# 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", "WMT", "COST", "AMZN")
prices <- tq_get(x = symbols, 
                 get = "stock.prices",
                 from = "2012-12-31")

2 Convert prices to returns (quarterly)

asset_returns_tbl <- prices %>%
    
    group_by(symbol) %>%

    tq_transmute(select     = adjusted, 
                 mutate_fun = periodReturn, 
                 period     = "quarterly",
                 type       = "log") %>%
    slice(-1) %>%
    #Remove the first row, but since data is group, it will remove the first line of each group
    
    ungroup() %>%

    set_names(c("asset", "date", "returns"))

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMZN" "COST" "SPY"  "WMT"
# weights
weights <- c(0.30, 0.30, 0.25, 0.2)
weights
## [1] 0.30 0.30 0.25 0.20
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMZN       0.3 
## 2 COST       0.3 
## 3 SPY        0.25
## 4 WMT        0.2

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 = "quarters")

portfolio_returns_tbl
## # A tibble: 52 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0853 
##  2 2013-06-28           0.0331 
##  3 2013-09-30           0.0611 
##  4 2013-12-31           0.122  
##  5 2014-03-31          -0.0697 
##  6 2014-06-30           0.00982
##  7 2014-09-30           0.0319 
##  8 2014-12-31           0.0626 
##  9 2015-03-31           0.0796 
## 10 2015-06-30          -0.0153 
## # ℹ 42 more rows

5 Plot: Portfolio Histogram and Density

Histogram & Density Plot

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill = "cornflowerblue", binwidth = 0.01) +
    # This creates the blue form/graph
     geom_density() +
    # This creates the line
   
     # Formatting
    scale_x_continuous(labels = scales::percent_format()) + 
    
    labs(x= "returns",
         y = "distribution", 
         title = "Portfolio Histogram & Density ")

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

The range for my portfolio is approximately -27% to 17%, with the most frequent return at 5% and 7% (happened 4 times). Which is where the peak of the curve is centered and the mode is approximately 7%. The most high likely returns, with 3-4 frequency are between 0.5 and 12%. Thus, I expect my portfolio to have a return between 0.5% and 12% in a typical quarter, as those returns are the most high likely .