# 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("MCD", "ISRG", "KHC", "FIS", "GOOG")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",    
                 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] "FIS"  "GOOG" "ISRG" "KHC"  "MCD"
# 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 FIS        0.25
## 2 GOOG       0.25
## 3 ISRG       0.2 
## 4 KHC        0.2 
## 5 MCD        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  0.0719 
##  2 2013-02-28 -0.00415
##  3 2013-03-28  0.00839
##  4 2013-04-30  0.0271 
##  5 2013-05-31  0.0274 
##  6 2013-06-28 -0.00198
##  7 2013-07-31 -0.0501 
##  8 2013-08-30 -0.00818
##  9 2013-09-30  0.0171 
## 10 2013-10-31  0.0506 
## # ℹ 50 more rows

5 Compute Sharpe Ratio

# Risk free rate
rfr <- 0.0003

portfolio_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_performance(Ra = returns,
                   Rf = rfr,
                   performance_fun = SharpeRatio,
                   FUN = "StdDev") 

portfolio_sharpe_tbl
## # A tibble: 1 × 1
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.404

6 Plot: Rolling Sharpe Ratio

# Custom function
# necessary because we would not be able to specify FUN = "StdDev" otherwise

calculate_rolling_sharpeRatio <- function(df) {

    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")

}

# dump(list = "calculate_rolling_sharpeRatio",
#      file = "00_scripts/calculate_rolling_sharpeRatio.R")

# Set the length of periods for rolling calculation
window <- 24

# Calculate rolling sharpe ratios
rolling_sharpe_tbl <- portfolio_returns_tbl %>%

    tq_mutate(select = returns,
              mutate_fun = rollapply,
              width = window,
              align = "right",
              FUN = calculate_rolling_sharpeRatio,
              col_rename = "sharpeRatio") %>%
    na.omit()

rolling_sharpe_tbl
## # A tibble: 37 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.00114       0.400
##  2 2015-01-30 -0.0101        0.311
##  3 2015-02-27  0.0406        0.373
##  4 2015-03-31 -0.00147       0.356
##  5 2015-04-30 -0.0301        0.258
##  6 2015-05-29 -0.00473       0.211
##  7 2015-06-30 -0.0103        0.197
##  8 2015-07-31  0.0843        0.377
##  9 2015-08-31 -0.0200        0.354
## 10 2015-09-30 -0.0337        0.271
## # ℹ 27 more rows
# Figure 7.5 Rolling Sharpe ggplot ----

rolling_sharpe_tbl %>%

    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +

    labs(title = paste0("Rolling ", window, "-Month Sharpe Ratio"),
         y = "rolling Sharpe Ratio",
         x = NULL) +
    theme(plot.title = element_text(hjust = 0.5)) +

    annotate(geom = "text",
             x = as.Date("2016-06-01"), y = 0.5,
             label = "This portfolio has done good since 2016.",
             size = 5, color = "red")

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?

If there is a structural break in my portfolio within the observable time period, it would be in the middle of 2016, as there is a 6-month period of low movement/variation which differs from the rest of the data. As for the Code Along Assignment 9 having one structural break, this could be for countless reasons and it would be very hard to narrow it down without extensive research. However, the reason most likely falls under one of the following categories, economic events and policy changes, Technological advancement, geopolitical events, or possibly changes in market structure or changes to whats in the code along portfolio.