# 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("SPY", "EFA", "IJS", "EEM", "AGG")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2017-12-31")
prices
## # A tibble: 6,300 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 SPY    2012-12-31  140.  143.  140.  142. 243935200     113.
##  2 SPY    2013-01-02  145.  146.  145.  146. 192059000     116.
##  3 SPY    2013-01-03  146.  146.  145.  146. 144761800     115.
##  4 SPY    2013-01-04  146.  147.  146.  146. 116817700     116.
##  5 SPY    2013-01-07  146.  146.  145.  146. 110002500     116.
##  6 SPY    2013-01-08  146.  146.  145.  146. 121265100     115.
##  7 SPY    2013-01-09  146.  146.  146.  146.  90745600     116.
##  8 SPY    2013-01-10  147.  147.  146.  147. 130735400     117.
##  9 SPY    2013-01-11  147.  147.  147.  147. 113917300     117.
## 10 SPY    2013-01-14  147.  147.  146.  147.  89567200     116.
## # ℹ 6,290 more rows

2 Convert prices to returns (quarterly)

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"))

asset_returns_tbl
## # A tibble: 100 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AGG   2013-03-28  0.000645
##  2 AGG   2013-06-28 -0.0264  
##  3 AGG   2013-09-30  0.00553 
##  4 AGG   2013-12-31  0.000199
##  5 AGG   2014-03-31  0.0176  
##  6 AGG   2014-06-30  0.0193  
##  7 AGG   2014-09-30  0.00275 
##  8 AGG   2014-12-31  0.0186  
##  9 AGG   2015-03-31  0.0151  
## 10 AGG   2015-06-30 -0.0184  
## # ℹ 90 more rows

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AGG         0.2
## 2 EEM         0.2
## 3 EFA         0.2
## 4 IJS         0.2
## 5 SPY         0.2

4 Build a portfolio

# ?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
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0419 
##  2 2013-06-28          -0.0113 
##  3 2013-09-30           0.0628 
##  4 2013-12-31           0.0565 
##  5 2014-03-31           0.00793
##  6 2014-06-30           0.0385 
##  7 2014-09-30          -0.0318 
##  8 2014-12-31           0.0161 
##  9 2015-03-31           0.0221 
## 10 2015-06-30          -0.00449
## 11 2015-09-30          -0.0916 
## 12 2015-12-31           0.0257 
## 13 2016-03-31           0.0257 
## 14 2016-06-30           0.0175 
## 15 2016-09-30           0.0508 
## 16 2016-12-30           0.0112 
## 17 2017-03-31           0.0508 
## 18 2017-06-30           0.0346 
## 19 2017-09-29           0.0482 
## 20 2017-12-29           0.0422

5 Plot: Portfolio Histogram and Density

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", 
       y = "distribution",
       title = "Portfolio Histogram & Density")

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

2.5%