# 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

# Choose stocks

symbols <- c("SPY", "NVDA", "VOOG")


prices <- tq_get(x = symbols,
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

 asset_returns_tbl <- prices %>%

   
    group_by(symbol) %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 type = "log") %>%
    slice(-1) %>%
    ungroup() %>%
    
    set_names(c("asset", "date", "returns"))

3 Assign a weight to each asset

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

weights <- c (.25, .50, .25)
weights
## [1] 0.25 0.50 0.25
weights_tbl <- tibble(symbols, weights)
weights_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 NVDA       0.25
## 2 SPY        0.5 
## 3 VOOG       0.25

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0347
##  2 2013-02-28  0.0193
##  3 2013-03-28  0.0310
##  4 2013-04-30  0.0322
##  5 2013-05-31  0.0311
##  6 2013-06-28 -0.0173
##  7 2013-07-31  0.0436
##  8 2013-08-30 -0.0150
##  9 2013-09-30  0.0384
## 10 2013-10-31  0.0286
## # ℹ 50 more rows

5 Plot

portfolio_returns_tbl %>%
    
    ggplot(aes(x = date, y = returns)) +
    geom_point(color = "cornflower blue") +
    
    # Formatting
    scale_x_date(breaks = scales::breaks_pretty(n = 6)) +
    
    labs(title = "Portfolio Returns Scatter",
         y = "monthly return")

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

portfolio_returns_tbl %>%
    
    ggplot(aes(returns)) +
    geom_histogram(fill = "cornflower blue",
                   binwidth = 0.01) +
    geom_density(aes(returns)) +
    
    labs(title = "Portfolio Histogram and Density",
         y = "distribution",
         x = "monthly returns")