# 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("GOOG", "GME", "NVDA", "V")

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     = "quarterly",
                 type       = "log") %>% 
    slice(-1) %>%
    
    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] "GME"  "GOOG" "NVDA" "V"
# weights
weights <- c(0.60, 0.55, 0.50, 0.45)
weights
## [1] 0.60 0.55 0.50 0.45
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 GME        0.6 
## 2 GOOG       0.55
## 3 NVDA       0.5 
## 4 V          0.45

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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28            0.213 
##  2 2013-06-28            0.387 
##  3 2013-09-30            0.175 
##  4 2013-12-31            0.221 
##  5 2014-03-31           -0.0615
##  6 2014-06-30            0.0233
##  7 2014-09-30            0.0236
##  8 2014-12-31           -0.0278
##  9 2015-03-31            0.120 
## 10 2015-06-30            0.0473
## 11 2015-09-30            0.187 
## 12 2015-12-31            0.0930
## 13 2016-03-31            0.106 
## 14 2016-06-30           -0.0121
## 15 2016-09-30            0.333 
## 16 2016-12-30            0.149 
## 17 2017-03-31            0.0510
## 18 2017-06-30            0.202 
## 19 2017-09-29            0.174 
## 20 2017-12-29            0.0521

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    ggplot(mapping = aes(x = portfolio.returns)) +
    geom_histogram(fill    = "plum3", binwidth = 0.01) +
    geom_density() +
    
    # Formatting 
    scale_x_continuous(labels = scales::percent_format()) +
    
    labs(x     = "returns",
         y     = "distribution",
         title = "Portolio Histogram & Density")

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

From the Portfolio Histogram & Density plot I created above, the typical returns are relatively steady besides around 10% to 20%. This is where a majority of the returns cluster. There is quite the gap between about 22%-30%. In a quarter, the most frequent return for this portfolio is between 10% and 20%. For instance, if I were to pick a random quarter, I would be most likely to get some kind of return between those percentages.