# 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("SBUX", "AAPL", "VZ", "T")

prices <- tq_get(x    = symbols,
                 get  = "stock.prices",
                 from = "2012-12-31",
                 to   = "2022-12-31")

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" "SBUX" "T"    "VZ"
# weights
weights <- c(0.25, 0.25, 0.25, 0.25)
weights
## [1] 0.25 0.25 0.25 0.25
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AAPL       0.25
## 2 SBUX       0.25
## 3 T          0.25
## 4 VZ         0.25

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: 120 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.0106 
##  2 2013-02-28  0.0129 
##  3 2013-03-28  0.0292 
##  4 2013-04-30  0.0514 
##  5 2013-05-31 -0.0279 
##  6 2013-06-28 -0.00994
##  7 2013-07-31  0.0557 
##  8 2013-08-30 -0.00316
##  9 2013-09-30  0.0126 
## 10 2013-10-31  0.0799 
## # … with 110 more rows

5 Compute Sharpe Ratio

# Define risk free rate 
rfr <- 0.003

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=0.3%,p=95%)`
##                           <dbl>
## 1                         0.145

6 Plot: Rolling Sharpe Ratio

# Create a custom function to calculate rolling SR
calculate_rolling_sharpeRatio <- function(df) {
    SharpeRatio(df,
                Rf = rfr,
                FUN = "StdDev")
}
# Define value for Window
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: 97 × 3
##    date        returns sharpeRatio
##    <date>        <dbl>       <dbl>
##  1 2014-12-31 -0.0486        0.277
##  2 2015-01-30  0.0279        0.328
##  3 2015-02-27  0.0732        0.379
##  4 2015-03-31 -0.0231        0.310
##  5 2015-04-30  0.0444        0.304
##  6 2015-05-29  0.0179        0.370
##  7 2015-06-30 -0.00936       0.370
##  8 2015-07-31  0.0137        0.330
##  9 2015-08-31 -0.0463        0.260
## 10 2015-09-30 -0.0146        0.225
## # … with 87 more rows
rolling_sharpe_tbl %>%
    ggplot(aes(date, sharpeRatio)) +
    geom_line(color = "cornflowerblue") +
    
    # Labeling 
   labs(x = NULL, y = "Rolling Sharpe Ratio") +
    
    annotate(geom = "text",
             x = as.Date("2018-06-01"), y = 0.4,
             label = "This portfolio has not done quite as well since 2016.",
             size = 4, color = "blue")

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?

Over time, my portfolio has performed not to well. It has declined from 2016 at almost 0.4 to now which is at almost -0.1. It had large structural break in 2020, and 2022. I think these could have been caused from the pandemic, and the current state of the economy with the harsh inflation we have been fighting. When we look back at code along 9 there was a structure break in 2016, this could have been caused because of the fast growing market during that time which created the possibility of greater returns.