# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

Stocks : “MSFT”, “AAPL”, “F”, “JPM”, “SBUX”

from 2012-12-31 to 2017-12-31

1 Import stock prices

symbols <- c("MSFT", "AAPL", "F", "JPM", "SBUX")

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.     = "quarters",
                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" "F"    "JPM"  "MSFT" "SBUX"
# weights
weights <- c(0.3, 0.2, 0.2, 0.15, 0.15)
weights
## [1] 0.30 0.20 0.20 0.15 0.15
w_tbl <- tibble(symbols, weights)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col = asset,
                 returns_col = returns,
                 weigts = w_tbl,
                 rebalance_on = "quarters")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31         -0.000198
##  2 2013-02-28         -0.00101 
##  3 2013-03-28          0.0157  
##  4 2013-04-30          0.0584  
##  5 2013-05-31          0.0743  
##  6 2013-06-28         -0.0273  
##  7 2013-07-31          0.0580  
##  8 2013-08-30         -0.00318 
##  9 2013-09-30          0.0245  
## 10 2013-10-31          0.0460  
## # ℹ 50 more rows

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

Histogram

portfolio_returns_tbl %>% 
   
     ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "skyblue1", binwidth = 0.005,) +
    
    labs(x = "returns",
         title = "portfolio Returns Distribution")

Histogram & Density Plot

portfolio_returns_tbl %>% 
   
     ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "skyblue1", binwidth = 0.01,) +
    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?

Based on the visuals, you can expect the portfolio to have a typical quarterly return that is slightly positive, probably somewhere between 1% and 5%. However, keep in mind that there’s some variability, so returns could dip below that range or even go higher. Most of the returns tend to cluster around 0% to 3%, so that’s where you’d likely see the most common performance.