# 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"))

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(0.25, 0.25, 0.2, 0.2, 0.1)
weights
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weights)
w_tbl 
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        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: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0204 
##  2 2013-02-28          -0.00239
##  3 2013-03-28           0.0121 
##  4 2013-04-30           0.0174 
##  5 2013-05-31          -0.0128 
##  6 2013-06-28          -0.0247 
##  7 2013-07-31           0.0321 
##  8 2013-08-30          -0.0224 
##  9 2013-09-30           0.0511 
## 10 2013-10-31           0.0301 
## # … with 50 more rows

5 Calculate Standard Deviation

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)*100)

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0235  2.35
# Mean of portfolio returns
portfolio_mean_tidyquant_builtin_percent <-
mean(portfolio_returns_tbl$portfolio.returns)

portfolio_mean_tidyquant_builtin_percent
## [1] 0.005899135

6 Plot

# Excepted 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() %>%
    mutate(Stdev = Stdev * 100,
           Mean = Mean * 100) %>% 
    
    # Add portfolio sd
    add_row(tibble(asset = "Portfolio",
                  Mean  = portfolio_mean_tidyquant_builtin_percent * 100,
                  Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
sd_mean_tbl
## # A tibble: 6 × 3
##   asset      Mean Stdev
##   <chr>     <dbl> <dbl>
## 1 AGG       0.17   0.86
## 2 EEM       0.28   4.19
## 3 EFA       0.6    3.26
## 4 IJS       1.19   3.96
## 5 SPY       1.21   2.72
## 6 Portfolio 0.590  2.35

Expected Returns

sd_mean_tbl %>%
    
    ggplot(aes(x = Stdev, y= Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_label_repel(aes(label = asset))

## 24 Months Rolling

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       0.0237
##  2 2015-01-30       0.0236
##  3 2015-02-27       0.0245
##  4 2015-03-31       0.0246
##  5 2015-04-30       0.0247
##  6 2015-05-29       0.0245
##  7 2015-06-30       0.0242
##  8 2015-07-31       0.0238
##  9 2015-08-31       0.0262
## 10 2015-09-30       0.0247
## # … 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())
## <ScaleContinuousPosition>
##  Range:  
##  Limits:    0 --    1
    #Labeling
    labs(x = NULL,
         y = NULL,
         title = "24 Month Rolling Volatility")+
    theme(plot.title = element_text(hjust = 0.5))    
## NULL