# 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("NFLX", "AAPL", "VRTX")

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] "AAPL" "NFLX" "VRTX"
# weights
weights <- c(0.3, 0.3, 0.4)
weights
## [1] 0.3 0.3 0.4
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL        0.3
## 2 NFLX        0.3
## 3 VRTX        0.4

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  0.154 
##  2 2013-02-28  0.0490
##  3 2013-03-28  0.0670
##  4 2013-04-30  0.174 
##  5 2013-05-31  0.0383
##  6 2013-06-28 -0.0599
##  7 2013-07-31  0.0824
##  8 2013-08-30  0.0450
##  9 2013-09-30  0.0226
## 10 2013-10-31  0.0159
## # 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                       0.365

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 -0.0236       0.574
##  2 2015-01-30  0.0648       0.555
##  3 2015-02-27  0.0829       0.571
##  4 2015-03-31 -0.0537       0.475
##  5 2015-04-30  0.106        0.469
##  6 2015-05-29  0.0636       0.483
##  7 2015-06-30 -0.0113       0.537
##  8 2015-07-31  0.0848       0.537
##  9 2015-08-31 -0.0415       0.462
## 10 2015-09-30 -0.120        0.326
## # 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 FALLEN 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?

overtime my portfolio can be seen having a falling Sharpe ratio till the end of 2017 where it starts to follow an increasing value trend. Structural breaks can be seen at the start of 2017 with a up tick in Sharpe ratio value. I believe the structural break may have been due to a presidential election where risk is seen increasing due to the change of power dynamics between political powers.