# 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", "MSFT", "META")

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] "AAPL" "META" "MSFT"
#weights
weights <- c(0.30, 0.30, 0.40)
weights
## [1] 0.3 0.3 0.4
w_tbl <-  tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.3
## 2 META        0.3
## 3 MSFT        0.4

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          -0.0347 
##  2 2013-06-28           0.0388 
##  3 2013-09-30           0.256  
##  4 2013-12-31           0.126  
##  5 2014-03-31           0.0573 
##  6 2014-06-30           0.102  
##  7 2014-09-30           0.119  
##  8 2014-12-31           0.0281 
##  9 2015-03-31           0.00244
## 10 2015-06-30           0.0519 
## 11 2015-09-30          -0.0195 
## 12 2015-12-31           0.126  
## 13 2016-03-31           0.0390 
## 14 2016-06-30          -0.0648 
## 15 2016-09-30           0.136  
## 16 2016-12-30           0.00921
## 17 2017-03-31           0.155  
## 18 2017-06-30           0.0408 
## 19 2017-09-29           0.0918 
## 20 2017-12-29           0.0961

5 Plot: Portfolio Histogram and Density

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 Plot")

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

As we can see on the “portfolio histogram & density plot: AAPL, MSFT & META has between 2012-12-31 through 2017-12-31 generated a lot of profit. The amount of quarters distributed in the histogram are 20 and I can identify that from a typical quarter, the return I should expect is around a 5 % gain quarterly. Over a 5 year period with an expected quarterly gain of 5 %, that proves this portfolio would have been an amazing hold and made anyone rich if they held these stocks during this period.