# Load packages

# Core
library(tidyverse)
library(tidyquant)

Goal

Visualize and examine changes in the underlying trend in the performance of your portfolio in terms of Sharpe Ratio.

Choose your stocks.

from 2012-12-31 to present

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

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,
                 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") %>%
    
    # 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")

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

Scatterplot 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, size = 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 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()