# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

Choose your stocks.

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 (quarterly)

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 (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(symbol) %>% 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 = symbol, 
                returns_col = monthly.returns,
                weights = w_tbl,
                replace_on = "months")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-01-31           0.0204 
##  2 2013-02-28          -0.00220
##  3 2013-03-28           0.0127 
##  4 2013-04-30           0.0173 
##  5 2013-05-31          -0.0113 
##  6 2013-06-28          -0.0233 
##  7 2013-07-31           0.0342 
##  8 2013-08-30          -0.0231 
##  9 2013-09-30           0.0513 
## 10 2013-10-31           0.0305 
## # ℹ 50 more rows

5 Plot: Portfolio Histogram and Density

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 Plot")

#Histogram

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

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

#Portfolio History and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", binwidth = 0.01) +
    geom_density() +

    # Formatting
    scale_x_continuous(labels = scales:: percent_format()) +
    
labs(x = "returns",
     y = "distribution",
     title = "Portfolio History & Density")

What return should you expect from the portfolio in a typical quarter?

Typically returns are 1% growth every quarter. With it happening 15 times we can only expect that has the best chance however anywhere from 0-2.5% happens the average and could see it falling in that range.