2014-09-30           0.0747 
##  8 2014-12-31          -0.0123 
##  9 2015-03-31           0.0585 
## 10 2015-06-30           0.0647 
## 11 2015-09-30           0.0613 
## 12 2015-12-31           0.177  
## 13 2016-03-31          -0.00723
## 14 2016-06-30           0.00233
## 15 2016-09-30           0.139  
## 16 2016-12-30          -0.0360 
## 17 2017-03-31           0.150  
## 18 2017-06-30           0.0623 
## 19 2017-09-29           0.0635 
## 20 2017-12-29           0.112

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", binwidth = 0.03) +
    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?

A typical expected return from this portfolio is roughly 4.5% - 7.5%, which occurred in seven quarters during this time frame.