# 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,
                 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()
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,
               rebalence_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 -0.00220
##  3 2013-03-28  0.0127 
##  4 2013-04-30  0.0173 
##  5 2013-05-31 -0.0113 
##  6 2013-06-28 -0.0233 
##  7 2013-07-31  0.0342 
##  8 2013-08-30 -0.0231 
##  9 2013-09-30  0.0513 
## 10 2013-10-31  0.0305 
## # 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") %>%
  
  # Convert prices to returns
  tq_transmute(select = adjusted,
               mutate_fun = periodReturn,
               period = "monthly",
               type = "log",
               col_rename = "returns") %>%
  
  slice(-1)

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")

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       0.761

6 Plot

Scatterplot of returns with regression line

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

Lineplot of fitted vs actual returns

actual_fitted_long_tbl <- portfolio_market_returns_tbl %>%
  
  # Linear regression Modle
  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()