# 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  0.000985
##  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(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)

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 Plot

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(portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", binwidth = 0.005) +
    
    labs(x = "returns",
    title = "Portfolio Returns Distribution")

Portfolio Histogram and Density

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

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