# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

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

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

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

1 Import stock prices

symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG")
prices <- tq_get(x    = symbols,
                 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     = "monthly",
                 type       = "log") %>%
    
    slice(-1) %>% 
    
    ungroup() %>%
    
    set_names(c("asset", "date", "returns"))

asset_returns_tbl    
## # A tibble: 300 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AGG   2013-01-31 -0.00623 
##  2 AGG   2013-02-28  0.00589 
##  3 AGG   2013-03-28  0.000984
##  4 AGG   2013-04-30  0.00964 
##  5 AGG   2013-05-31 -0.0202  
##  6 AGG   2013-06-28 -0.0158  
##  7 AGG   2013-07-31  0.00269 
##  8 AGG   2013-08-30 -0.00830 
##  9 AGG   2013-09-30  0.0111  
## 10 AGG   2013-10-31  0.00829 
## # … with 290 more rows

3 Assign a weight to each asset

#Symbols
symbols <- asset_returns_tbl %>%
    distinct(asset) %>%
    pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
#Weights
weights <- c(1)
weights
## [1] 1
w_tbl <- tibble(symbols, weights)

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: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.129  
##  2 2013-02-28          -0.00133
##  3 2013-03-28           0.0812 
##  4 2013-04-30           0.0909 
##  5 2013-05-31          -0.0350 
##  6 2013-06-28          -0.112  
##  7 2013-07-31           0.182  
##  8 2013-08-30          -0.119  
##  9 2013-09-30           0.251  
## 10 2013-10-31           0.161  
## # … with 50 more rows

5 Calculate Portfolio

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

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##   Stdev tq_sd
##   <dbl> <dbl>
## 1  0.12  0.12
#Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <- mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.03444651

6 Plot

#Expected returns vs risk

sd_mean_table <- asset_returns_tbl %>%
    
    group_by(asset) %>%
    tq_performance(Ra = returns, 
                   performance_fun = table.Stats) %>%
    select(Mean = ArithmeticMean, Stdev) %>%
    ungroup() %>%

    
    #Add portfolio stdev
    add_row(tibble(asset = "Portfolio",
                   Mean = portfolio_mean_tidyquant_builtin_percent,
                   Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))

sd_mean_table
## # A tibble: 6 × 3
##   asset       Mean  Stdev
##   <chr>      <dbl>  <dbl>
## 1 AGG       0.0017 0.0086
## 2 EEM       0.0028 0.0419
## 3 EFA       0.006  0.0326
## 4 IJS       0.0119 0.0396
## 5 SPY       0.0121 0.0272
## 6 Portfolio 0.0344 0.12
sd_mean_table %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) + 
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

### 24 month rolling vol

rolling_sd_tbl <- portfolio_returns_tbl %>%
    
    tq_mutate(select = portfolio.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      0.119
##  2 2015-01-30      0.120
##  3 2015-02-27      0.125
##  4 2015-03-31      0.125
##  5 2015-04-30      0.125
##  6 2015-05-29      0.125
##  7 2015-06-30      0.124
##  8 2015-07-31      0.121
##  9 2015-08-31      0.133
## 10 2015-09-30      0.127
## # … with 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))