# 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("CRWD", "AMZN", "SHOP","TTD", "NVDA")

prices <- tq_get(x = symbols, 
                 get  = "stock.prices", 
                 from = "2021-01-01")

2 Convert prices to returns (quarterly)

asset_returns_tbl <- prices %>%
    
    group_by(symbol) %>%
    
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn, 
                 period     = "monthly",
                 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" "CRWD" "NVDA" "SHOP" "TTD"
# weights
weights <- c(0.25, 0.25, 0.2, 0.2, 0.1)

w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.25
## 2 CRWD       0.25
## 3 NVDA       0.2 
## 4 SHOP       0.2 
## 5 TTD        0.1

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: 45 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2021-02-26          0.0378  
##  2 2021-03-31         -0.0978  
##  3 2021-04-30          0.110   
##  4 2021-05-28          0.00183 
##  5 2021-06-30          0.149   
##  6 2021-07-30         -0.000161
##  7 2021-08-31          0.0648  
##  8 2021-09-30         -0.0992  
##  9 2021-10-29          0.105   
## 10 2021-11-30          0.0333  
## # ℹ 35 more rows

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     = "distrobution", 
         title = "Portfolio Histogram & Density")

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

You should expect around a 3% return from these stocks, with a high variance or risk involved. There have been 3 negative 10 percent losses, so that is a posibility with this portfolio. An average return for this would be anywhere from -2 percent to around 10 percent. In the past this portfolio has seen a loss of 30 percent, and a few losses of 10-15 percent in a quarter. But it has also seen gains of over 20 percent.