# 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

#choose stocks 
symbols <- c("NFLX", "GOOG", "TSLA")

prices <- tq_get(x     = symbols, 
                 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 <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols 
## [1] "GOOG" "NFLX" "TSLA"
# weights 
weights <- c(0.25, 0.45, 0.30)
weights
## [1] 0.25 0.45 0.30
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GOOG       0.25
## 2 NFLX       0.45
## 3 TSLA       0.3

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 = "quarters") 

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28            0.384 
##  2 2013-06-28            0.387 
##  3 2013-09-30            0.347 
##  4 2013-12-31            0.0648
##  5 2014-03-31            0.0764
##  6 2014-06-30            0.151 
##  7 2014-09-30            0.0148
##  8 2014-12-31           -0.174 
##  9 2015-03-31            0.0503
## 10 2015-06-30            0.298 
## 11 2015-09-30            0.0589
## 12 2015-12-31            0.0910
## 13 2016-03-31           -0.0682
## 14 2016-06-30           -0.0921
## 15 2016-09-30            0.0506
## 16 2016-12-30            0.115 
## 17 2017-03-31            0.177 
## 18 2017-06-30            0.106 
## 19 2017-09-29            0.0832
## 20 2017-12-29            0.0200

5 Plot: Portfolio Histogram and Density

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

The returns, during this period of time, are relatively flat. Therefore it is difficult to pinpoint what a return from this portfolio should be during a typical quarter. However, there is a peak between 5% and 11% returns.