# 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"))
##     asset      date   returns 
##   "asset"    "date" "returns"

3 Assign a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(symbol) %>% pull()
    

# 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

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol, 
                 returns_col = monthly.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

Scatter

portfolio_returns_tbl %>%
    ggplot(mapping = aes(x = date, y = portfolio.returns)) +
    geom_point(color = "cornflowerblue") + 
    
    # formatting 
    scale_x_date(date_breaks = "1 week", 
                 date_labels = "%Y")

# 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"

Histogram

portfolio_returns_tbl %>% 
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", binwidth = 0.005)+
    labs(x = "returns",
         title = "Portfolio Returns Distribution")

#Histogram and Density

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

    labs(x = "returns",
         y = "distribution",
         title = "Portfolio Histogram & Density")
## $x
## [1] "returns"
## 
## $y
## [1] "distribution"
## 
## $title
## [1] "Portfolio Histogram & Density"
## 
## attr(,"class")
## [1] "labels"