# 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("TSLA", "GM", "F", "VWAGY", "HMC")

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"))

asset_returns_tbl
## # A tibble: 100 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 F     2013-03-28  0.0227
##  2 F     2013-06-28  0.170 
##  3 F     2013-09-30  0.0925
##  4 F     2013-12-31 -0.0835
##  5 F     2014-03-31  0.0189
##  6 F     2014-06-30  0.108 
##  7 F     2014-09-30 -0.146 
##  8 F     2014-12-31  0.0558
##  9 F     2015-03-31  0.0506
## 10 F     2015-06-30 -0.0632
## # … with 90 more rows

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "F"     "GM"    "HMC"   "TSLA"  "VWAGY"
# weights
weight <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 F         0.25
## 2 GM        0.25
## 3 HMC       0.2 
## 4 TSLA      0.2 
## 5 VWAGY     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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0131 
##  2 2013-06-28           0.295  
##  3 2013-09-30           0.181  
##  4 2013-12-31          -0.00344
##  5 2014-03-31          -0.00960
##  6 2014-06-30           0.0717 
##  7 2014-09-30          -0.0901 
##  8 2014-12-31          -0.00368
##  9 2015-03-31           0.0390 
## 10 2015-06-30           0.0152 
## 11 2015-09-30          -0.141  
## 12 2015-12-31           0.0771 
## 13 2016-03-31          -0.0641 
## 14 2016-06-30          -0.0747 
## 15 2016-09-30           0.0509 
## 16 2016-12-30           0.0417 
## 17 2017-03-31           0.0657 
## 18 2017-06-30           0.0294 
## 19 2017-09-29           0.0706 
## 20 2017-12-29           0.0490

5 Plot: Portfolio Histogram and Density

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?

In a typical quarter you would expect to see a return higher than 0 as that is where the results are more concentrated. For three quarters which was the highest distribution, the returns were about 7%. Most other returns only happened for one quarter, with a handful occuring for two quarters at 0%, 4% and 5%. So it is more typical to see a return at or above 0 with the most typical return at 7%.