# 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 ("PANW", "AAPL", "NVDA")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 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] "AAPL" "NVDA" "PANW"
# weights
weights <- c(0.33, 0.33, 0.33)
weights 
## [1] 0.33 0.33 0.33
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.33
## 2 NVDA       0.33
## 3 PANW       0.33

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           -0.0234
##  2 2013-06-28           -0.0999
##  3 2013-09-30            0.126 
##  4 2013-12-31            0.142 
##  5 2014-03-31            0.0841
##  6 2014-06-30            0.144 
##  7 2014-09-30            0.0799
##  8 2014-12-31            0.134 
##  9 2015-03-31            0.114 
## 10 2015-06-30            0.0514
## 11 2015-09-30            0.0225
## 12 2015-12-31            0.0909
## 13 2016-03-31            0.0149
## 14 2016-06-30           -0.0431
## 15 2016-09-30            0.268 
## 16 2016-12-30            0.0765
## 17 2017-03-31            0.0453
## 18 2017-06-30            0.153 
## 19 2017-09-29            0.118 
## 20 2017-12-29            0.0603

5 Plot: Portfolio Histogram and Density

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

portfolio_returns_tbl %>%
  
  ggplot(mapping = aes(x = date, y = portfolio.returns)) +
  geom_point(color = "cornflowerblue") +
  
  # Formatting
  scale_x_date(date_breaks = "1 year", 
               date_labels = "%Y") + 
  
  # Labeling
  labs(y = "monthly returns", 
       x = NULL, 
       title = "Portfolio Returns Scatter")

Histogram

portfolio_returns_tbl %>%
  
  ggplot(mapping = aes(x = portfolio.returns)) +
  geom_histogram(fill = "cornflowerblue", binwidth = 0.05) +
  
  labs(x = "returns", 
       title = "Portfolio Returns Distribution")

Histogram & Density Plot

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

```