# 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")

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, 0.20, 0.20, 0.10)
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 Sharpe Ratio

# Define Risk Free Rate
rfr <- 0.0003

portfolio_SharpeRatio_tbl <- portfolio_returns_tbl %>% 
  
  tq_performance(Ra     = returns, 
                 performance_fun = SharpeRatio, 
                 Rf              = rfr,
                FUN              = "StdDev")

portfolio_SharpeRatio_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.239

6 Plot

Histogram of Returns with Risk Free Rate

portfolio_returns_tbl %>% 
  
  ggplot(aes(x = returns)) +
  geom_histogram(binwidth = 0.002, fill = "cornflowerblue", alpha= 0.5) +
  
  geom_vline(xintercept = rfr, color = "green", size = 1) +
  
  annotate(geom  = "text", x = rfr + 0.001, y = 13,
           label = "Risk Free Rate",
           angle = 90)

labs(y = "count")
## <ggplot2::labels> List of 1
##  $ y: chr "count"

Scatterplot of Returns around Risk Free Rate

portfolio_returns_tbl %>% 
  
  # Add a New Variable
  mutate(excess_returns = if_else(returns > rfr, 
                                  "rfr_above",
                                  "rfr_below")) %>% 
  
  # Plot
  ggplot(aes(x = date, y = returns)) +
  geom_point(aes(color = excess_returns)) +
  geom_hline(yintercept = rfr, color = "cornflowerblue", 
             linetype = 3, size = 1) +
  geom_vline(xintercept = as.Date("2016-11-01"),
             color = "cornflowerblue", size = 1, alpha = 0.5) +
  
  theme(legend.position = "none") +

  annotate(geom = "text",
           x    = as.Date("2016-12-01"), y = -0.04,
           label = "Election", size = 5, angle = 90) +
  
  annotate(geom = "text",
           x = as.Date("2017-05-01"), y = -0.01,
           label = str_glue("No Returns Below RFR 
                            after the 2016 Election."),
           color = "green") +
 
  labs(y = "monthly returns", x = NULL)

Rolling Sharpe Ratio

# Create a custom function to Calculate Rolling SR
Calculate_rolling_SharpeRatio <- function(data) {
  
  
  rolling_SR <- SharpeRatio(R   = data, 
              Rf  = rfr,
              FUN = "StdDev")
  
  return(rolling_SR)
}

# Define Window
window <- 24

# Transform Data : Calculate Rolling Sharpe Ratio
rolling_sr_tbl <- portfolio_returns_tbl %>% 
  
  tq_mutate(select     = returns, 
            mutate_fun = rollapply, 
            width      = window,
            FUN        = Calculate_rolling_SharpeRatio, 
            col_rename = "rolling_sr") %>% 
  
  select(-returns) %>% 
  na.omit()

rolling_sr_tbl
## # A tibble: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31     0.230 
##  2 2015-01-30     0.178 
##  3 2015-02-27     0.240 
##  4 2015-03-31     0.210 
##  5 2015-04-30     0.214 
##  6 2015-05-29     0.222 
##  7 2015-06-30     0.238 
##  8 2015-07-31     0.162 
##  9 2015-08-31     0.0950
## 10 2015-09-30    -0.0279
## # ℹ 27 more rows
rolling_sr_tbl %>% 

ggplot(aes(x = date, y = rolling_sr)) +
  geom_line(color = "cornflowerblue") +

# Labeling
labs(x = NULL, y = "Rolling Sharpe Ratio") +
  
  annotate(geom = "text",
           x    = as.Date("2016-06-01"), y = 0.5,
           label = "This portfolio has done quite qell since 2016", 
           color = "red", size = 5)