# 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("FDX", "MSFT", "UPS")
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      date   returns 
##   "asset"    "date" "returns"

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

# symbols
symbols <- asset_returns_tbl %>% distinct(symbol) %>% pull()
    

# weights
weights <- c(0.5, 0.3, 0.2)
weights
## [1] 0.5 0.3 0.2
w_tbl <- tibble(symbols, weights)
w_tbl
## # A tibble: 3 × 2
##   symbols weights
##   <chr>     <dbl>
## 1 FDX         0.5
## 2 MSFT        0.3
## 3 UPS         0.2

4 Build a portfolio

portfolio_returns_tbl <- asset_returns_tbl %>%
    tq_portfolio(assets_col = symbol, 
                 returns_col = monthly.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  0.0732
##  2 2013-02-28  0.0353
##  3 2013-03-28 -0.0185
##  4 2013-04-30  0.0218
##  5 2013-05-31  0.0318
##  6 2013-06-28  0.0105
##  7 2013-07-31  0.0126
##  8 2013-08-30  0.0214
##  9 2013-09-30  0.0432
## 10 2013-10-31  0.102 
## # … with 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                       0.402

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      0.754
##  2 2015-01-30      0.492
##  3 2015-02-27      0.507
##  4 2015-03-31      0.421
##  5 2015-04-30      0.456
##  6 2015-05-29      0.424
##  7 2015-06-30      0.375
##  8 2015-07-31      0.391
##  9 2015-08-31      0.254
## 10 2015-09-30      0.205
## # … with 27 more rows
rolling_sr_tbl %>%
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    # Labelling
    labs(x = NULL, y = "Rolling Sharpe Ratio") +
    
     annotate(geom = "text",
         x = as.Date("2016-06-01"),
         y = 0.5,
         label = "This portfolio started and ended well,
                    but had a sizeable dip in the middle.",
         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?

My portfolio started out doing very well. Then it dropped pretty quickly with its structural break happening right around July 2016. Then it rose back up but never to the point at which it started. After researching the stock market in 2016, I found that it was a very volitile year, which could explain when the break was.