# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Collect individual returns into a portfolio by assigning a weight to each stock

five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG”

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")
symbols
## [1] "SPY" "EFA" "IJS" "EEM" "AGG"
prices
## # A tibble: 6,300 × 8
##    symbol date        open  high   low close    volume adjusted
##    <chr>  <date>     <dbl> <dbl> <dbl> <dbl>     <dbl>    <dbl>
##  1 SPY    2012-12-31  140.  143.  140.  142. 243935200     114.
##  2 SPY    2013-01-02  145.  146.  145.  146. 192059000     117.
##  3 SPY    2013-01-03  146.  146.  145.  146. 144761800     117.
##  4 SPY    2013-01-04  146.  147.  146.  146. 116817700     117.
##  5 SPY    2013-01-07  146.  146.  145.  146. 110002500     117.
##  6 SPY    2013-01-08  146.  146.  145.  146. 121265100     117.
##  7 SPY    2013-01-09  146.  146.  146.  146.  90745600     117.
##  8 SPY    2013-01-10  147.  147.  146.  147. 130735400     118.
##  9 SPY    2013-01-11  147.  147.  147.  147. 113917300     118.
## 10 SPY    2013-01-14  147.  147.  146.  147.  89567200     118.
## # ℹ 6,290 more rows

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"))
asset_returns_tbl
## # A tibble: 300 × 3
##    asset date         returns
##    <chr> <date>         <dbl>
##  1 AGG   2013-01-31 -0.00623 
##  2 AGG   2013-02-28  0.00589 
##  3 AGG   2013-03-28  0.000985
##  4 AGG   2013-04-30  0.00964 
##  5 AGG   2013-05-31 -0.0202  
##  6 AGG   2013-06-28 -0.0158  
##  7 AGG   2013-07-31  0.00269 
##  8 AGG   2013-08-30 -0.00830 
##  9 AGG   2013-09-30  0.0111  
## 10 AGG   2013-10-31  0.00829 
## # ℹ 290 more rows

3 Assign a weight to each asset

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AGG" "EEM" "EFA" "IJS" "SPY"
# 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 AGG        0.25
## 2 EEM        0.25
## 3 EFA        0.2 
## 4 IJS        0.2 
## 5 SPY        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 = "months",
                 col_rename = "returns")
portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31  0.0204 
##  2 2013-02-28 -0.00239
##  3 2013-03-28  0.0121 
##  4 2013-04-30  0.0174 
##  5 2013-05-31 -0.0128 
##  6 2013-06-28 -0.0247 
##  7 2013-07-31  0.0321 
##  8 2013-08-30 -0.0224 
##  9 2013-09-30  0.0511 
## 10 2013-10-31  0.0301 
## # ℹ 50 more rows

5 Calculate CAPM Beta

5.1 Get market returns

market_returns_tbl <- tq_get(x = "SPY",
                 get = "stock.prices",
                 from = "2012-12-31",
                 to = "2017-12-31") %>%
    tq_transmute(select = adjusted,
                 mutate_fun = periodReturn,
                 period = "monthly",
                 type = "log", 
                 col_rename = "returns") %>%
    slice(-1)
market_returns_tbl
## # A tibble: 60 × 2
##    date       returns
##    <date>       <dbl>
##  1 2013-01-31  0.0499
##  2 2013-02-28  0.0127
##  3 2013-03-28  0.0373
##  4 2013-04-30  0.0190
##  5 2013-05-31  0.0233
##  6 2013-06-28 -0.0134
##  7 2013-07-31  0.0504
##  8 2013-08-30 -0.0305
##  9 2013-09-30  0.0312
## 10 2013-10-31  0.0453
## # ℹ 50 more rows

5.2 Join Returns

portfolio_market_returns_tbl <- left_join(market_returns_tbl, 
          portfolio_returns_tbl, 
          by = "date") %>%
    set_names("date",
              "market_returns",
              "portfolio_returns")
portfolio_market_returns_tbl
## # A tibble: 60 × 3
##    date       market_returns portfolio_returns
##    <date>              <dbl>             <dbl>
##  1 2013-01-31         0.0499           0.0204 
##  2 2013-02-28         0.0127          -0.00239
##  3 2013-03-28         0.0373           0.0121 
##  4 2013-04-30         0.0190           0.0174 
##  5 2013-05-31         0.0233          -0.0128 
##  6 2013-06-28        -0.0134          -0.0247 
##  7 2013-07-31         0.0504           0.0321 
##  8 2013-08-30        -0.0305          -0.0224 
##  9 2013-09-30         0.0312           0.0511 
## 10 2013-10-31         0.0453           0.0301 
## # ℹ 50 more rows

5.3 CAPM Beta

portfolio_market_returns_tbl %>%
    tq_performance(Ra = portfolio_returns,
                   Rb = market_returns, 
                   performance_fun = CAPM.beta)
## # A tibble: 1 × 1
##   CAPM.beta.1
##         <dbl>
## 1       0.738

6 Plot

Scaterplot of returns with regression line

portfolio_market_returns_tbl %>%
    ggplot(aes(x = market_returns, 
               y = portfolio_returns)) +
    geom_point(color = "cornflowerblue") + 
    geom_smooth(method = "lm", 
                se = FALSE, 
                linewidth = 1.5, 
                color = tidyquant::palette_light()[3]) +
labs(y = "Portfolio Returns", 
     x = "Market Returns")

Line plot of fitted vs actual returns

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
    
    #linear regression model
    lm(portfolio_returns ~ market_returns, data = .) %>%
    
    # get fitted and actual returns
    broom::augment() %>%
   
     # add date 
    mutate(date = portfolio_market_returns_tbl$date) %>%
    select(date, 
           portfolio_returns,
           .fitted) %>%
    
    # Transform data to long
    pivot_longer(cols = c(portfolio_returns, .fitted),
                 names_to = "type", 
                 values_to = "returns")

actual_fitted_long_tbl %>%
    ggplot(aes(x = date, y = returns, color = type)) +
    geom_line()