# 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
weight <- c(0.25,0.25,0.2,0.2,0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols,weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <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 
## # ℹ 50 more rows

Calculate Standard Deviation

portfolio_sd <- portfolio_returns_tbl %>%
    tq_performance(Ra = portfolio.returns, 
                   performance_fun = table.Stats) %>%
    select(Stdev) %>%
    mutate(tq_sd = round(Stdev, 3))
portfolio_sd
## # A tibble: 1 × 2
##    Stdev tq_sd
##    <dbl> <dbl>
## 1 0.0235 0.024
#mean of portfolio returns
portfoliomean <- mean(portfolio_returns_tbl$portfolio.returns)
portfoliomean
## [1] 0.005899135

6 Plot

Expected Return vs Risk

#Expected Return 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 = portfoliomean,
                   Stdev = portfolio_sd$tq_sd))
sd_mean_tbl
## # 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 0.00590 0.024
sd_mean_tbl %>%
    ggplot(aes(x = Stdev,
               y = Mean,
               color = asset)) +
    geom_point() + 
    ggrepel::geom_label_repel(aes(label = asset))

24 Month Rolling Volatility

rollingsd_table <- portfolio_returns_tbl %>%
    tq_mutate(select = portfolio.returns,
              mutate_fun = rollapply,
              width = 24,
              FUN = sd,
              col_rename = "rollingsd") %>%
    na.omit() %>%
    select(date, rollingsd)
rollingsd_table
## # A tibble: 37 × 2
##    date       rollingsd
##    <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
## # ℹ 27 more rows
rollingsd_table %>%
    ggplot(aes( x = date,
                 y = rollingsd,)) +
    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))

ScatterPlot

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = date, y = portfolio.returns)) +
    geom_point(color = "cornflowerblue") +
    
    #formatting
    scale_x_date(date_breaks = "1 year",
                 date_labels = "%Y") +
    
    #Labeling
    labs(y = "monthly returns",
         x = NULL,
         title = "portfolio returns scatter")

# Histogram

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "violet", binwidth = 0.005) +

    labs(x= "returns",
         title = "Portfolio Returns Distribution")

#Histogram & Density Plot

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "violet", binwidth = 0.01) +
    geom_density() +
    
    #formatting
    scale_x_continuous(labels = scales::percent_format()) +

    labs(x= "returns",
         y = "distribution",
         title = "Portfolio Histogram & Density")