# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(scales)
library(ggrepel)

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, 
                 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     = "monthly", 
                 type       = "log") %>%
    
   slice(-1) %>%
    
    ungroup() %>%
    set_names(c("asset", "date", "returns"))

3 Assign a weight to each asset

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))

portfolio_sd_tidyquant_builtin_percent
## # A tibble: 1 × 2
##    Stdev  tq_sd
##    <dbl>  <dbl>
## 1 0.0235 0.0235
# 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

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(Stdev, Mean = ArithmeticMean) %>%
    ungroup() %>%
    
    # Add Portfolio Standard Deviation
    
    add_row(tibble(asset = "portfolio",
    Mean = portfolio_mean_tidyquant_builtin_percent,
    Stdev = portfolio_sd_tidyquant_builtin_percent$tq_sd))
Sd_Mean_tbl
## # A tibble: 6 × 3
##   asset      Stdev    Mean
##   <chr>      <dbl>   <dbl>
## 1 AGG       0.0086 0.0017 
## 2 EEM       0.0419 0.0028 
## 3 EFA       0.0326 0.006  
## 4 IJS       0.0396 0.0119 
## 5 SPY       0.0272 0.0121 
## 6 portfolio 0.0235 0.00590
Sd_Mean_tbl %>%
    
    ggplot(aes(x = Stdev, y = Mean, color = asset)) +
    geom_point() +
    ggrepel::geom_text_repel(aes(label = asset))

24 Months Rolling Volitility

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 = "orange") +

# Formating
   scale_y_continuous(labels = scales::percent_format()) +
    labs(x = NULL, y = NULL, title = "24 Month Rolling Volitility") +
    theme(plot.title = element_text(hjust = .5))