# 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", "MSFT", "META", "TSLA", "NFLX")

prices  <- tq_get(x    = symbols, 
                  get  = "stock.prices", 
                  from = "2012-12-31",
                  to   = "2021-12-31")

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" "META" "MSFT" "NFLX" "TSLA"
#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 AAPL       0.25
## 2 META       0.25
## 3 MSFT       0.2 
## 4 NFLX       0.2 
## 5 TSLA       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: 108 × 2
##    date         returns
##    <date>         <dbl>
##  1 2013-01-31  0.131   
##  2 2013-02-28 -0.0158  
##  3 2013-03-28  0.000339
##  4 2013-04-30  0.112   
##  5 2013-05-31  0.0533  
##  6 2013-06-28 -0.0327  
##  7 2013-07-31  0.166   
##  8 2013-08-30  0.113   
##  9 2013-09-30  0.0734  
## 10 2013-10-31  0.0247  
## # … with 98 more rows

5 Compute Sharpe Ratio

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                       0.455

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: 85 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.657
##  2 2015-01-30      0.614
##  3 2015-02-27      0.684
##  4 2015-03-31      0.624
##  5 2015-04-30      0.623
##  6 2015-05-29      0.614
##  7 2015-06-30      0.669
##  8 2015-07-31      0.675
##  9 2015-08-31      0.542
## 10 2015-09-30      0.460
## # … with 75 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 has a lot of structual 
             swings but is in a straight line upwards since 2020.", 
             color = "red", size = 4)

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?

From 2012-12-31 until 2020-12-31 I can identify four pretty significant structural breaks in my portfolio with regards to the rolling Sharpe ratio, that I have listed down below. It indicates a lot of volatility with risk and reward when looking at this metric. Over time I can see that it has had a lot of swings and the Sharpe ratio has gone down from 2014 overall. The reason for this volatility can have many factors. It could have to do with macroeconomic factors or internal within the company that creates these kind of swings. The risk of the assets within this portfolio are high but produce massive reruns as well so for a long term hold, it is a good bet if you have the nerves for it.

Average Sharpe Ratio for portfolio = 0.455. With an average Sharpe ratio of 0.455, Generally, you’re increasing your risk while reducing your possible reward. A Sharpe ratio of 0.5, or 50%, denotes that there is a significant amount of risk associated with the investment relative to its potential return.

Structural breaks:

  1. September 2015 to the downside. Sharpe Ratio = about 0.67

  2. October 2016 to the upside. Sharpe Ratio = about 0.2

  3. June 2018 towards the downside. Sharpe Ratio = 0.72

  4. October 2019 towards the upside. Sharpe Ratio = 0.17