# 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", "DELL")

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

2 Convert prices to returns (monthly)

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] "DELL" "TSLA"
# weights
weights <- c(0.50,0.50)
weights
## [1] 0.5 0.5
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 2 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 DELL        0.5
## 2 TSLA        0.5

4 Build a portfolio

#?tq_portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0510
##  2 2013-02-28 -0.0389
##  3 2013-03-28  0.0425
##  4 2013-04-30  0.177 
##  5 2013-05-31  0.342 
##  6 2013-06-28  0.0640
##  7 2013-07-31  0.112 
##  8 2013-08-30  0.127 
##  9 2013-09-30  0.0809
## 10 2013-10-31 -0.0949
## # ℹ 50 more rows

Compute Standard Deviation

portfolio_sd_tidyquant_building_percent <- portfolio_returns_tbl %>%
    
    tq_performance(Ra = returns,
                   performance_fun = table.Stats) %>%
    
    select(Stdev) %>%
    mutate(tq_sd = round(Stdev, 4))

portfolio_sd_tidyquant_building_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0787 0.0787
# Mean of portfolio returns 
portfolio_mean_tidyquant_building_percent <- mean(portfolio_returns_tbl$returns)

portfolio_mean_tidyquant_building_percent
## [1] 0.02462261

6 Plot

Expected Returns vs Risk

# Expected Returns vs Risk
sd_mean_tbl <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%
    
    # Add portfolio sd
    add_row(tibble(asset = "portfolio",
                Mean = portfolio_mean_tidyquant_building_percent,
                   Stdev = portfolio_sd_tidyquant_building_percent$tq_sd ))

sd_mean_tbl
## # A tibble: 3 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 DELL      0.0374 0.0662
## 2 TSLA      0.037  0.145 
## 3 portfolio 0.0246 0.0787
sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point()

24 month rolling volatility

rolling_sd_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select     = returns, 
              mutate_fun = rollapply,
              width      = 24,
              FUN        = sd, 
              col_rename = "rolling_sd") %>%
    
    na.omit() %>%
    select(date, rolling_sd)

rolling_sd_tbl
## # A tibble: 37 × 2
##    date       rolling_sd
##    <date>          <dbl>
##  1 2014-12-31     0.102 
##  2 2015-01-30     0.103 
##  3 2015-02-27     0.103 
##  4 2015-03-31     0.104 
##  5 2015-04-30     0.100 
##  6 2015-05-29     0.0757
##  7 2015-06-30     0.0752
##  8 2015-07-31     0.0727
##  9 2015-08-31     0.0693
## 10 2015-09-30     0.0675
## # ℹ 27 more rows
rolling_sd_tbl %>%
    
    ggplot(aes(x = date, y= rolling_sd)) +
    geom_line(color = "cornflowerblue") +
    
    # Formatting
    scale_y_continuous(labels = scales::percent_format()) +
    
    #Labeling
    labs(x = NULL,
         y = NULL,
         title = "24-Month Rolling Volatility") +
    theme(plot.title = element_text(hjust = 0.5))