# 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 ("DLR", "NVDA", "GOOG", "MSFT", "AMZN")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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] "AMZN" "DLR"  "GOOG" "MSFT" "NVDA"
# weights
weights <- c(0.2, 0.2, 0.1, 0.3, 0.2)
weights 
## [1] 0.2 0.2 0.1 0.3 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN        0.2
## 2 DLR         0.2
## 3 GOOG        0.1
## 4 MSFT        0.3
## 5 NVDA        0.2

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            0.0564
##  2 2013-06-28            0.0803
##  3 2013-09-30            0.0109
##  4 2013-12-31            0.105 
##  5 2014-03-31            0.0370
##  6 2014-06-30            0.0330
##  7 2014-09-30            0.0484
##  8 2014-12-31            0.0177
##  9 2015-03-31            0.0135
## 10 2015-06-30            0.0503
## 11 2015-09-30            0.0915
## 12 2015-12-31            0.238 
## 13 2016-03-31            0.0228
## 14 2016-06-30            0.109 
## 15 2016-09-30            0.135 
## 16 2016-12-30            0.0951
## 17 2017-03-31            0.0820
## 18 2017-06-30            0.112 
## 19 2017-09-29            0.0824
## 20 2017-12-29            0.101

5 Plot: Portfolio Histogram and Density

Scatterplot

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

Histogram

portfolio_returns_tbl %>%
  
  ggplot(mapping = aes(x = portfolio.returns)) +
  geom_histogram(fill = "cornflowerblue", 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 = "cornflowerblue", 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 histogram, I would expect the portfolio to earn around 9% to 10% in a typical quarter, since most of the quarterly returns are concentrated in this range. An 8% return occurred in three quarters, and an 11% return also occurred in three quarters, while 9% occurred once and 10% occurred twice. Overall, the distribution shows that quarterly returns were mostly concentrated around 8% to 11%.