# 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("NVDA", "MSFT", "AMD", "TSLA")
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"))

asset_returns_tbl
## # A tibble: 240 × 3
##    asset date       returns
##    <chr> <date>       <dbl>
##  1 AMD   2013-01-31  0.0800
##  2 AMD   2013-02-28 -0.0432
##  3 AMD   2013-03-28  0.0238
##  4 AMD   2013-04-30  0.101 
##  5 AMD   2013-05-31  0.350 
##  6 AMD   2013-06-28  0.0198
##  7 AMD   2013-07-31 -0.0790
##  8 AMD   2013-08-30 -0.142 
##  9 AMD   2013-09-30  0.153 
## 10 AMD   2013-10-31 -0.132 
## # ℹ 230 more rows

3 Assign a weight to each asset (change the weigting scheme)

# symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AMD"  "MSFT" "NVDA" "TSLA"
# weights
weights <- c(0.25, 0.20, 0.20, 0.30)
weights
## [1] 0.25 0.20 0.20 0.30
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 4 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 AMD        0.25
## 2 MSFT       0.2 
## 3 NVDA       0.2 
## 4 TSLA       0.3

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  0.0561
##  2 2013-02-28 -0.0212
##  3 2013-03-28  0.0396
##  4 2013-04-30  0.175 
##  5 2013-05-31  0.288 
##  6 2013-06-28  0.0249
##  7 2013-07-31  0.0367
##  8 2013-08-30  0.0496
##  9 2013-09-30  0.0886
## 10 2013-10-31 -0.0823
## # 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
##   `StdDevSharpe(Rf=0%,p=95%)`
##                         <dbl>
## 1                       0.416

6 Plot: 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     0.398 
##  2 2015-01-30     0.325 
##  3 2015-02-27     0.379 
##  4 2015-03-31     0.308 
##  5 2015-04-30     0.271 
##  6 2015-05-29     0.191 
##  7 2015-06-30     0.176 
##  8 2015-07-31     0.121 
##  9 2015-08-31     0.0742
## 10 2015-09-30     0.0240
## # 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 = 5)

Answer:

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?

The rolling Sharpe Ratio graph of my portfolio shows a clear performance trend. Initially, from 2015 through mid-2016, the Sharpe Ratio declines, indicating decreasing performance due to mabye lower returns or higher volatility. A turnaround occurs around mid-2016 marked by a structural break, and it looks like its leading to a significant improvement in performance, with the Sharpe Ratio climbing until it peaks in early 2018. However, a sharp decline follows this peak, suggesting a sudden drop in risk-adjusted returns. Overall, I think my portfolio recovered well after a rough start, performing strongly until the early part of 2018, when it faced new challenges.

The events and strategies around my companies in my portfolio around early 2016 like new product launches, strategic shifts towards growth sectors, and pivotal new model announcements could very well explain the observed structural break in my portfolio’s performance. I looked into some of the events after the structural break in 2016 on my graph, in 2016 AMD rose with 70% on the stock exchange and Nvidia with 15%, the reason for this was they both released two super popular GPU`s and the leading to them both having incredible revenue.