# 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("AMZN", "TSLA", "TM")

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

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" ))

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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "TM"   "TSLA"
# weights
weights <- c(0.5, 0.25, 0.25)
weights
## [1] 0.50 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.5 
## 2 TM         0.25
## 3 TSLA       0.25

4 Build a 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           0.0853 
##  2 2013-06-28           0.321  
##  3 2013-09-30           0.223  
##  4 2013-12-31           0.0467 
##  5 2014-03-31          -0.0182 
##  6 2014-06-30           0.0323 
##  7 2014-09-30          -0.00272
##  8 2014-12-31          -0.0246 
##  9 2015-03-31           0.0804 
## 10 2015-06-30           0.154  
## 11 2015-09-30           0.0338 
## 12 2015-12-31           0.142  
## 13 2016-03-31          -0.108  
## 14 2016-06-30           0.0583 
## 15 2016-09-30           0.110  
## 16 2016-12-30          -0.0411 
## 17 2017-03-31           0.131  
## 18 2017-06-30           0.101  
## 19 2017-09-29           0.0136 
## 20 2017-12-29           0.0914

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 = "quarterly returns",
         x = NULL,
         title = "Portfolio Returns Scatter")

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

Looking at the histogram you can see that 14/20 quarterly returns fell between 0 and .15 percent. You can see that this leaves six total outliers being two that fall above .15 percent and four that showed a negative return. After reveiwing this information I would be expecting a typical quarter return of .05 percent as that is historically the median for returns on my portfolio.