# 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,
                 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 
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 
## # ℹ 50 more rows

5 Plot

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = date, y= portfolio.returns))

    geom_point(color = "turquoise") 
## geom_point: na.rm = FALSE
## stat_identity: na.rm = FALSE
## position_identity
    # Formating
    scale_x_date(date_breaks = "1 year", date_labels = "xy" )
## <ScaleContinuousDate>
##  Range:  
##  Limits:    0 --    1
    # Labeling
    labs(y = "monthly returns",
         x = NULL,
         title = "Portfolio Returns Scatter")
## $y
## [1] "monthly returns"
## 
## $x
## NULL
## 
## $title
## [1] "Portfolio Returns Scatter"
## 
## attr(,"class")
## [1] "labels"
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

Histogram

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

labs(x ="returns",
     title = "Portfolio Returns Distribution")
## $x
## [1] "returns"
## 
## $title
## [1] "Portfolio Returns Distribution"
## 
## attr(,"class")
## [1] "labels"
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

Histogram & Density Plot

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "turquoise", binwitdth = 0.01) +
    geom_density() + 
    
    # Formating
scale_x_continuous(labels = scales::percent_format())+
    
labs(x ="returns",
     y = "distribution",
     title = "Histogram and Density")