# 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("RTX", "GD", "LMT", "BA")
stock_prices <- tq_get(x    = symbols, 
                 get  = "stock.prices", 
                 from = "2012-12-31", 
                 to   = "2017-12-31")

2 Convert prices to returns (quarterly)

ar_table <- stock_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 <- ar_table %>% distinct(asset) %>% pull()
symbols
## [1] "BA"  "GD"  "LMT" "RTX"
# weights
weights <- c(0.35, 0.30, 0.20, 0.15)
weights
## [1] 0.35 0.30 0.20 0.15
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 BA         0.35
## 2 GD         0.3 
## 3 LMT        0.2 
## 4 RTX        0.15

4 Build a portfolio

portfolio_returns_table <- ar_table %>%
    
    tq_portfolio(assets_col   = asset, 
                 returns_col  = returns, 
                 weights      = w_tbl, 
                 rebalance_on = "quarters")
portfolio_returns_table
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28            0.0852
##  2 2013-06-28            0.123 
##  3 2013-09-30            0.142 
##  4 2013-12-31            0.123 
##  5 2014-03-31            0.0387
##  6 2014-06-30            0.0263
##  7 2014-09-30            0.0448
##  8 2014-12-31            0.0602
##  9 2015-03-31            0.0653
## 10 2015-06-30           -0.0328
## 11 2015-09-30           -0.0348
## 12 2015-12-31            0.0602
## 13 2016-03-31           -0.0414
## 14 2016-06-30            0.0605
## 15 2016-09-30            0.0343
## 16 2016-12-30            0.117 
## 17 2017-03-31            0.0926
## 18 2017-06-30            0.0822
## 19 2017-09-29            0.119 
## 20 2017-12-29            0.0750

5 Plot: Portfolio Histogram and Density

portfolio_returns_table %>% 
    
    ggplot(mapping = aes(x = portfolio.returns)) + 
    geom_histogram(fill = "lightsteelblue", binwidth = 0.025) +
    geom_density(color = "maroon") + 
    
    
    scale_x_continuous(labels = scales::percent_format())+
    
    labs(x = "Quarterly Returns", 
         y = "Distributions", 
         title = "Portfolio Histogram & Density")

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

From this portfolio, I could expect a return of about 6.5% each quarter. There is a chance that the return could actually be negative but there is a better chance that the return could be over 10%. There were far more quarters during this period that were positive than negative which to me means that it would be a strong portfolio.