# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize expected returns and risk to make it easier to compare the performance of multiple assets and portfolios.

Choose your stocks.

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

1 Import stock prices

# Choose stocks

symbols <- c("SPY", "NVDA", "VOOG")


prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

 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

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()

weights <- c (.25, .50, .25)
weights
## [1] 0.25 0.50 0.25
weights_tbl <- tibble(symbols, weights)
weights_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 NVDA       0.25
## 2 SPY        0.5 
## 3 VOOG       0.25

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = weights_tbl,
                 col_rename   = "returns",
                 rebalance_on = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-03-28  0.0850 
##  2 2013-06-28  0.0461 
##  3 2013-09-30  0.0671 
##  4 2013-12-31  0.0848 
##  5 2014-03-31  0.0409 
##  6 2014-06-30  0.0488 
##  7 2014-09-30  0.0106 
##  8 2014-12-31  0.0576 
##  9 2015-03-31  0.0223 
## 10 2015-06-30 -0.00699
## 11 2015-09-30  0.00602
## 12 2015-12-31  0.126  
## 13 2016-03-31  0.0284 
## 14 2016-06-30  0.0846 
## 15 2016-09-30  0.125  
## 16 2016-12-30  0.132  
## 17 2017-03-31  0.0545 
## 18 2017-06-30  0.0968 
## 19 2017-09-29  0.0876 
## 20 2017-12-29  0.0692

calculate standard deviation

portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   Rb = NULL, 
                   performance_fun = table.Stats) %>%
    
    select(Stdev) %>%
    mutate(tq_sd = round(Stdev, 4) * 100)
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0401  4.01
portfolio_returns_tbl
## # A tibble: 20 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-03-28  0.0850 
##  2 2013-06-28  0.0461 
##  3 2013-09-30  0.0671 
##  4 2013-12-31  0.0848 
##  5 2014-03-31  0.0409 
##  6 2014-06-30  0.0488 
##  7 2014-09-30  0.0106 
##  8 2014-12-31  0.0576 
##  9 2015-03-31  0.0223 
## 10 2015-06-30 -0.00699
## 11 2015-09-30  0.00602
## 12 2015-12-31  0.126  
## 13 2016-03-31  0.0284 
## 14 2016-06-30  0.0846 
## 15 2016-09-30  0.125  
## 16 2016-12-30  0.132  
## 17 2017-03-31  0.0545 
## 18 2017-06-30  0.0968 
## 19 2017-09-29  0.0876 
## 20 2017-12-29  0.0692

6 plot

asset_returns_sd_tbl <- asset_returns_tbl %>%
  
  group_by(asset) %>%
  tq_performance(Ra = returns,
                 Rb = NULL,
                 performance_fun = table.Stats) %>%
  
  select(asset, Stdev) %>%
  ungroup() %>%
  
  # Add portfolio sd
  add_row(tibble(asset = "Portfolio",
                 Stdev = sd(portfolio_returns_tbl$returns)))

asset_returns_sd_tbl %>%
  
  # Plot
  ggplot(aes(asset, Stdev, dol = asset)) +
  geom_point() +
  ggrepel::geom_text_repel(aes(label = asset),
                           data = asset_returns_sd_tbl %>%
                             filter(asset == "Portfolio")) +
  
  labs(title = "")