# 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("XOM", "QQQ", "SPY", "TSLA","CGC")

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] "CGC"  "QQQ"  "SPY"  "TSLA" "XOM"
# 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 CGC        0.25
## 2 QQQ        0.25
## 3 SPY        0.2 
## 4 TSLA       0.2 
## 5 XOM        0.1

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: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0620 
##  2 2013-06-28           0.224  
##  3 2013-09-30           0.150  
##  4 2013-12-31           0.0147 
##  5 2014-03-31           0.0666 
##  6 2014-06-30           0.0599 
##  7 2014-09-30          -0.0626 
##  8 2014-12-31          -0.0338 
##  9 2015-03-31          -0.0487 
## 10 2015-06-30           0.0595 
## 11 2015-09-30          -0.114  
## 12 2015-12-31           0.181  
## 13 2016-03-31          -0.0218 
## 14 2016-06-30           0.0114 
## 15 2016-09-30           0.112  
## 16 2016-12-30           0.221  
## 17 2017-03-31           0.124  
## 18 2017-06-30           0.00157
## 19 2017-09-29           0.0985 
## 20 2017-12-29           0.268

5 Plot: Portfolio Histogram and Density

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

What return should you expect from the portfolio in a typical quarter? In a typical quarter you should expect returns -5% all the way to 15% with the highest frequency at 7%.