# 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

# Stocks
symbols <- c("TSLA", "NVDA", "AAPL", "MSFT", "AMZN")

# Prices
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 %>%

    # Calculate quarterly returns
    group_by(symbol) %>%
    tq_transmute(select     = adjusted,
                 mutate_fun = periodReturn,
                 period     = "quarterly",
                 type       = "log") %>%
    slice(-1) %>%
    ungroup() %>%

    # remane
    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" "MSFT" "NVDA" "TSLA"
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 AAPL       0.25
## 2 AMZN       0.25
## 3 MSFT       0.2 
## 4 NVDA       0.2 
## 5 TSLA       0.1

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  0.00743
##  2 2013-06-28  0.147  
##  3 2013-09-30  0.152  
##  4 2013-12-31  0.110  
##  5 2014-03-31  0.0235 
##  6 2014-06-30  0.0675 
##  7 2014-09-30  0.0431 
##  8 2014-12-31  0.0248 
##  9 2015-03-31  0.0441 
## 10 2015-06-30  0.0874 
## 11 2015-09-30  0.0459 
## 12 2015-12-31  0.161  
## 13 2016-03-31 -0.00991
## 14 2016-06-30  0.0496 
## 15 2016-09-30  0.179  
## 16 2016-12-30  0.0899 
## 17 2017-03-31  0.140  
## 18 2017-06-30  0.117  
## 19 2017-09-29  0.0696 
## 20 2017-12-29  0.109

5 Plot: Portfolio Histogram and Density

portfolio_returns_tbl %>%
    
    ggplot(mapping = aes(x = returns)) +
    geom_histogram(fill = "red", binwidth = 0.005) + 
    
    labs(x     = "Returns", 
         title = "Portfolio Returns Distrubtion")

portfolio_returns_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "red", binwidth = 0.005) +
    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? With the peak of the density curve beginning at 3% and going to 5% with a few highs of 15%, so on average the quarterly returs will be around 4%-6%.