# 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("AMZN", "AAPL", "TSLA")

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
symbols
## [1] "AAPL" "AMZN" "TSLA"
weight <- c(0.25, 0.25, 0.2)
weight
## [1] 0.25 0.25 0.20
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 3 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AAPL      0.25
## 2 AMZN      0.25
## 3 TSLA      0.2

4 Build a 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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28         -0.00706 
##  2 2013-06-28          0.193   
##  3 2013-09-30          0.195   
##  4 2013-12-31          0.0528  
##  5 2014-03-31          0.0131  
##  6 2014-06-30          0.0689  
##  7 2014-09-30          0.0218  
##  8 2014-12-31         -0.00309 
##  9 2015-03-31          0.0435  
## 10 2015-06-30          0.112   
## 11 2015-09-30         -0.00518 
## 12 2015-12-31          0.0520  
## 13 2016-03-31         -0.0311  
## 14 2016-06-30         -0.000369
## 15 2016-09-30          0.0746  
## 16 2016-12-30         -0.0110  
## 17 2017-03-31          0.150   
## 18 2017-06-30          0.0760  
## 19 2017-09-29          0.00452 
## 20 2017-12-29          0.0550

5 Plot: Portfolio Histogram and Density

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

    labs(y = "quarterly returns",
         x = NULL,
         title = "Portfolio Returns Scatter")

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

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

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

A typical expected return from this portfolio is roughly 0% - 3%, which occurred in seven quarters during this time frame.