# Load packages

# Core
library(tidyverse)
library(tidyquant)
library(PerformanceAnalytics)
library(ggrepel)

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("NKE", "TSLA", "MSFT", "JPM", "AAPL")

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" "JPM"  "MSFT" "NKE"  "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 JPM        0.25
## 3 MSFT       0.2 
## 4 NKE        0.2 
## 5 TSLA       0.1

4 Build a 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  0.00469
##  2 2013-02-28  0.00240
##  3 2013-03-28  0.0234 
##  4 2013-04-30  0.0892 
##  5 2013-05-31  0.0983 
##  6 2013-06-28 -0.0261 
##  7 2013-07-31  0.0521 
##  8 2013-08-30  0.0299 
##  9 2013-09-30  0.0420 
## 10 2013-10-31  0.0260 
## # 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                       0.497

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_data <- 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_data
## # A tibble: 37 × 2
##    date       rolling_sr
##    <date>          <dbl>
##  1 2014-12-31      0.790
##  2 2015-01-30      0.622
##  3 2015-02-27      0.682
##  4 2015-03-31      0.606
##  5 2015-04-30      0.602
##  6 2015-05-29      0.578
##  7 2015-06-30      0.634
##  8 2015-07-31      0.605
##  9 2015-08-31      0.452
## 10 2015-09-30      0.414
## # ℹ 27 more rows
rolling_sr_data %>%
    
    ggplot(aes(x = date, y = rolling_sr)) +
    geom_line(color = "cornflowerblue") +
    
    # Labeling
    labs(x = NULL, y = "Rolling Sharpe Ratio")

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 portfolio was doing well in 2015 with a high Sharpe ratio, but steadily declined through 2016, reaching its weakest point toward the end of 2016. After that, there was a structural break and the ratio improved, indicating the portfolio began earning better returns for the level of risk taken.