# 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("SPY", "EFA", "IJS", "EEM", "AGG")

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] "AGG" "EEM" "EFA" "IJS" "SPY"
weight  <- c(0.25, 0.25, 0.2, 0.2, 0.1)
weight
## [1] 0.25 0.25 0.20 0.20 0.10
w_tbl <- tibble(symbols, weight)
w_tbl
## # A tibble: 5 × 2
##   symbols weight
##   <chr>    <dbl>
## 1 AGG       0.25
## 2 EEM       0.25
## 3 EFA       0.2 
## 4 IJS       0.2 
## 5 SPY       0.1

4 Build a portfolio

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

portfolio_returns_tbl
## # A tibble: 20 × 2
##    date       portfolio.returns
##    <date>                 <dbl>
##  1 2013-03-28           0.0301 
##  2 2013-06-28          -0.0201 
##  3 2013-09-30           0.0608 
##  4 2013-12-31           0.0482 
##  5 2014-03-31           0.00617
##  6 2014-06-30           0.0375 
##  7 2014-09-30          -0.0348 
##  8 2014-12-31           0.0102 
##  9 2015-03-31           0.0231 
## 10 2015-06-30          -0.00588
## 11 2015-09-30          -0.0938 
## 12 2015-12-31           0.0186 
## 13 2016-03-31           0.0290 
## 14 2016-06-30           0.0167 
## 15 2016-09-30           0.0515 
## 16 2016-12-30           0.00295
## 17 2017-03-31           0.0514 
## 18 2017-06-30           0.0351 
## 19 2017-09-29           0.0482 
## 20 2017-12-29           0.0391

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 = "Monthly Returns",
         x = NULL,
         title = "Portfolio Returns Scatter")

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

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

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