# 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", "AMD", "AMZN", "INTC", "AAPL")

prices <- tq_get(x = symbols,
                 from = "2012-12-31",
                 to = "2017-12-31")

2 Convert prices to returns

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

#symbols
symbols <- asset_returns_tbl %>% distinct(asset) %>% pull()
symbols
## [1] "AAPL" "AMD"  "AMZN" "INTC" "NVDA"
#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 AMD        0.25
## 3 AMZN       0.2 
## 4 INTC       0.2 
## 5 NVDA       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,
               rebalence_on = "months",
               col_rename = "returns")

portfolio_returns_tbl
## # A tibble: 60 × 2
##    date        returns
##    <date>        <dbl>
##  1 2013-01-31 -0.00352
##  2 2013-02-28 -0.0137 
##  3 2013-03-28  0.0194 
##  4 2013-04-30  0.0434 
##  5 2013-05-31  0.124  
##  6 2013-06-28 -0.0138 
##  7 2013-07-31  0.00425
##  8 2013-08-30 -0.0522 
##  9 2013-09-30  0.0750 
## 10 2013-10-31  0.0180 
## # ℹ 50 more rows

5 Calculate 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                       0.407

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      0.263
##  2 2015-01-30      0.267
##  3 2015-02-27      0.339
##  4 2015-03-31      0.265
##  5 2015-04-30      0.245
##  6 2015-05-29      0.195
##  7 2015-06-30      0.167
##  8 2015-07-31      0.160
##  9 2015-08-31      0.185
## 10 2015-09-30      0.138
## # 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 = 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?