# 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 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                       0.250

6 Plot

Histogram of Returns with risk free rate

portfolio_returns_tbl %>%
  
  ggplot(aes(x = returns)) +
  geom_histogram(binwidth = 0.015, fill = "cornflowerblue", alpha = 0.5) +
  
  geom_vline(xintercept = rfr, color = "green", size = 1) +
  
  annotate(geom = "text",
           x = rfr + 0.002, y = 11,
           label = "risk free rate",
           angle = 90) +
  
  labs(y = "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     0.250 
##  2 2015-01-30     0.190 
##  3 2015-02-27     0.252 
##  4 2015-03-31     0.223 
##  5 2015-04-30     0.221 
##  6 2015-05-29     0.231 
##  7 2015-06-30     0.246 
##  8 2015-07-31     0.173 
##  9 2015-08-31     0.109 
## 10 2015-09-30    -0.0134
## # 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 well since 2016.", color = "red", size = 4)