# 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("AAPL", "MSFT", "GOOG")

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 <- asset_returns_tbl %>% distinct(asset) %>% pull()

w <- c(0.35,
       0.35,
       0.30)

w_tbl <- tibble(symbols, w)

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    
    tq_portfolio(assets_col   = asset,
                 returns_col  = returns,
                 weights      = w_tbl,
                 col_rename   = "returns",
                 rebalance_on = "quarters")

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-03-28  0.00118
##  2 2013-06-28  0.0585 
##  3 2013-09-30  0.0560 
##  4 2013-12-31  0.183  
##  5 2014-03-31  0.0145 
##  6 2014-06-30  0.0878 
##  7 2014-09-30  0.0649 
##  8 2014-12-31  0.00359
##  9 2015-03-31  0.0196 
## 10 2015-06-30  0.0139 
## 11 2015-09-30  0.0139 
## 12 2015-12-31  0.132  
## 13 2016-03-31  0.00839
## 14 2016-06-30 -0.0903 
## 15 2016-09-30  0.139  
## 16 2016-12-30  0.0326 
## 17 2017-03-31  0.121  
## 18 2017-06-30  0.0496 
## 19 2017-09-29  0.0688 
## 20 2017-12-29  0.107

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "pink",
                   binwidth = 0.005) +
    
    labs(title = "Portfolio Returns Distribution",
         y = "count",
         x = "returns")

portfolio_returns_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "pink",
                   binwidth = 0.01) +
    geom_density(aes(returns)) +
    
    labs(title = "Portfolio Histogram and Density",
         y = "Distribution",
         x = "Quarterly Returns")

What return should you expect from the portfolio in a typical quarter? In a typical quarter you should expect anywhere between -10% return and 20% return, with the majority of returns occurring on the positive side of that.