# 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 ("AAPL", "NVDA", "CAT", "PANW", "MU")

prices <- tq_get(x = symbols,
                 get = "stockprices",
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns (monthly)

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 (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "CAT"  "MU"   "NVDA" "PANW"
# weights
weights <- c(0.2, 0.2, 0.2, 0.2, 0.2)
weights 
## [1] 0.2 0.2 0.2 0.2 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 5 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.2
## 2 CAT         0.2
## 3 MU          0.2
## 4 NVDA        0.2
## 5 PANW        0.2

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 = "quarters",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.0295  
##  2 2013-02-28  0.0332  
##  3 2013-03-28  0.0176  
##  4 2013-04-30 -0.0105  
##  5 2013-05-31  0.0389  
##  6 2013-06-28 -0.0170  
##  7 2013-07-31  0.0484  
##  8 2013-08-30  0.0218  
##  9 2013-09-30  0.0421  
## 10 2013-10-31  0.000630
## # ℹ 50 more rows

5 Compute 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
##   `SharpeRatio(Rf=0%,p=95%)`
##                        <dbl>
## 1                      0.473

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("2019-02-19"),
             color = "cornflowerblue", size = 1, alpha = 0.5) +
  
  theme(legend.position = "none") +
  
  annotate(geom = "text", 
           x = as.Date("2019-06-19"), y = -0.05, 
           label = "Covid", size = 5, angle = 90) +
             
  annotate(geom = "text", 
           x = as.Date("2015-09-01"), y = -0.13, 
           label = str_glue("More volatile after COVID, 
                            with more of negative returns."), 
           color = "green") +
             
  labs(y = "monthly returns", x = NULL)

# 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      0.750
##  2 2015-01-30      0.612
##  3 2015-02-27      0.639
##  4 2015-03-31      0.561
##  5 2015-04-30      0.614
##  6 2015-05-29      0.607
##  7 2015-06-30      0.475
##  8 2015-07-31      0.423
##  9 2015-08-31      0.359
## 10 2015-09-30      0.309
## # 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("2018-03-01"), y = 0.08,
           label = "Between 2015 to mid year 2016 this portfolio saw signifigant loss. However, since then the portfolio has made a signifigant bounce back into 2018", 
           color = "red", size = 2.75)

6 Plot: Rolling Sharpe Ratio

How has your portfolio performed over time? Provide dates of the structural breaks, if any. The Code Along Assignment 9 had one structural break in November 2016. What do you think the reason is?